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INTERNATIONAL JOURNAL OF COMPUTER(IJCET), ISSN 0976 –
  International Journal of Computer Engineering and Technology ENGINEERING
  6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1,(IJCET)
                             & TECHNOLOGY January- February (2013), © IAEME
ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 4, Issue 1, January- February (2013), pp. 16-33
                                                                           IJCET
© IAEME: www.iaeme.com/ijcet.asp
Journal Impact Factor (2012): 3.9580 (Calculated by GISI)               ©IAEME
www.jifactor.com




     DETECTING IN-SITU MELANOMA USING MULTI PARAMETER
     EXTRACTION AND NEURAL CLASSIFICATION MECHANISMS

          Ruksar Fatima1,Dr.Mohammed Zafar Ali Khan2,Dr. A. Govardhan3 and
                                         Kashyap Dhruve4
           1
             (Head Dept. of Bio-Medical Engg, KBN College of Engg, Gulbarga, India,
                                     Ruksar_blue@yahoo.co.in)
            2
              (Associate Professor,IIIT Hyderabad, Hyderabad, India,zafar @iith.ac.in)
            3
              (Director of Evaluation of JNTU, Hyderabad, India, dejntuh@jntuh.ac.in)
  4
    (Technical Director, Planet-i Technologies, Bangalore, India,kashyapdhruve@hotmail.com,)


  ABSTRACT

          Computer aided diagnostics systems are widely used for diagnosis of skin lesions.

  aimed to enable early detection of skin cancer melanoma. The ‫ ܵܥܧܲܯ‬adopts a supervised
  This paper discusses a Multi Parameter Extraction and Classification System (‫ )ܵܥܧܲܯ‬to

  machine learning algorithm for classification. The dermoscopic images are represented by

  operation of the ‫ ܵܥܧܲܯ‬in the training and testing phase. The adoption of the Multilayer
  extensive parameter sets extracted using a six phase approach. The paper discusses the

  Feed Forward Neural Network (‫ )ܰܰܨܯ‬classifier is justified, its proficiency in accurately
  diagnosing and classifying early signs of skin cancer melanoma in dermoscopic images of
  skin lesions is proved through the experimental results discussed in the manuscript.

  Keywords: Skin Cancer, Melanoma, Early Detection, In Situ Melanoma, MPECS , Multi
  Parameter Extraction, Classification, Computer Aided Diagnostics, Image Processing, Skin
  Lesions. Feature Extraction, Multilayer Feed Forward, Neural Network, Back Propagation,
  SVM Classifier, ROC, Receiver Operating Characteristics

  1. INTRODUCTION

         Computer vision based diagnosis systems which are non-invasive have experienced a
  wide acceptability ratio in the recent years. Deaths arising from skin cancers have increased
  tremendously in the past decade [1] [2][3]. Skin Cancer Melanoma is dangerous and accounts
  for maximum number of deaths arising due to skin cancers [3][4]. A recent survey [5]
  conducted in 2012 for the United States of America alone, states that more than 75% of the
  deaths arising from skin cancers are due to melanotic skin lesions. Skin cancer melanomas

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International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME

are generally diagnosed by adopting biopsy procedures recommended by dermatologists.
Studies have proved that the mortality rate could be reduced if melanotic lesions are detected

        The research work presented here puts forth the ‫ ܵܥܧܲܯ‬to aid early detection of in
at a very early stage [6].

situ melanoma and classification of skin lesions. The dermoscopic images of skin lesions are
represented as a set of 21 features or parameter extracted phase wise. The skin lesions
represented as a set of features need to be classified. Statistical analysis carried out for on the
parameter set obtained post extraction were inconclusive in classifying the skin lesions [7]

into the ‫ .ܵܥܧܲܯ‬Machine learning algorithms are adopted for accurate classification. A
hence there was an requirement for advanced classification mechanisms to be incorporated

‫ ܰܰܨܯ‬classifier trained using the back propagation algorithm is adopted. The training phase
of the ‫ ܵܥܧܲܯ‬enables the system to understand the features that exhibit properties of specific
classes defined during training. Subsequently the ‫ ܵܥܧܲܯ‬can be utilized for classification of
the testing data set. The proposed methodology is compared with the popular ܸܵ‫ ܯ‬classifier.
        This research manuscript is organized as follows. Section 2 discusses the

methodologies adopted by researchers. The next section discusses the proposed ‫ ܵܥܧܲܯ‬at
literaturesurvey about skin cancer melanoma diagnosis systems and classification

length along with the ‫ ܰܰܨܯ‬classifier. The experimental evaluation and comparisons are
discussed in the penultimate section of this paper. The conclusion of the research work
presented in this paper is discussed in the fifth section.

2. LITERATURE SURVEY

        The Asymmetry Border Color and Differential Structure (ABCD) methodology is the
most commonly used in the diagnosis of skin cancer melanoma [8]. To enhance the
efficiency additional optimizations such as fuzzy border extraction [9], “JSEG” based
optimization [10]; hybrid techniques [11] have been incorporated. A classification accuracy
of about 60% was achieved by incorporating wavelet decomposition techniques along with
the ABCD principle. Skin lesion classification can also be achieved using the Menzies
Method [12]. The ABCD based methods and the Menzies Method exhibit lesser accuracy in
detection of early stages of skin cancer melanoma. G. Betta at el [13] introduced a seven
point checklist method to diagnose skin lesions which proved to exhibit higher classification
accuracy than the other methodologies described. The computational complexity of the seven
point checklist method is considered as a major drawback. The, methods discussed above
exhibited less of low classification accuracy. The need of machine learning algorithms to
accurately classify and diagnose early symptoms of skin cancer melanoma arose. The use of
advance clustering techniques [14], k Nearest Neighborhood [6], classification/regression
trees [15] has been closely studied. The use of the support vector machines (ܸܵ‫ )ܯ‬for

of skin cancer melanoma [18]. The ‫ ܵܥܧܲܯ‬adopts the ‫ ܰܰܨܯ‬based classifier [23]. The
classification [16] [17] has been proposed by most of the researchers even for early detection

above mentioned methodologies are inaccurate to detect and classify early stages of skin
cancer melanoma. A few systems proposed to detect early symptoms exhibit low
classification accuracy as the early stages of skin cancer melanoma described through
features belong to the non-melanoma and melanoma classes cumulatively making
classification a very difficult or cumbersome task.




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International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME

3. MULTI PARAMETER EXTRACTION AND CLASSIFICATION SYSTEM
(ࡹࡼࡱ࡯ࡿ) MODELING

        Appropriate skin lesions diagnosis at early stages enable possible cure to melanoma
skin cancers. Skin lesions of the melanotic type in the in situ stages are easily mistaken with
other skin related ailments by even experienced physicians. This paper introduces a computer
aided skin lesion diagnosis system named Multi Parameter Extraction and Classification

accounts for the highest number of deaths amongst skin cancers. The ‫ ܵܥܧܲܯ‬relies on the
System (‫ )ܵܥܧܲܯ‬aiding early detection of skin cancer melanoma. Skin cancer melanoma

machine learning techniques incorporated to accurately classify the skin lesions defined by a
set of parameters extracted.

    3.1. ࡹࡼࡱ࡯ࡿ – PRELIMINARY NOTATION

         Let‫ܫ‬ௌ௄ represent a set of dermoscopic skin lesion images for analysis defined as

‫ܫ‬ௌ௄ ൌ ሼ݅ଵ , ݅ଶ , ݅ଷ , ݅ସ , … … ݅௡ ሽ


݅ represents an image
Where

݊represents the total number of images to be analyzed.

      The MPECS system proposed in this paper is aids the early diagnosis of skin cancer
melanoma skin lesions. Let the classification set of the skin lesions be defined as

C ൌ ሼcଵ , cଶ , cଷ … cୡ୪ ሽ

Where c represents the class and cl represents the total number of classes considered.

Based on the above mentioned definitions it could be stated that ‫ ׊‬i ‫ א‬I ‫ !׌ ׷‬c ‫ א‬C | i ‫ א‬c.

        The ‫ ܵܥܧܲܯ‬aids the classification of skin lesions from dermoscopic images. The skin
lesions are defined as a set of parameter P that it exhibits. Let the Parameter set be defined by

P ൌ ሼpଵ , pଶ , pଷ , … pୟ ሽ

Where a represents the total number of p parameters

        In the MPECS an image i ‫ א‬I is represented by a set of parameters P is used for
classification. The image I୬ could now defined as

I୬ ൌ ൛p୬ଵ , p୬ଶ , p୬ଷ , … p୬୮ ൟ

An image I୬ could be represented as a matrix and is defined as




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International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME


                                          x଴,଴           ‫ ڮ‬x଴,୛ୢ
                                    I୬ ൌ ൥ ‫ڭ‬             ‫ڰ‬    ‫ ڭ‬൩
                                          xୌ୲,଴          ‫ ڮ‬xୌ୲,୛ୢ

Where Ht represents the height and Wd represents the width of the image I୬ and x represents
the pixel value at the given location.

        The MPECS adopts a 6 phase approach to extract the parameters set P ‫ ׊‬i ‫ א‬Iୗ୏ . The
system considers 21 parameters for classification i.e. a ൌ 21. Each phase provides towards
the construction of the parameter set P.The ‫ ܵܥܧܲܯ‬relies on the supervised machine learning

with back propagation [24] training is adopted for classification. The trainings set ܶோ is
algorithm for classification. The use of multi-layer feed forward neural networks (‫)ܰܰܨܯ‬

defined as

ܶோ ൌ ሼ்ܴோଵ , ்ܴோଶ , ்ܴோଷ , … … . ்ܴோ௥ ሽ

Where ்ܴோ௥ represents the ‫ ݎ‬௧௛ training record.


c from the training image. I.e. ்ܴோ௥ ൌ ሼI୰ ‫ ׫‬c௥ ሽwhere I୰ represents the parameter set that
       The training record is constructed from the parameter set and the corresponding class

defines the ‫ ݎ‬௧௛ training image and c௥ represents the corresponding classification class.
Similarly the test set ்ܶ is defined as

்ܶ ൌ ሼ்்ܴଵ , ்்ܴଶ , ்ܴோଷ , … … . ்்ܴ௧ ሽ

The objective of the ‫ ܵܥܧܲܯ‬is to identify c௧ | ்்ܴ௧ ‫ א‬c௧ ܽ݊݀ c௧ ‫ܥ א‬

    3.2 OPERATION OF THE ࡹࡼࡱ࡯ࡿ

        The ‫ ܵܥܧܲܯ‬relies on supervised learning techniques for classification. To impart
intelligence the ‫ ܵܥܧܲܯ‬is trained using the pre-classified dermoscopic images. The training
operation of the ‫ ܵܥܧܲܯ‬is described by the algorithm given below where
‫்ܰܰܨܯ‬ோ ሺܺሻrepresents the back propagation training function based on the inputs set ܺ. The
parameters extracted from the phase ‫ ݌‬is represented by ‫்݌‬ோ௜ .

Algorithm Name:Training Phase of the ‫ܵܥܧܲܯ‬


   1. ‫ݎ‬training dermoscopic images
Input:

   2. classification set C ൌ ሼcଵ , cଶ , cଷ … c୰ ሽ


   1. Trained ‫ ܰܰܨܯ‬classifier.
Output:




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International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME

Algorithm

Step 2: ܶோ ൌ ‫׎‬
Step 1: Training Phase Begin

Step 3: For all training images ݅ ‫ݎ ݋ݐ 1 ׷‬
                 ்ܴோ௜ ൌ ‫׎‬
Step 5: ࡹࡼࡱ࡯ࡿ Parameter Extraction Begin
Step 4:

                         Parameter Set P்ோ௜ ൌ ‫׎‬
                         For phases ࢖ ‫ ׷‬૚: ૟
Step 6:

                         P்ோ௜ ൌ P்ோ௜ ‫்݌ ׫‬ோ௜
Step 7:
Step 8:
Step 9:                  End phase

Step 11:்ܴோ௜ ൌ P்ோ௜ ‫ ׫‬c௜
Step 10:                 Parameter Extraction End

Step 12:         ܶோ ൌ ܶோ ‫்ܴ ׫‬ோ௜

Step 14: Perform Training ‫்ܰܰܨܯ‬ோ ሺܶோ ሻ
Step 13: End For

Step 15: End Training Phase

        The ‫ ܰܰܨܯ‬post training could be used for classification of the skin lesions represented in the
testing dermoscopic image dataset. The testing phase of the ‫ ܵܥܧܲܯ‬is described in the algorithm
given below where‫்்݌‬௜ represents the parameters extracted in the ‫݌‬௧௛ extraction phase
and‫ ்்ܰܰܨܯ‬ሺܻሻis the classification function of ܻ defined as

‫ ்்ܰܰܨܯ‬ሺܻሻ ൌ ܿ௧

Where ܿ௧ ‫ܥ א‬

Algorithm Name: Testing Phase of the ‫ܵܥܧܲܯ‬


   1. ‫ݐ‬testing dermoscopic images
Input:

   2. Trained ‫்ܰܰܨܯ‬ோ ሺܶோ ሻ


   1. Classification Output ܿ௧ ‫ܥ א‬
Output:


Algorithm

Step 2:்ܶ ൌ ‫׎‬
Step 1: Testing Phase Begin

Step 3: For all testing images ݅ ‫ݐ ݋ݐ 1 ׷‬
                 ்்ܴ௜ ൌ ‫׎‬
Step 5: ࡹࡼࡱ࡯ࡿ Parameter Extraction Begin
Step 4:

                         Parameter Set P்்௜ ൌ ‫׎‬
                         For phases ࢖ ‫ ׷‬૚: ૟
Step 6:

                         P்்௜ ൌ P்்௜ ‫்்݌ ׫‬௜
Step 7:
Step 8:
Step 9:                  End phase

Step 11:்்ܴ௜ ൌ P்்௜
Step 10:                 Parameter Extraction End

Step 12:         ்ܶ ൌ ்ܶ ‫்்ܴ ׫‬௜

Step 14: Perform Classification Query‫ ்்ܰܰܨܯ‬ሺ்ܶ ሻ ൌ ܿ௧
Step 13: End For

Step 15: End Testing Phase



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International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME

       The training and testing phases of the ‫ ܵܥܧܲܯ‬have been discussed above which
explains the operation. Parameter extraction is an integral part of the ‫ ܵܥܧܲܯ‬which defines

‫ ܵܥܧܲܯ‬is acheied using a 6 phase approach described in the subsequent section of this paper.
the skin lesions and aids in training and classification. The 21 parameter extraction of the


    3.3. PARAMETER EXTRACTION IN THE ࡹࡼࡱ࡯ࡿ

    The dermoscopic images of skin lesions is represented as a set of 21 parameters extracted
in 6 phases described below [7]

          3.3.1. ‫܁۱۳۾ۻ‬PARAMETER EXTRACTION – PHASE 1


present in an image ݅ ‫ܫ א‬and extracting the Lesion Borders, the symmetry of the lesions and
        This preliminary phase is primarily designed towards identifying the skin lesions

the color spreading factor of the skin lesions.
        To extract the parameters discussed the images are resized using a bicubic

pixels in the nearest ሾ 4 ൈ 4 ሿproximity and is defined as
interpolation technique. The bicubic interpolation technique is an weighted average of the


                   ଷ    ଷ

Pୖःऊ ሺx, yሻ ൌ ෍ ෍൫wt ୧୨ ൈ x୧ y ୨ ൯
     ୧౤

                  ୧ୀଵ ୨ୀଵ
Where ‫ݕ , ݔ‬represent the pixel position and wt ୧୨is the weight at position ݅, ݆ of the image
i୬ ‫ א‬Iୗ୏

      Grey scaling is performed on the resized pixels, and the resultant grey scale pixel is
computed based on the following definition.

          Pୋ୰ୣ୷ୗୡୟ୪ୣ ୧౤ ሺx, yሻ
                            ൌ ቂ0.2989 ൈ R ቀPୖःऊ ୧౤ ሺx, yሻቁቃ ൅ ቂ0.5870 ൈ G ቀPୖःऊ ୧౤ ሺx, yሻቁቃ
                            ൅ ቂ0.11400 ൈ B ቀPୖःऊ ୧౤ ሺx, yሻቁቃ

Where R ቀPୖःऊ ୧౤ ሺx, yሻቁrepresented the red channel value, G ቀPୖःऊ ୧౤ ሺx, yሻቁ represents the
green channel value and B ቀPୖःऊ ୧౤ ሺx, yሻቁrepresents the blue channel value of the resized
pixelPୖःऊ ୧౤ ሺx, yሻ.

        Binirazation is performed on the grayscale image utilizing the adaptive thresholding
algorithm. The image noise elimination property of the adaptive thresholding based
binirazation algorithm is an additional parameter for adopting this algorithm. The adaptive
thresholding based binirazation is an iterative process wherein the thresholds are adapted

convergence is achieved. Let P୆୧୬ ୧౤ ሺx, yሻ represent the pixels of the binirized image i୬ . The
based on the regions it is being performed on. The iterative process is terminated when

regions of interest ሺ‫݅݋ݎ‬ሻ is extracted using the connected component labeling algorithm. The
‫ ݏ’݅݋ݎ‬obtained hold the information required and are identified by labels Lୖ୓୍ି୪ ୧౤ . The image

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International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME

i୬ ‫ א‬Iୗ୏ based on the ܴܱ‫ ݏ’ܫ‬is defined as

                    i୬ ି୰୭୧ ൌ ሼL୰୭୧ିଵ ୧౤ ‫ ׫‬L୰୭୧ିଶ ୧౤ ‫ ׫‬L୰୭୧ିଷ ୧౤ ‫ ׫ ڮ ׫‬L୰୭୧ି୪ ୧౤ ሽ

Where ݈represents the total number of ‫ ݏ’݅݋ݎ‬extracted.

        The centroids of the ‫ ݏ’݅݋ݎ‬are computed based on the area enclosed by the ‫ .ݏ’݅݋ݎ‬The
centroids are computed to cluster the similar roi’s together in the same vicinity as it is
observed that the roi’s are similar in nature and can be clustered not effecting the
classification accuracy. This operation adopted enables roi’s exhibiting similar properties to
be clustered into a single cluster together represented asܴܱ‫ .ܫ‬The labels L୰୭୧ି୪ ୧౤ used to
address the roi’s are clustered based on the energy computed and is defined as

ࣟgyୖ୓୍ି୪ ൫Lୖ୓୍ି୪ ୧౤ ൯ ൌ ෍ ቎ ෍ ∆൫L୰୭୧ିୟ ୧౤ , L୰୭୧ିୠ ୧౤ ൯቏
                         ୮୭ୱ ୪ ‫א‬ୗ౨౥౟


Where ∆൫L୰୭୧ିୟ ୧౤ , L୰୭୧ିୠ ୧౤ ൯represents a function ∆൫L୰୭୧ିୟ ୧౤ , L୰୭୧ିୠ ୧౤ ൯ ൌ ሼ0,1ሽ i.e. if the
roi’s L୰୭୧ିୟ ୧౤ and L୰୭୧ିୠ ୧౤ are similar, 0 is returned and 1 in the case of dissimilarity. S୰୭୧ ൌ
ሼ1,2,3, … . lሽrepresents the set of roi’s obtained and a ‫ א‬S୰୭୧and b ‫ א‬S୰୭୧. pos represents the
position of the l୲୦ clustered ROI represented as Lୖ୓୍ି୪ ୧౤ .
The clustered ROI’s obtained define the image i୬ as
i୬ ିୖ୓୍ ൌ ሼLୖ୓୍ିଵ ୧౤ ‫ ׫‬Lୖ୓୍ିଶ ୧౤ ‫ ׫‬Lୖ୓୍ିଷ ୧౤ ‫ ׫ ڮ ׫‬Lୖ୓୍ି୪ ୧౤ ሽ

        Sobel edge detection is performed on the image i୬ ୖ୓୍. Post the edge detection the
distortion on the basis of the coordinate displacement and the frequency of distortion
observed is computed using the Fast Fourier Transforms. The axis based asymmetry
parameter of the lesion is obtained based on the principal component decomposition. The skin
lesion extracted is rotated such that the principal component coincides with the horizontal

p୅୶୧ୱ_ୗ୷୫୫ୣ୲୰୷ ൌ ቂ෍|i୬ ሺxሻ െ ıഥ ሺxሻ|ቃൗቂ෍ i୬ ሺxሻቃ
axis. The skin lesion axis based asymmetry parameter is defined as
                              ୬


Where i୬ ሺxሻthe original skin lesion and its reflected version is represented as ıഥ ሺxሻ.
                                                                                  ୬

        The symmetry parameter is computed for all the pixels within the lesion along the

based on the similarity of a pixel in a n ൈ n neighbourhood. The standard deviation of the
horizontal and vertical axis respectively. The color spreading factor of the lesions is computed

pixels in the neighborhood based on the Red channel, Green channel and Blue channel is
computed using

           1
              ୬మ
                        തതതതതത
        ൌ ඩ ଶ ෍ሺChnl୧ െ Chnlሻଶ
           n
σେ୦୬୪
              ୧ୀଵ
                                           തതതതതത
Where Chnl ൌ ሼR େ୦୬୪ , Gେ୦୬୪ , Bେ୦୬୪ ሽ and Chnlrepresents the mean value defined as



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International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME

      1
              ୬మ
തതതതത
Chn ൌ ଶ ෍ Chn୧
      n
              ୧ୀଵ



σେ୳୫୧୪୧୲୧୴ୣ ൌ σୖి౞౤ౢ ൅ σୋి౞౤ౢ ൅ σ୆ి౞౤ౢ
The cumulative standard deviation for all the channels is defined as



                ‫ۍ‬                           ‫ۍ ې‬                           ‫ې‬
                  1                               1
                     ୬మ                              ୬మ

              ൌ ‫ێ‬ඩ ଶ ෍ሺR େ୦୬୪ ୧ െ R େ୦୬୪ ሻଶ ‫ ۑ‬൅ ‫ێ‬ඩ ଶ ෍ሺGେ୦୬୪ ୧ െ Gେ୦୬୪ ሻଶ ‫ۑ‬
                                  തതതതതതത                        തതതതതതത
                ‫ ێ‬n ୧ୀଵ                     ‫ ێ ۑ‬n ୧ୀଵ                     ‫ۑ‬
σେ୳୫୧୪୧୲୧୴ୣ
                ‫ۏ‬                           ‫ۏ ے‬                           ‫ے‬
                      ‫ۍ‬                     ‫ې‬
                        1
                            ୬మ

                    ൅ ‫ێ‬ඩ ෍ሺBେ୦୬୪ െ Bେ୦୬୪ ሻଶ ‫ۑ‬
                                   തതതതതതത
                      ‫ ێ‬nଶ ୧ୀଵ  ୧
                                            ‫ۑ‬
                      ‫ۏ‬                     ‫ے‬

The color spreading factor of the lesion is computed using

pେ୪୰ିୗ୮୰ୣୟୢ ൌ 1 െ σ୒୭୰୫

Where σ୒୭୰୫is the normalized value of σେ୳୫୧୪୧୲୧୴ୣ between the range 0 and 1 and is obtained

σ୒୭୰୫ ൌ σେ୳୫୧୪୧୲୧୴ୣ ⁄σେ୳୫୧୪୧୲୧୴ୣି୑ୟ୶
as


       In order to extract the lesion border parameters the image is filtered using the
Gaussian and laplacian filter. The parameter describing the lesion borders is obtained by
computing the mean of the resultant pixels and is defined as

                         ୛ୢ ୌ୲

p୐ୣୱ୧୭୬ି୆୭୳୬ୢ୰୷ ൌ ቎෍ ෍ P୧౤ ሺx, yሻ቏൘ሾWd ൈ Htሿ
                         ୶ୀଵ ୷ୀଵ


       In this phase the parameters of the lesions such as the asymmetry along the horizontal
and vertical axis, the color spreading factor of the lesion and the boundary parameters is
discussed.

        3.3.2. ‫ ܁۱۳۾ۻ‬PARAMETER EXTRACTION – PHASE 2

        The second phase of the ‫ ܵܥܧܲܯ‬enables the extraction of the area, perimeter and the
eccentricity. This phase considers the binirized and clustered ܴܱ‫ ܫ‬image as an input and

݅௡ ିோைூ ൌ ሼ‫ܮ‬ோைூିଵ ௜೙ ‫ܮ ׫‬ோைூିଶ ௜೙ ‫ܮ ׫‬ோைூିଷ ௜೙ ‫ܮ ׫ ڮ ׫‬ோைூି௟ ௜೙ ሽ
computes the parameters based on the binirized image obtained from phase 1 defined as

Where ݈ represents the total number of ܴܱ‫ ݏ’ܫ‬identified for the image ݅௡ .
        The ܴܱ‫ ݏ’ܫ‬of image ݅௡ ିோைூ contain binirized pixel points i.e. black or white pixels.
The area parameter defines the white pixels that are encapsulated by the skin lesion and the
lesion area parameter is computed using

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                              ௉௡௧௦ିଵ

‫݌‬௅௘௦௜௢௡ି஺௥௘௔ ൌ ሺ1⁄2ሻ ቎ | ෍ ൫‫ݔ‬௜ ‫ݕ‬௝ାଵ െ ‫ݔ‬௜ାଵ ‫ݕ‬௝ ൯ | ቏
                              ௜,௝ୀ଴
Where ܲ݊‫ ݏݐ‬is the number of points enclosed by the skin lesion defined by the ݈ ௧௛ ܴܱ‫ . ܫ‬Also
݅, ݆ represent the ܲ݊‫ݏݐ‬location and ൫‫ݔ‬௜ , ‫ݕ‬௝ ൯ ‫ א‬൛൫‫ݔ‬௜ , ‫ݕ‬௝ ൯ห ݂൫‫ݔ‬௜ , ‫ݕ‬௝ ൯ ൌ 1 ሽ as ‫ݔ‬௜ , ‫ݕ‬௝ represents a

         The perimeter defines the boundary ߂‫ ܤ‬length of the skin lesion. The lesion boundary
white pixel.


߂‫ܤ‬ሺ݈ሻ ൌ ሺ‫ܤ‬ሺ2: ݈, : ሻ െ ‫ܤ‬ሺ1: ݈ െ 1, : ሻሻଶ
is computed using

Where ݈ represents the number of ܴܱ‫ ݏ’ܫ‬and its corresponding boundary is represented as ‫.ܤ‬
The parameter defining boundary of the skin lesion is defined as

‫݌‬௅௘௦௜௢௡ି஻௢௨௡ௗ ൌ ෍ ඨ෍ ߂‫ܤ‬ሺ݈ሻ
                          ௟


eccentricity is obtained by obtaining the moments represented as ‫ .ܯ‬The major axis is
       The aspect ratio of the skin lesion is defined as the eccentricity of a skin lesion. The


‫ܣ‬ெ௔௝௢௥ ൌ 2√2ሺඥ‫ܯ‬௫௫ ൅ ‫ܯ‬௬௬ ൅ ‫ܯ‬஼௢௠௠ ሻ
defined as


‫ܣ‬ெ௜௡௢௥ ൌ 2√2ሺඥ‫ܯ‬௫௫ ൅ ‫ܯ‬௬௬ െ ‫ܯ‬஼௢௠௠ ሻ
The minor axis is defined as

Where the moments ‫ܯ‬௫௫ , ‫ܯ‬௬௬ , ‫ܯ‬௫௬ and‫ܯ‬஼௢௠௠ are defined as
‫ܯ‬௫௫ ൌ ቂቀ෍ ‫ ݔ‬ଶ ቁൗܰ ൅ 1⁄12ቃ

‫ܯ‬௬௬ ൌ ቂቀ෍ ‫ ݕ‬ଶ ቁൗܰ ൅ 1⁄12ቃ

‫ܯ‬௫௬ ൌ ቂቀ෍ ‫ݕ כ ݔ‬ቁൗܰቃ
And
‫ܯ‬஼௢௠௠ ൌ ටሺ‫ܯ‬௫௫ െ ‫ܯ‬௬௬ ሻଶ ൅ 4‫ܯ‬௫௬ ଶ
The eccentricity parameter is computed using
‫݌‬௅௘௦௜௢௡ିா௖௖௘௡௧ ൌ ቈට൫‫ܣ‬ெ௔௝௢௥ ଶ െ ‫ܣ‬ெ௜௡௢௥ ଶ ൯቉ൗ‫ܣ‬ெ௔௝௢௥
                    మ




        3.3.3. ‫ ܁۱۳۾ۻ‬PARAMETER EXTRACTION – PHASE 3

    Researches have well understood the importance to 3‫ ܦ‬depth parameters to enhance the
classification accuracy [6] [19]. To obtain the 3‫ ܦ‬depth of the skin lesions the
‫ ܵܥܧܲܯ‬introduced in this paper considers creating a 3 ൈ 3 masked windows represented as

                ܹଵ ܹଶ ܹଷ
݅௡ ଷൈଷିெ௔௦௞ ൌ ൥ܹସ ܹହ ܹ଺ ൩
                ܹ଻ ଼ܹ ܹଽ
Where ܹ௜ represent the weights of the pixel
The projection filter is defined as


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International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –
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                ଽ

݅௡ ଷ஽ି஽௘௣௧௛ ൌ ෍ ܹ௜ ‫ܫ‬௜
               ௜ୀଵ
Where ‫ܫ‬௜ represents the intensity of the ݅ ௧௛ pixel.
The 3D depth projection parameter ‫݌‬௅௘௦௜௢௡ିଷ஽஽௘௣௧ is obtained by considering the geometric

‫݌‬௅௘௦௜௢௡ିଷ஽஽௘௣௧
mean defined as

               ൌ ሾ1⁄9ሿ
               ൈ ൣ‫ܫ‬௫ିଵ,௬ିଵ ൅ ‫ܫ‬௫ିଵ,௬ଵ ൅ ‫ܫ‬௫ିଵ,௬ାଵ ൅ ‫ܫ‬௫,௬ିଵ ൅ ‫ܫ‬௫,௬ ൅ ‫ܫ‬௫,௬ାଵ ൅ ‫ܫ‬௫ାଵ,௬ିଵ ൅ ‫ܫ‬௫ାଵ,௬ଵ
               ൅ ‫ܫ‬௫ାଵ,௬ାଵ ሿ
Where ‫ ݕ ,ݔ‬represent the horizontal and vertical pixel position.

       3.3.4. ‫ ܁۱۳۾ۻ‬PARAMETER EXTRACTION – PHASE 4

    The fourth phase of the ‫ܵܥܧܲܯ‬is targeted towards obtaining the color components of the
skin lesions. The color components are extracted for the red channel blue channel and the
green channel. The mean and the variance parameters of each channel are considered as the
parameters to be utilized for classification. For the red channel the mean parameter is defined

                ∑ௐௗ ∑ு௧ ܴ஼௛௡௟ ሺ‫ݕ ,ݔ‬ሻ
as follows

 ‫݌‬ோ஼௛௡௟ିெ௘௔௡ ൌ
                  ௫ୀଵ ௬ୀଵ
                        ܹ݀ ‫ݐܪ כ‬

Where ‫ ݕ ,ݔ‬represent the pixel positions.

    The variance parameter is computed by obtaining the mean of all the ‫ܤ‬஼௛௡௟ columns , the
difference amongst them and then summed square difference divided by the height ‫ݐܪ‬
                      ∑ௐௗ ܴሺ݅ሻ
‫݊ܽ݁݉_݈݉ܥ‬ோ஼௛௡௟ ሺ݅ሻ ൌ
                       ௜ୀଵ
                        ܹ݀

‫݂݂݅ܦ‬ோ஼௛௡௟ ሺ݅ ሻ ൌ ܴ‫݄݊ܥ‬ሺ: , ݅ ሻ െ ‫݊ܽ݁݉_݈݉ܥ‬ோ஼௛௡௟ ሺ݅ሻ

                ∑௜ ‫݂݂݅ܦ‬ோ஼௛௡௟ ሺ݅ሻ
‫݌‬ோ஼௛௡௟ି௏௔௥ ൌ
                   ሺ‫ ݐܪ‬െ 1ሻ


                 ∑ௐௗ ∑ு௧ ‫ܩ‬஼௛௡௟ ሺ‫ݕ ,ݔ‬ሻ
Similarly the green channel and blue channel parameters are defined as

‫ீ݌‬಴೓೙೗௟ିெ௘௔௡ ൌ
                   ௫ୀଵ ௬ୀଵ
                       ܹ݀ ‫ݐܪ כ‬
               ∑௜ ‫ீ݂݂݅ܦ‬಴೓೙೗ ሺ݅ ሻ
‫ீ݌‬಴೓೙೗ି௏௔௥ ൌ
                     ሺ‫ ݐܪ‬െ 1ሻ
                ∑ௐௗ ∑ு௧ ‫ܤ‬஼௛௡௟ ሺ‫ݕ ,ݔ‬ሻ
‫݌‬஻಴೓೙೗ିெ௘௔௡ ൌ
                 ௫ୀଵ ௬ୀଵ
                      ܹ݀ ‫ݐܪ כ‬
               ∑௜ ‫݂݂݅ܦ‬஻಴೓೙೗ ሺ݅ሻ
‫݌‬஻಴೓೙೗ି௏௔௥ ൌ
                     ሺ‫ ݐܪ‬െ 1ሻ



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International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –
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       3.3.5. ‫ ܁۱۳۾ۻ‬PARAMETER EXTRACTION – PHASE 5
    This phase of the ‫ ܵܥܧܲܯ‬discusses the smoothening process of the ܴ‫ ݈݄݊ܥ‬and
‫ܩ‬஼௛௡௟ using the 3 ൈ 3 masking procedure discussed in the third phase. The smoothing is not
adopted for the ‫ܤ‬஼௛௡௟ as the blue veils of the skin lesions is an important parameter for early
detection of skin cancer melanoma [20]. The smoothening filter for the ܴ‫ ݈݄݊ܥ‬and ‫ܩ‬஼௛௡௟ is
                       ଽ
defined as follows

݅௡ ோ஼௛௡௟ିௌ௠௢௢௧௛ ൌ ෍ ܹ௜ ܴ‫݈݄݊ܥ‬௜
                      ௜ୀଵ
                       ଽ

݅௡ ீ              ൌ ෍ ܹ௜ ‫ܩ‬஼௛௡௟ ௜
   ಴೓೙೗ ିௌ௠௢௢௧௛
                     ௜ୀଵ
Where ܹ௜ represent the weights of the pixel and ܴ‫݈݄݊ܥ‬௜ , ‫ܩ‬஼௛௡௟ ௜ is red channel intensity and
green channel intensity of the ݅௧௛ pixel.
The red channel smoothened parameter ‫݌‬ோ஼௛௡௟ିௌ௠௢௢௧௛ is defined as
‫݌‬ோ஼௛௡௟ିௌ௠௢௢௧௛
               ൌ ሾ1⁄9ሿ
               ൈ ൣܴ‫݈݄݊ܥ‬௫ିଵ,௬ିଵ ൅ ܴ‫݈݄݊ܥ‬௫ିଵ,௬ଵ ൅ ܴ‫݈݄݊ܥ‬௫ିଵ,௬ାଵ ൅ ܴ‫݈݄݊ܥ‬௫,௬ିଵ ൅ ܴ‫݈݄݊ܥ‬௫,௬
               ൅ ܴ‫݈݄݊ܥ‬௫,௬ାଵ ൅ ܴ‫݈݄݊ܥ‬௫ାଵ,௬ିଵ ൅ ܴ‫݈݄݊ܥ‬௫ାଵ,௬ଵ ൅ ܴ‫݈݄݊ܥ‬௫ାଵ,௬ାଵ ሿ
The green channel smoothened parameter ‫ீ݌‬಴೓೙೗ିௌ௠௢௢௧௛
‫ீ݌‬಴೓೙೗ିௌ௠௢௢௧௛ ൌ ሾ1⁄9ሿ
                  ൈ ቂ‫ܩ‬஼௛௡௟ ௫ିଵ,௬ିଵ ൅ ‫ܩ‬஼௛௡௟ ௫ିଵ,௬ଵ ൅ ‫ܩ‬஼௛௡௟ ௫ିଵ,௬ାଵ ൅ ‫ܩ‬஼௛௡௟ ௫,௬ିଵ ൅ ‫ܩ‬஼௛௡௟ ௫,௬
                  ൅ ‫ܩ‬஼௛௡௟ ௫,௬ାଵ ൅ ‫ܩ‬஼௛௡௟ ௫ାଵ,௬ିଵ ൅ ‫ܩ‬஼௛௡௟ ௫ାଵ,௬ଵ ൅ ‫ܩ‬஼௛௡௟ ௫ାଵ,௬ାଵ ൧

       3.3.6. ‫ ܁۱۳۾ۻ‬PARAMETER EXTRACTION – PHASE 6


components of the skin lesion to obtain accurate classification results [21][22]. The ‫ܵܥܧܲܯ‬
    For early detection of skin cancer melanoma it is essential to extract all the color

discussed considers the cylindrical coordinate representation of pixels in the fourth and fifth

‫ ܵܥܧܲܯ‬considers the Cartesian representation of pixels. Hue, Saturation and Value
phase. In order to extract accurate and elaborate color parameters this phase of the

representation of the pixels are considered as the Cartesian representations. The mean and

from the ܴ஼௛௡௟ , ‫ܩ‬஼௛௡௟ ܽ݊݀‫ܤ‬஼௛௡௟ pixel values of the skin lesions. The hue value of a pixel is
variance parameters of the hue channel, variance channel and the value channel are extracted


                  43 ‫ܩ| כ‬஼௛௡௟ െ ‫ܤ‬஼௛௡௟ |
computed using the following definition
          ‫ۓ‬ቈ0 ൅                         ቉      , ‫ ݈ܸܽݔܽܯ‬ൌ ܴ‫݈݄݊ܥ‬
          ۖ       ‫ ݈ܸܽݔܽܯ‬െ ‫݈ܸܽ݊݅ܯ‬
          ۖ
                   43 ‫ܤ| כ‬஼௛௡௟ െ ܴ‫|݈݄݊ܥ‬
‫݁ݑܪ‬௜,௝ ൌ ቈ85 ൅                            ቉      , ‫ ݈ܸܽݔܽܯ‬ൌ ‫ܩ‬஼௛௡௟
          ‫۔‬         ‫ ݈ܸܽݔܽܯ‬െ ‫݈ܸܽ݊݅ܯ‬
          ۖቈ171 ൅ 43 ‫ ݈݄݊ܥܴ| כ‬െ ‫ܩ‬஼௛௡௟ |቉
          ۖ
                                                 , ‫ ݈ܸܽݔܽܯ‬ൌ ‫ܤ‬஼௛௡௟
          ‫ە‬          ‫ ݈ܸܽݔܽܯ‬െ ‫݈ܸܽ݊݅ܯ‬

Where ‫ ݈ܸܽݔܽܯ‬ൌ ‫ݔܽܯ‬ሺܴ‫ܩ ,݈݄݊ܥ‬஼௛௡௟ , ‫ܤ‬஼௛௡௟ ሻ is the maximum value of the red green and
blue channel of the pixel

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‫ ݈ܸܽ݊݅ܯ‬ൌ ‫݊݅ܯ‬ሺܴ‫ܩ ,݈݄݊ܥ‬஼௛௡௟ , ‫ܤ‬஼௛௡௟ ሻis the minimum value of the red green and blue channel
of the pixel

                         ሼ‫ ݈ܸܽݔܽܯ‬െ ‫݈ܸܽ݊݅ܯ‬ሽ
The Saturation and the value parameter of the pixel is computed using
ܵܽ‫݊݋݅ݐܽݎݑݐ‬௜,௝ ൌ 255 ‫כ‬
                               ‫݈ܸܽݔܽܯ‬
ܸ݈ܽ‫݁ݑ‬௜,௝ ൌ ‫݈ܸܽݔܽܯ‬

                    ∑ௐௗ ∑ு௧ ‫݁ݑܪ‬஼௛௡௟ ሺ‫ݕ ,ݔ‬ሻ
The mean of the hue channel and the is defined as

 ‫݌‬ு௨௘಴೓೙೗ ௟ିெ௘௔௡ ൌ
                      ௫ୀଵ ௬ୀଵ
                             ܹ݀ ‫ݐܪ כ‬
The variance is computed in a manner similar to the procedure described in phase 4 and is

                   ∑௜ ‫݂݂݅ܦ‬ு௨௘಴೓೙೗ ሺ݅ ሻ
defined as
 ‫݌‬ு௨௘಴೓೙೗ ି௏௔௥ ൌ
                       ሺ‫ ݐܪ‬െ 1ሻ

                          ∑ௐௗ ∑ு௧ ܵܽ‫݊݋݅ݐܽݎݑݐ‬஼௛௡௟ ሺ‫ݕ ,ݔ‬ሻ
The mean and the variance of the saturation channel is defined as

 ‫݌‬ௌ௔௧௨௥௔௧௜௢௡಴೓೙೗ ିெ௘௔௡ ൌ
                            ௫ୀଵ ௬ୀଵ
                                       ܹ݀ ‫ݐܪ כ‬
                         ∑௜ ‫݂݂݅ܦ‬ௌ௔௧௨௥௔௧௜௢௡಴೓೙೗ ሺ݅ሻ
 ‫݌‬ௌ௔௧௨௥௔௧௜௢௡಴೓೙೗ ି௏௔௥ ൌ
                                ሺ‫ ݐܪ‬െ 1ሻ

                     ∑ௐௗ ∑ு௧ ܸ݈ܽ‫݁ݑ‬஼௛௡௟ ሺ‫ݕ ,ݔ‬ሻ
Accordingly the Value channel parameters are obtained using the following equations

 ‫݌‬௏௔௟௨௘಴೓೙೗ ିெ௘௔௡ ൌ
                       ௫ୀଵ ௬ୀଵ
                              ܹ݀ ‫ݐܪ כ‬
                    ∑௜ ‫݂݂݅ܦ‬௏௔௟௨௘಴೓೙೗ ሺ݅ ሻ
 ‫݌‬௏௔௟௨௘಴೓೙೗ ି௏௔௥ ൌ
                         ሺ‫ ݐܪ‬െ 1ሻ
        The ‫ ܵܥܧܲܯ‬discussed in this paper discusses the extraction 21 vital parameters which
would enable for classification of skin lesions especially targeted towards early detection of
skin cancer melanoma. The parameter extraction and the procedure involved in extraction are
achieved adopting a six phase approach discussed above.

   3.4. ࡹࡲࡺࡺ CLASSIFIER




                         Figure 1 : A MFNN Classifier Structure


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International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –
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        The classification of the testing data set of dermoscopic images requires a supervised
training procedure to be incorporated. A multilayer feed forward neural network (‫)ܰܰܨܯ‬
with the back propagation algorithm for training is adopted for the purpose of classification.

of the neural network. A sample ‫ ܰܰܨܯ‬is shown in Fig 1. The ‫ ܰܰܨܯ‬consists of a set of
The parameters extracted from the testing image set are directly provided to the input neurons

input neurons ,‫ ܯ‬number of hidden layers and an output layer.The ‫ ܰܰܨܯ‬classifier is a
machine learning algorithm. The learning and classification is achieved based on the

layer represented as ܽఊ where ߛ ൌ 1,2,3, … c୰ and c୰ is the trained ‫ ݎ‬classes.
weightsࣱ which are changed in the neighboring layers. The input to the neuron in every



                                ࣿࣾషభ
          The output of the neuron is defined as

ܽࣼ    ൌ   ݂൫߮ࣼ ൯      ൌ ݂ ቌ ෍ ࣱࣼࣻ ܽࣻ
                                             ሺࣾିଵሻఊ
                                                      ቍ
 ࣾఊ          ࣾఊ                 ࣾ

                                ࣻୀ଴


݉represents the index of the hidden layer i.e. 1 ൑ ݉ ൑ ‫ܯ‬
Where

݊௠ represents the number of neurons in݉௧௛ layer
݆ represents the index ݆ | 1 ൑ ݆ ൑ ݊௠
߮ ࣼ represents the weighted sum of input values for ࣼ ௧௛ neuron in layer ܽ
ࣱࣼ௜ represents the weight between ࣼ ௧௛ neuron in ݉௧௛ layer and ݅ ௧௛ neuron in ሺ݉ െ 1ሻ௧௛ layer
   γ

    ௠



The classification error ߜࣼ ം observed at the output layer is obtained from the following
                                 ࣧ

definition

ܽࣼ    ൌ ݂ ′ ൫߮ࣼ ൯ߜࣼ ൌ ݂ ′ ൫߮ࣼ ൯൫‫ࣼݕ‬                െ ‫ ࣼݕ‬൯
 ࣾఊ              ெఊ    ఊ              ெఊ     ࣴఊ      ఊ



The sum of square errors observed through the ‫ ܰܰܨܯ‬is defined as

                                                       1
                                                            ௔

                                                   ߦఊ ൌ ෍൫ߜ௝ ൯
                                                            ఊ ଶ
                                                       2
                                                           ࣼୀଵ



                       ௔ࣾశభ
The error existent at the output layers is propagated back and is defined as

      ൌ݂       ൫߮ࣼ ൯   ෍ ߜℓ
                                ሺࣾାଵሻఊ       ሺࣾାଵሻ
ߜࣼ                                         ࣱℓࣼ
 ࣾఊ        ᇱ     ࣾఊ

                       ℓୀଵ
The errors propagated is minimized by altering the weights of the neighboring neurons and

∆ஓ ࣱ௝௜ ൌ ߟߜࣼ ‫ࣻݑ‬
     ௠        ࣾఊ ሺࣾିଵሻఊ
the weight update function is defined as


            ୡ౨             ୡ౨
                 1
                                 ௔
The classification error that exist through
                                 ଶ
ߦ஼௟௔௦௦   ൌ ෍ ߦఓ ൌ ෍ ෍ቀ‫ ࣼݕ‬െ ߮ ࣼ ቁ
                       ୸ஓ    ஓ
                 2
           ఓୀଵ             ఓୀଵ ࣼୀଵ
       The ‫ ܰܰܨܯ‬Classifier is designed to minimize the classification error by effective
weight adjustments achieved due to the back propagation algorithm.

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        The ‫ ܵܥܧܲܯ‬adopts machine learning techniques for classification. The ‫ ܰܰܨܯ‬based
classifier is adopted for classification which classifies based on the supervised learning
imparted using the back propagation algorithm. Parameter extraction is achieved using a six
phase approach to define the skin lesion. The training dataset constitutes of a set of
dermoscopic images which are pre-classified with the aid of dermatologists. The images

set. The ‫ ܰܰܨܯ‬classifier is trained using the training data set. The testing set consists of
defined in terms of the parameters and the classified type constitute towards the training data

dermoscopic images that need to be diagnosed. The diagnostic results are represented as

using the ‫ ܵܥܧܲܯ‬parameter extraction phases. The resulting dataset is termed as the test
classes. To enable diagnosis the test images are represented as a set of parameters extracted

dataset. The test dataset is provided to the trained ‫ ܰܰܨܯ‬for classification. The subsequent

ܸܵ‫ ܯ‬classifiers.
section of this paper discusses the experimental verification and comparisons with the


4. EXPERIMENTAL STUDY AND COMPARISONS

    The ‫ ܵܥܧܲܯ‬discussed in this paper was realized on the MATLAB platform. In order to
evaluate the ‫ ܵܥܧܲܯ‬training and testing dataset are to be created. To construct the training
and testing datasets dermoscopic images are obtained from the “The Atlas of Dermoscopy”
[25]. The atlas considered is a collection of over 2000 skin lesion images obtained from

a reference to evaluate the classification accuracy of the ‫ ܵܥܧܲܯ‬system proposed. The use of
various patients. The atlas also provides the classification and diagnosis data which is used as

the atlas is charted as others researchers too have used the same atlas to evaluate their

‫ ܵܥܧܲܯ‬proposed is compared with the popular ܸܵ‫ ܯ‬classifiers as proposed by Xiaojing
proposed diagnosis mechanisms especially skin cancer melanoma [20] [26]. The

Yuan et al.[18].


                                                CLASSIFICATION ERROR
                                14                                                      13.33 %

                                12
    CLASSIFICATION ERROR IN %




                                10

                                 8

                                 6

                                 4

                                 2
                                                < 1%
                                 0
                                     MEPCS - CLASSIFICATION ERROR        SVM CLASSIFIER - CLASSIFICATION ERROR

                                             Figure 2 : Classification Error Observed


                                                                    29
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME

    The training dataset consists of pre-classified dermoscopic images. We have considered a
set of 45 dermoscopic images from the “Atlas of Dermoscopy” as the training data set. The
training dataset consist of skin lesion images of primarily 3 classes namely “Advanced
Melanoma”, “Early Melanoma” and “Non-Melanoma”. The 21 parameters extracted per
image using the 6 phase approach contribute to each record of the training data set along with

The ‫ ܵܥܧܲܯ‬and the ܸܵ‫ ܯ‬Classifier was trained using similar training datasets. Similar test
the classified name. A similar approach is followed to construct the testing dataset.

data sets were evaluated used the ‫ ܵܥܧܲܯ‬and ܸܵ‫ ܯ‬classifier. The classification results
obtained are provided to the Receiver Operating Characteristic (ܴܱ‫ܥ‬ሻ analysis tool. The error

classification error of the ܸܵ‫ ܯ‬classifier is greater than the error exhibited by the proposed
in classification obtained is graphically shown in Fig 2. From the figure it is evidentthat the

‫ ܵܥܧܲܯ‬system.The ‫ ܵܥܧܲܯ‬and the ܸܵ‫ ܯ‬classifier classifiers are compared on the basis of
the receiver operating characteristic (ܴܱ‫ )ܥ‬analysis. The ܴܱ‫ ܥ‬analysis is carried out using a

The ܴܱ‫ ܥ‬analysis plot obtained is shown in Fig 3.
tool developed using C#.Net programming language on the Visual Studio 2010 platform.




                                Receiver Operating Characteristic (ROC) - CURVE
                      1
                    0.9
                    0.8
                    0.7
      SENSITIVITY




                    0.6
                    0.5
                    0.4
                    0.3
                    0.2                                       MPECS           SVM CLASSIFIER
                    0.1
                      0
                          0.1     0.2    0.3    0.4     0.5     0.6     0.7    0.8     0.9     1


                                        Figure 3 : ‫ ۱۽܀‬Curve Analysis
                                                       1- SPECIFICITY




    The classification accuracy and the efficiency of the ‫ ܵܥܧܲܯ‬Classifier along with the
ܸܵ‫ ܯ‬Classifier are diagrammatically shown in Fig 4 and Fig 5. The ‫ ܵܥܧܲܯ‬systemexhibits
an 81% accuracy in classification as compared to the 75% accuracy exhibited by the ܸܵ‫ܯ‬
Classifier. The ‫ ܵܥܧܲܯ‬system proposed in this paper improves the efficiency in
classification by about 7.4% when compared to the ܸܵ‫ ܯ‬classifier.The experimental study

the ‫ ܵܥܧܲܯ‬system proposed in this paper is capable of accurately classifying skin lesions
conducted and from the results discussed in this section of the paper, it can be concluded that

exhibiting early melanotic symptoms when compared to the popularly used ܸܵ‫ ܯ‬classifier
systems.



                                                      30
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME


                                              CLASSIFIER ACCURACY
                0.81
                 0.8
                0.79
                0.78
     ACCURACY




                0.77
                0.76
                0.75
                0.74
                0.73
                0.72
                                          MEPCS ACCURACY                     SVM CLASSIFIER ACCURACY

                                       Figure 4 : Classification Accuracy Comparisons



                                0.83          CLASSIFIER EFFICIENCY
                                0.82
                                0.81
                                 0.8
                   EFFECIENCY




                                0.79
                                0.78
                                0.77
                                0.76
                                0.75
                                0.74
                                0.73
                                           MPECS EFFICIENCY        SVM CLASSIFIER EFFICIENCY
                                       Figure 5: Classification Efficiency Comparisons

5. CONCLUSION

    This paper highlights the benefits and the requirement for early detection of skin cancer

paper relies on a supervised ‫ ܰܰܨܯ‬based classifier to accurately classify skin lesions and enable
melanoma. The Multi Parameter Extraction and Classification System (‫ )ܵܥܧܲܯ‬discussed in this

early detection of skin cancer melanoma. The ‫ ܵܥܧܲܯ‬comprehensively defines skin lesions in terms

of the ‫ ܵܥܧܲܯ‬is discussed in this paper. The ‫ ܵܥܧܲܯ‬system is trained using the Back Propagation
of a parameter sets extracted using the six phase approach. The training and testing phase algorithms

Algorithm. The trained classifier of the ‫ ܵܥܧܲܯ‬is used for analysis and diagnosis of skin lesions on
unknown types. The classification preeminence of the ‫ ܵܥܧܲܯ‬is proved over the popular SVM
classifier systems.The results obtained and discussed prove that the ‫ ܵܥܧܲܯ‬system proposed in this
paper aids the detection of skin cancer melanoma in the naïve stages thereby improving computer
aided diagnosis mechanisms and early treatment initiatives to be adopted by dermatologists for the
benefit of patients suffering from the life threatening melanotic skin cancer diseases.

                                                              31
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME

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                                             33

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Detecting in situ melanoma using multi parameter extraction and neural classification mechanisms

  • 1. INTERNATIONAL JOURNAL OF COMPUTER(IJCET), ISSN 0976 – International Journal of Computer Engineering and Technology ENGINEERING 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1,(IJCET) & TECHNOLOGY January- February (2013), © IAEME ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), pp. 16-33 IJCET © IAEME: www.iaeme.com/ijcet.asp Journal Impact Factor (2012): 3.9580 (Calculated by GISI) ©IAEME www.jifactor.com DETECTING IN-SITU MELANOMA USING MULTI PARAMETER EXTRACTION AND NEURAL CLASSIFICATION MECHANISMS Ruksar Fatima1,Dr.Mohammed Zafar Ali Khan2,Dr. A. Govardhan3 and Kashyap Dhruve4 1 (Head Dept. of Bio-Medical Engg, KBN College of Engg, Gulbarga, India, Ruksar_blue@yahoo.co.in) 2 (Associate Professor,IIIT Hyderabad, Hyderabad, India,zafar @iith.ac.in) 3 (Director of Evaluation of JNTU, Hyderabad, India, dejntuh@jntuh.ac.in) 4 (Technical Director, Planet-i Technologies, Bangalore, India,kashyapdhruve@hotmail.com,) ABSTRACT Computer aided diagnostics systems are widely used for diagnosis of skin lesions. aimed to enable early detection of skin cancer melanoma. The ‫ ܵܥܧܲܯ‬adopts a supervised This paper discusses a Multi Parameter Extraction and Classification System (‫ )ܵܥܧܲܯ‬to machine learning algorithm for classification. The dermoscopic images are represented by operation of the ‫ ܵܥܧܲܯ‬in the training and testing phase. The adoption of the Multilayer extensive parameter sets extracted using a six phase approach. The paper discusses the Feed Forward Neural Network (‫ )ܰܰܨܯ‬classifier is justified, its proficiency in accurately diagnosing and classifying early signs of skin cancer melanoma in dermoscopic images of skin lesions is proved through the experimental results discussed in the manuscript. Keywords: Skin Cancer, Melanoma, Early Detection, In Situ Melanoma, MPECS , Multi Parameter Extraction, Classification, Computer Aided Diagnostics, Image Processing, Skin Lesions. Feature Extraction, Multilayer Feed Forward, Neural Network, Back Propagation, SVM Classifier, ROC, Receiver Operating Characteristics 1. INTRODUCTION Computer vision based diagnosis systems which are non-invasive have experienced a wide acceptability ratio in the recent years. Deaths arising from skin cancers have increased tremendously in the past decade [1] [2][3]. Skin Cancer Melanoma is dangerous and accounts for maximum number of deaths arising due to skin cancers [3][4]. A recent survey [5] conducted in 2012 for the United States of America alone, states that more than 75% of the deaths arising from skin cancers are due to melanotic skin lesions. Skin cancer melanomas 16
  • 2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME are generally diagnosed by adopting biopsy procedures recommended by dermatologists. Studies have proved that the mortality rate could be reduced if melanotic lesions are detected The research work presented here puts forth the ‫ ܵܥܧܲܯ‬to aid early detection of in at a very early stage [6]. situ melanoma and classification of skin lesions. The dermoscopic images of skin lesions are represented as a set of 21 features or parameter extracted phase wise. The skin lesions represented as a set of features need to be classified. Statistical analysis carried out for on the parameter set obtained post extraction were inconclusive in classifying the skin lesions [7] into the ‫ .ܵܥܧܲܯ‬Machine learning algorithms are adopted for accurate classification. A hence there was an requirement for advanced classification mechanisms to be incorporated ‫ ܰܰܨܯ‬classifier trained using the back propagation algorithm is adopted. The training phase of the ‫ ܵܥܧܲܯ‬enables the system to understand the features that exhibit properties of specific classes defined during training. Subsequently the ‫ ܵܥܧܲܯ‬can be utilized for classification of the testing data set. The proposed methodology is compared with the popular ܸܵ‫ ܯ‬classifier. This research manuscript is organized as follows. Section 2 discusses the methodologies adopted by researchers. The next section discusses the proposed ‫ ܵܥܧܲܯ‬at literaturesurvey about skin cancer melanoma diagnosis systems and classification length along with the ‫ ܰܰܨܯ‬classifier. The experimental evaluation and comparisons are discussed in the penultimate section of this paper. The conclusion of the research work presented in this paper is discussed in the fifth section. 2. LITERATURE SURVEY The Asymmetry Border Color and Differential Structure (ABCD) methodology is the most commonly used in the diagnosis of skin cancer melanoma [8]. To enhance the efficiency additional optimizations such as fuzzy border extraction [9], “JSEG” based optimization [10]; hybrid techniques [11] have been incorporated. A classification accuracy of about 60% was achieved by incorporating wavelet decomposition techniques along with the ABCD principle. Skin lesion classification can also be achieved using the Menzies Method [12]. The ABCD based methods and the Menzies Method exhibit lesser accuracy in detection of early stages of skin cancer melanoma. G. Betta at el [13] introduced a seven point checklist method to diagnose skin lesions which proved to exhibit higher classification accuracy than the other methodologies described. The computational complexity of the seven point checklist method is considered as a major drawback. The, methods discussed above exhibited less of low classification accuracy. The need of machine learning algorithms to accurately classify and diagnose early symptoms of skin cancer melanoma arose. The use of advance clustering techniques [14], k Nearest Neighborhood [6], classification/regression trees [15] has been closely studied. The use of the support vector machines (ܸܵ‫ )ܯ‬for of skin cancer melanoma [18]. The ‫ ܵܥܧܲܯ‬adopts the ‫ ܰܰܨܯ‬based classifier [23]. The classification [16] [17] has been proposed by most of the researchers even for early detection above mentioned methodologies are inaccurate to detect and classify early stages of skin cancer melanoma. A few systems proposed to detect early symptoms exhibit low classification accuracy as the early stages of skin cancer melanoma described through features belong to the non-melanoma and melanoma classes cumulatively making classification a very difficult or cumbersome task. 17
  • 3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME 3. MULTI PARAMETER EXTRACTION AND CLASSIFICATION SYSTEM (ࡹࡼࡱ࡯ࡿ) MODELING Appropriate skin lesions diagnosis at early stages enable possible cure to melanoma skin cancers. Skin lesions of the melanotic type in the in situ stages are easily mistaken with other skin related ailments by even experienced physicians. This paper introduces a computer aided skin lesion diagnosis system named Multi Parameter Extraction and Classification accounts for the highest number of deaths amongst skin cancers. The ‫ ܵܥܧܲܯ‬relies on the System (‫ )ܵܥܧܲܯ‬aiding early detection of skin cancer melanoma. Skin cancer melanoma machine learning techniques incorporated to accurately classify the skin lesions defined by a set of parameters extracted. 3.1. ࡹࡼࡱ࡯ࡿ – PRELIMINARY NOTATION Let‫ܫ‬ௌ௄ represent a set of dermoscopic skin lesion images for analysis defined as ‫ܫ‬ௌ௄ ൌ ሼ݅ଵ , ݅ଶ , ݅ଷ , ݅ସ , … … ݅௡ ሽ ݅ represents an image Where ݊represents the total number of images to be analyzed. The MPECS system proposed in this paper is aids the early diagnosis of skin cancer melanoma skin lesions. Let the classification set of the skin lesions be defined as C ൌ ሼcଵ , cଶ , cଷ … cୡ୪ ሽ Where c represents the class and cl represents the total number of classes considered. Based on the above mentioned definitions it could be stated that ‫ ׊‬i ‫ א‬I ‫ !׌ ׷‬c ‫ א‬C | i ‫ א‬c. The ‫ ܵܥܧܲܯ‬aids the classification of skin lesions from dermoscopic images. The skin lesions are defined as a set of parameter P that it exhibits. Let the Parameter set be defined by P ൌ ሼpଵ , pଶ , pଷ , … pୟ ሽ Where a represents the total number of p parameters In the MPECS an image i ‫ א‬I is represented by a set of parameters P is used for classification. The image I୬ could now defined as I୬ ൌ ൛p୬ଵ , p୬ଶ , p୬ଷ , … p୬୮ ൟ An image I୬ could be represented as a matrix and is defined as 18
  • 4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME x଴,଴ ‫ ڮ‬x଴,୛ୢ I୬ ൌ ൥ ‫ڭ‬ ‫ڰ‬ ‫ ڭ‬൩ xୌ୲,଴ ‫ ڮ‬xୌ୲,୛ୢ Where Ht represents the height and Wd represents the width of the image I୬ and x represents the pixel value at the given location. The MPECS adopts a 6 phase approach to extract the parameters set P ‫ ׊‬i ‫ א‬Iୗ୏ . The system considers 21 parameters for classification i.e. a ൌ 21. Each phase provides towards the construction of the parameter set P.The ‫ ܵܥܧܲܯ‬relies on the supervised machine learning with back propagation [24] training is adopted for classification. The trainings set ܶோ is algorithm for classification. The use of multi-layer feed forward neural networks (‫)ܰܰܨܯ‬ defined as ܶோ ൌ ሼ்ܴோଵ , ்ܴோଶ , ்ܴோଷ , … … . ்ܴோ௥ ሽ Where ்ܴோ௥ represents the ‫ ݎ‬௧௛ training record. c from the training image. I.e. ்ܴோ௥ ൌ ሼI୰ ‫ ׫‬c௥ ሽwhere I୰ represents the parameter set that The training record is constructed from the parameter set and the corresponding class defines the ‫ ݎ‬௧௛ training image and c௥ represents the corresponding classification class. Similarly the test set ்ܶ is defined as ்ܶ ൌ ሼ்்ܴଵ , ்்ܴଶ , ்ܴோଷ , … … . ்்ܴ௧ ሽ The objective of the ‫ ܵܥܧܲܯ‬is to identify c௧ | ்்ܴ௧ ‫ א‬c௧ ܽ݊݀ c௧ ‫ܥ א‬ 3.2 OPERATION OF THE ࡹࡼࡱ࡯ࡿ The ‫ ܵܥܧܲܯ‬relies on supervised learning techniques for classification. To impart intelligence the ‫ ܵܥܧܲܯ‬is trained using the pre-classified dermoscopic images. The training operation of the ‫ ܵܥܧܲܯ‬is described by the algorithm given below where ‫்ܰܰܨܯ‬ோ ሺܺሻrepresents the back propagation training function based on the inputs set ܺ. The parameters extracted from the phase ‫ ݌‬is represented by ‫்݌‬ோ௜ . Algorithm Name:Training Phase of the ‫ܵܥܧܲܯ‬ 1. ‫ݎ‬training dermoscopic images Input: 2. classification set C ൌ ሼcଵ , cଶ , cଷ … c୰ ሽ 1. Trained ‫ ܰܰܨܯ‬classifier. Output: 19
  • 5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME Algorithm Step 2: ܶோ ൌ ‫׎‬ Step 1: Training Phase Begin Step 3: For all training images ݅ ‫ݎ ݋ݐ 1 ׷‬ ்ܴோ௜ ൌ ‫׎‬ Step 5: ࡹࡼࡱ࡯ࡿ Parameter Extraction Begin Step 4: Parameter Set P்ோ௜ ൌ ‫׎‬ For phases ࢖ ‫ ׷‬૚: ૟ Step 6: P்ோ௜ ൌ P்ோ௜ ‫்݌ ׫‬ோ௜ Step 7: Step 8: Step 9: End phase Step 11:்ܴோ௜ ൌ P்ோ௜ ‫ ׫‬c௜ Step 10: Parameter Extraction End Step 12: ܶோ ൌ ܶோ ‫்ܴ ׫‬ோ௜ Step 14: Perform Training ‫்ܰܰܨܯ‬ோ ሺܶோ ሻ Step 13: End For Step 15: End Training Phase The ‫ ܰܰܨܯ‬post training could be used for classification of the skin lesions represented in the testing dermoscopic image dataset. The testing phase of the ‫ ܵܥܧܲܯ‬is described in the algorithm given below where‫்்݌‬௜ represents the parameters extracted in the ‫݌‬௧௛ extraction phase and‫ ்்ܰܰܨܯ‬ሺܻሻis the classification function of ܻ defined as ‫ ்்ܰܰܨܯ‬ሺܻሻ ൌ ܿ௧ Where ܿ௧ ‫ܥ א‬ Algorithm Name: Testing Phase of the ‫ܵܥܧܲܯ‬ 1. ‫ݐ‬testing dermoscopic images Input: 2. Trained ‫்ܰܰܨܯ‬ோ ሺܶோ ሻ 1. Classification Output ܿ௧ ‫ܥ א‬ Output: Algorithm Step 2:்ܶ ൌ ‫׎‬ Step 1: Testing Phase Begin Step 3: For all testing images ݅ ‫ݐ ݋ݐ 1 ׷‬ ்்ܴ௜ ൌ ‫׎‬ Step 5: ࡹࡼࡱ࡯ࡿ Parameter Extraction Begin Step 4: Parameter Set P்்௜ ൌ ‫׎‬ For phases ࢖ ‫ ׷‬૚: ૟ Step 6: P்்௜ ൌ P்்௜ ‫்்݌ ׫‬௜ Step 7: Step 8: Step 9: End phase Step 11:்்ܴ௜ ൌ P்்௜ Step 10: Parameter Extraction End Step 12: ்ܶ ൌ ்ܶ ‫்்ܴ ׫‬௜ Step 14: Perform Classification Query‫ ்்ܰܰܨܯ‬ሺ்ܶ ሻ ൌ ܿ௧ Step 13: End For Step 15: End Testing Phase 20
  • 6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME The training and testing phases of the ‫ ܵܥܧܲܯ‬have been discussed above which explains the operation. Parameter extraction is an integral part of the ‫ ܵܥܧܲܯ‬which defines ‫ ܵܥܧܲܯ‬is acheied using a 6 phase approach described in the subsequent section of this paper. the skin lesions and aids in training and classification. The 21 parameter extraction of the 3.3. PARAMETER EXTRACTION IN THE ࡹࡼࡱ࡯ࡿ The dermoscopic images of skin lesions is represented as a set of 21 parameters extracted in 6 phases described below [7] 3.3.1. ‫܁۱۳۾ۻ‬PARAMETER EXTRACTION – PHASE 1 present in an image ݅ ‫ܫ א‬and extracting the Lesion Borders, the symmetry of the lesions and This preliminary phase is primarily designed towards identifying the skin lesions the color spreading factor of the skin lesions. To extract the parameters discussed the images are resized using a bicubic pixels in the nearest ሾ 4 ൈ 4 ሿproximity and is defined as interpolation technique. The bicubic interpolation technique is an weighted average of the ଷ ଷ Pୖःऊ ሺx, yሻ ൌ ෍ ෍൫wt ୧୨ ൈ x୧ y ୨ ൯ ୧౤ ୧ୀଵ ୨ୀଵ Where ‫ݕ , ݔ‬represent the pixel position and wt ୧୨is the weight at position ݅, ݆ of the image i୬ ‫ א‬Iୗ୏ Grey scaling is performed on the resized pixels, and the resultant grey scale pixel is computed based on the following definition. Pୋ୰ୣ୷ୗୡୟ୪ୣ ୧౤ ሺx, yሻ ൌ ቂ0.2989 ൈ R ቀPୖःऊ ୧౤ ሺx, yሻቁቃ ൅ ቂ0.5870 ൈ G ቀPୖःऊ ୧౤ ሺx, yሻቁቃ ൅ ቂ0.11400 ൈ B ቀPୖःऊ ୧౤ ሺx, yሻቁቃ Where R ቀPୖःऊ ୧౤ ሺx, yሻቁrepresented the red channel value, G ቀPୖःऊ ୧౤ ሺx, yሻቁ represents the green channel value and B ቀPୖःऊ ୧౤ ሺx, yሻቁrepresents the blue channel value of the resized pixelPୖःऊ ୧౤ ሺx, yሻ. Binirazation is performed on the grayscale image utilizing the adaptive thresholding algorithm. The image noise elimination property of the adaptive thresholding based binirazation algorithm is an additional parameter for adopting this algorithm. The adaptive thresholding based binirazation is an iterative process wherein the thresholds are adapted convergence is achieved. Let P୆୧୬ ୧౤ ሺx, yሻ represent the pixels of the binirized image i୬ . The based on the regions it is being performed on. The iterative process is terminated when regions of interest ሺ‫݅݋ݎ‬ሻ is extracted using the connected component labeling algorithm. The ‫ ݏ’݅݋ݎ‬obtained hold the information required and are identified by labels Lୖ୓୍ି୪ ୧౤ . The image 21
  • 7. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME i୬ ‫ א‬Iୗ୏ based on the ܴܱ‫ ݏ’ܫ‬is defined as i୬ ି୰୭୧ ൌ ሼL୰୭୧ିଵ ୧౤ ‫ ׫‬L୰୭୧ିଶ ୧౤ ‫ ׫‬L୰୭୧ିଷ ୧౤ ‫ ׫ ڮ ׫‬L୰୭୧ି୪ ୧౤ ሽ Where ݈represents the total number of ‫ ݏ’݅݋ݎ‬extracted. The centroids of the ‫ ݏ’݅݋ݎ‬are computed based on the area enclosed by the ‫ .ݏ’݅݋ݎ‬The centroids are computed to cluster the similar roi’s together in the same vicinity as it is observed that the roi’s are similar in nature and can be clustered not effecting the classification accuracy. This operation adopted enables roi’s exhibiting similar properties to be clustered into a single cluster together represented asܴܱ‫ .ܫ‬The labels L୰୭୧ି୪ ୧౤ used to address the roi’s are clustered based on the energy computed and is defined as ࣟgyୖ୓୍ି୪ ൫Lୖ୓୍ି୪ ୧౤ ൯ ൌ ෍ ቎ ෍ ∆൫L୰୭୧ିୟ ୧౤ , L୰୭୧ିୠ ୧౤ ൯቏ ୮୭ୱ ୪ ‫א‬ୗ౨౥౟ Where ∆൫L୰୭୧ିୟ ୧౤ , L୰୭୧ିୠ ୧౤ ൯represents a function ∆൫L୰୭୧ିୟ ୧౤ , L୰୭୧ିୠ ୧౤ ൯ ൌ ሼ0,1ሽ i.e. if the roi’s L୰୭୧ିୟ ୧౤ and L୰୭୧ିୠ ୧౤ are similar, 0 is returned and 1 in the case of dissimilarity. S୰୭୧ ൌ ሼ1,2,3, … . lሽrepresents the set of roi’s obtained and a ‫ א‬S୰୭୧and b ‫ א‬S୰୭୧. pos represents the position of the l୲୦ clustered ROI represented as Lୖ୓୍ି୪ ୧౤ . The clustered ROI’s obtained define the image i୬ as i୬ ିୖ୓୍ ൌ ሼLୖ୓୍ିଵ ୧౤ ‫ ׫‬Lୖ୓୍ିଶ ୧౤ ‫ ׫‬Lୖ୓୍ିଷ ୧౤ ‫ ׫ ڮ ׫‬Lୖ୓୍ି୪ ୧౤ ሽ Sobel edge detection is performed on the image i୬ ୖ୓୍. Post the edge detection the distortion on the basis of the coordinate displacement and the frequency of distortion observed is computed using the Fast Fourier Transforms. The axis based asymmetry parameter of the lesion is obtained based on the principal component decomposition. The skin lesion extracted is rotated such that the principal component coincides with the horizontal p୅୶୧ୱ_ୗ୷୫୫ୣ୲୰୷ ൌ ቂ෍|i୬ ሺxሻ െ ıഥ ሺxሻ|ቃൗቂ෍ i୬ ሺxሻቃ axis. The skin lesion axis based asymmetry parameter is defined as ୬ Where i୬ ሺxሻthe original skin lesion and its reflected version is represented as ıഥ ሺxሻ. ୬ The symmetry parameter is computed for all the pixels within the lesion along the based on the similarity of a pixel in a n ൈ n neighbourhood. The standard deviation of the horizontal and vertical axis respectively. The color spreading factor of the lesions is computed pixels in the neighborhood based on the Red channel, Green channel and Blue channel is computed using 1 ୬మ തതതതതത ൌ ඩ ଶ ෍ሺChnl୧ െ Chnlሻଶ n σେ୦୬୪ ୧ୀଵ തതതതതത Where Chnl ൌ ሼR େ୦୬୪ , Gେ୦୬୪ , Bେ୦୬୪ ሽ and Chnlrepresents the mean value defined as 22
  • 8. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME 1 ୬మ തതതതത Chn ൌ ଶ ෍ Chn୧ n ୧ୀଵ σେ୳୫୧୪୧୲୧୴ୣ ൌ σୖి౞౤ౢ ൅ σୋి౞౤ౢ ൅ σ୆ి౞౤ౢ The cumulative standard deviation for all the channels is defined as ‫ۍ‬ ‫ۍ ې‬ ‫ې‬ 1 1 ୬మ ୬మ ൌ ‫ێ‬ඩ ଶ ෍ሺR େ୦୬୪ ୧ െ R େ୦୬୪ ሻଶ ‫ ۑ‬൅ ‫ێ‬ඩ ଶ ෍ሺGେ୦୬୪ ୧ െ Gେ୦୬୪ ሻଶ ‫ۑ‬ തതതതതതത തതതതതതത ‫ ێ‬n ୧ୀଵ ‫ ێ ۑ‬n ୧ୀଵ ‫ۑ‬ σେ୳୫୧୪୧୲୧୴ୣ ‫ۏ‬ ‫ۏ ے‬ ‫ے‬ ‫ۍ‬ ‫ې‬ 1 ୬మ ൅ ‫ێ‬ඩ ෍ሺBେ୦୬୪ െ Bେ୦୬୪ ሻଶ ‫ۑ‬ തതതതതതത ‫ ێ‬nଶ ୧ୀଵ ୧ ‫ۑ‬ ‫ۏ‬ ‫ے‬ The color spreading factor of the lesion is computed using pେ୪୰ିୗ୮୰ୣୟୢ ൌ 1 െ σ୒୭୰୫ Where σ୒୭୰୫is the normalized value of σେ୳୫୧୪୧୲୧୴ୣ between the range 0 and 1 and is obtained σ୒୭୰୫ ൌ σେ୳୫୧୪୧୲୧୴ୣ ⁄σେ୳୫୧୪୧୲୧୴ୣି୑ୟ୶ as In order to extract the lesion border parameters the image is filtered using the Gaussian and laplacian filter. The parameter describing the lesion borders is obtained by computing the mean of the resultant pixels and is defined as ୛ୢ ୌ୲ p୐ୣୱ୧୭୬ି୆୭୳୬ୢ୰୷ ൌ ቎෍ ෍ P୧౤ ሺx, yሻ቏൘ሾWd ൈ Htሿ ୶ୀଵ ୷ୀଵ In this phase the parameters of the lesions such as the asymmetry along the horizontal and vertical axis, the color spreading factor of the lesion and the boundary parameters is discussed. 3.3.2. ‫ ܁۱۳۾ۻ‬PARAMETER EXTRACTION – PHASE 2 The second phase of the ‫ ܵܥܧܲܯ‬enables the extraction of the area, perimeter and the eccentricity. This phase considers the binirized and clustered ܴܱ‫ ܫ‬image as an input and ݅௡ ିோைூ ൌ ሼ‫ܮ‬ோைூିଵ ௜೙ ‫ܮ ׫‬ோைூିଶ ௜೙ ‫ܮ ׫‬ோைூିଷ ௜೙ ‫ܮ ׫ ڮ ׫‬ோைூି௟ ௜೙ ሽ computes the parameters based on the binirized image obtained from phase 1 defined as Where ݈ represents the total number of ܴܱ‫ ݏ’ܫ‬identified for the image ݅௡ . The ܴܱ‫ ݏ’ܫ‬of image ݅௡ ିோைூ contain binirized pixel points i.e. black or white pixels. The area parameter defines the white pixels that are encapsulated by the skin lesion and the lesion area parameter is computed using 23
  • 9. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME ௉௡௧௦ିଵ ‫݌‬௅௘௦௜௢௡ି஺௥௘௔ ൌ ሺ1⁄2ሻ ቎ | ෍ ൫‫ݔ‬௜ ‫ݕ‬௝ାଵ െ ‫ݔ‬௜ାଵ ‫ݕ‬௝ ൯ | ቏ ௜,௝ୀ଴ Where ܲ݊‫ ݏݐ‬is the number of points enclosed by the skin lesion defined by the ݈ ௧௛ ܴܱ‫ . ܫ‬Also ݅, ݆ represent the ܲ݊‫ݏݐ‬location and ൫‫ݔ‬௜ , ‫ݕ‬௝ ൯ ‫ א‬൛൫‫ݔ‬௜ , ‫ݕ‬௝ ൯ห ݂൫‫ݔ‬௜ , ‫ݕ‬௝ ൯ ൌ 1 ሽ as ‫ݔ‬௜ , ‫ݕ‬௝ represents a The perimeter defines the boundary ߂‫ ܤ‬length of the skin lesion. The lesion boundary white pixel. ߂‫ܤ‬ሺ݈ሻ ൌ ሺ‫ܤ‬ሺ2: ݈, : ሻ െ ‫ܤ‬ሺ1: ݈ െ 1, : ሻሻଶ is computed using Where ݈ represents the number of ܴܱ‫ ݏ’ܫ‬and its corresponding boundary is represented as ‫.ܤ‬ The parameter defining boundary of the skin lesion is defined as ‫݌‬௅௘௦௜௢௡ି஻௢௨௡ௗ ൌ ෍ ඨ෍ ߂‫ܤ‬ሺ݈ሻ ௟ eccentricity is obtained by obtaining the moments represented as ‫ .ܯ‬The major axis is The aspect ratio of the skin lesion is defined as the eccentricity of a skin lesion. The ‫ܣ‬ெ௔௝௢௥ ൌ 2√2ሺඥ‫ܯ‬௫௫ ൅ ‫ܯ‬௬௬ ൅ ‫ܯ‬஼௢௠௠ ሻ defined as ‫ܣ‬ெ௜௡௢௥ ൌ 2√2ሺඥ‫ܯ‬௫௫ ൅ ‫ܯ‬௬௬ െ ‫ܯ‬஼௢௠௠ ሻ The minor axis is defined as Where the moments ‫ܯ‬௫௫ , ‫ܯ‬௬௬ , ‫ܯ‬௫௬ and‫ܯ‬஼௢௠௠ are defined as ‫ܯ‬௫௫ ൌ ቂቀ෍ ‫ ݔ‬ଶ ቁൗܰ ൅ 1⁄12ቃ ‫ܯ‬௬௬ ൌ ቂቀ෍ ‫ ݕ‬ଶ ቁൗܰ ൅ 1⁄12ቃ ‫ܯ‬௫௬ ൌ ቂቀ෍ ‫ݕ כ ݔ‬ቁൗܰቃ And ‫ܯ‬஼௢௠௠ ൌ ටሺ‫ܯ‬௫௫ െ ‫ܯ‬௬௬ ሻଶ ൅ 4‫ܯ‬௫௬ ଶ The eccentricity parameter is computed using ‫݌‬௅௘௦௜௢௡ିா௖௖௘௡௧ ൌ ቈට൫‫ܣ‬ெ௔௝௢௥ ଶ െ ‫ܣ‬ெ௜௡௢௥ ଶ ൯቉ൗ‫ܣ‬ெ௔௝௢௥ మ 3.3.3. ‫ ܁۱۳۾ۻ‬PARAMETER EXTRACTION – PHASE 3 Researches have well understood the importance to 3‫ ܦ‬depth parameters to enhance the classification accuracy [6] [19]. To obtain the 3‫ ܦ‬depth of the skin lesions the ‫ ܵܥܧܲܯ‬introduced in this paper considers creating a 3 ൈ 3 masked windows represented as ܹଵ ܹଶ ܹଷ ݅௡ ଷൈଷିெ௔௦௞ ൌ ൥ܹସ ܹହ ܹ଺ ൩ ܹ଻ ଼ܹ ܹଽ Where ܹ௜ represent the weights of the pixel The projection filter is defined as 24
  • 10. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME ଽ ݅௡ ଷ஽ି஽௘௣௧௛ ൌ ෍ ܹ௜ ‫ܫ‬௜ ௜ୀଵ Where ‫ܫ‬௜ represents the intensity of the ݅ ௧௛ pixel. The 3D depth projection parameter ‫݌‬௅௘௦௜௢௡ିଷ஽஽௘௣௧ is obtained by considering the geometric ‫݌‬௅௘௦௜௢௡ିଷ஽஽௘௣௧ mean defined as ൌ ሾ1⁄9ሿ ൈ ൣ‫ܫ‬௫ିଵ,௬ିଵ ൅ ‫ܫ‬௫ିଵ,௬ଵ ൅ ‫ܫ‬௫ିଵ,௬ାଵ ൅ ‫ܫ‬௫,௬ିଵ ൅ ‫ܫ‬௫,௬ ൅ ‫ܫ‬௫,௬ାଵ ൅ ‫ܫ‬௫ାଵ,௬ିଵ ൅ ‫ܫ‬௫ାଵ,௬ଵ ൅ ‫ܫ‬௫ାଵ,௬ାଵ ሿ Where ‫ ݕ ,ݔ‬represent the horizontal and vertical pixel position. 3.3.4. ‫ ܁۱۳۾ۻ‬PARAMETER EXTRACTION – PHASE 4 The fourth phase of the ‫ܵܥܧܲܯ‬is targeted towards obtaining the color components of the skin lesions. The color components are extracted for the red channel blue channel and the green channel. The mean and the variance parameters of each channel are considered as the parameters to be utilized for classification. For the red channel the mean parameter is defined ∑ௐௗ ∑ு௧ ܴ஼௛௡௟ ሺ‫ݕ ,ݔ‬ሻ as follows ‫݌‬ோ஼௛௡௟ିெ௘௔௡ ൌ ௫ୀଵ ௬ୀଵ ܹ݀ ‫ݐܪ כ‬ Where ‫ ݕ ,ݔ‬represent the pixel positions. The variance parameter is computed by obtaining the mean of all the ‫ܤ‬஼௛௡௟ columns , the difference amongst them and then summed square difference divided by the height ‫ݐܪ‬ ∑ௐௗ ܴሺ݅ሻ ‫݊ܽ݁݉_݈݉ܥ‬ோ஼௛௡௟ ሺ݅ሻ ൌ ௜ୀଵ ܹ݀ ‫݂݂݅ܦ‬ோ஼௛௡௟ ሺ݅ ሻ ൌ ܴ‫݄݊ܥ‬ሺ: , ݅ ሻ െ ‫݊ܽ݁݉_݈݉ܥ‬ோ஼௛௡௟ ሺ݅ሻ ∑௜ ‫݂݂݅ܦ‬ோ஼௛௡௟ ሺ݅ሻ ‫݌‬ோ஼௛௡௟ି௏௔௥ ൌ ሺ‫ ݐܪ‬െ 1ሻ ∑ௐௗ ∑ு௧ ‫ܩ‬஼௛௡௟ ሺ‫ݕ ,ݔ‬ሻ Similarly the green channel and blue channel parameters are defined as ‫ீ݌‬಴೓೙೗௟ିெ௘௔௡ ൌ ௫ୀଵ ௬ୀଵ ܹ݀ ‫ݐܪ כ‬ ∑௜ ‫ீ݂݂݅ܦ‬಴೓೙೗ ሺ݅ ሻ ‫ீ݌‬಴೓೙೗ି௏௔௥ ൌ ሺ‫ ݐܪ‬െ 1ሻ ∑ௐௗ ∑ு௧ ‫ܤ‬஼௛௡௟ ሺ‫ݕ ,ݔ‬ሻ ‫݌‬஻಴೓೙೗ିெ௘௔௡ ൌ ௫ୀଵ ௬ୀଵ ܹ݀ ‫ݐܪ כ‬ ∑௜ ‫݂݂݅ܦ‬஻಴೓೙೗ ሺ݅ሻ ‫݌‬஻಴೓೙೗ି௏௔௥ ൌ ሺ‫ ݐܪ‬െ 1ሻ 25
  • 11. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME 3.3.5. ‫ ܁۱۳۾ۻ‬PARAMETER EXTRACTION – PHASE 5 This phase of the ‫ ܵܥܧܲܯ‬discusses the smoothening process of the ܴ‫ ݈݄݊ܥ‬and ‫ܩ‬஼௛௡௟ using the 3 ൈ 3 masking procedure discussed in the third phase. The smoothing is not adopted for the ‫ܤ‬஼௛௡௟ as the blue veils of the skin lesions is an important parameter for early detection of skin cancer melanoma [20]. The smoothening filter for the ܴ‫ ݈݄݊ܥ‬and ‫ܩ‬஼௛௡௟ is ଽ defined as follows ݅௡ ோ஼௛௡௟ିௌ௠௢௢௧௛ ൌ ෍ ܹ௜ ܴ‫݈݄݊ܥ‬௜ ௜ୀଵ ଽ ݅௡ ீ ൌ ෍ ܹ௜ ‫ܩ‬஼௛௡௟ ௜ ಴೓೙೗ ିௌ௠௢௢௧௛ ௜ୀଵ Where ܹ௜ represent the weights of the pixel and ܴ‫݈݄݊ܥ‬௜ , ‫ܩ‬஼௛௡௟ ௜ is red channel intensity and green channel intensity of the ݅௧௛ pixel. The red channel smoothened parameter ‫݌‬ோ஼௛௡௟ିௌ௠௢௢௧௛ is defined as ‫݌‬ோ஼௛௡௟ିௌ௠௢௢௧௛ ൌ ሾ1⁄9ሿ ൈ ൣܴ‫݈݄݊ܥ‬௫ିଵ,௬ିଵ ൅ ܴ‫݈݄݊ܥ‬௫ିଵ,௬ଵ ൅ ܴ‫݈݄݊ܥ‬௫ିଵ,௬ାଵ ൅ ܴ‫݈݄݊ܥ‬௫,௬ିଵ ൅ ܴ‫݈݄݊ܥ‬௫,௬ ൅ ܴ‫݈݄݊ܥ‬௫,௬ାଵ ൅ ܴ‫݈݄݊ܥ‬௫ାଵ,௬ିଵ ൅ ܴ‫݈݄݊ܥ‬௫ାଵ,௬ଵ ൅ ܴ‫݈݄݊ܥ‬௫ାଵ,௬ାଵ ሿ The green channel smoothened parameter ‫ீ݌‬಴೓೙೗ିௌ௠௢௢௧௛ ‫ீ݌‬಴೓೙೗ିௌ௠௢௢௧௛ ൌ ሾ1⁄9ሿ ൈ ቂ‫ܩ‬஼௛௡௟ ௫ିଵ,௬ିଵ ൅ ‫ܩ‬஼௛௡௟ ௫ିଵ,௬ଵ ൅ ‫ܩ‬஼௛௡௟ ௫ିଵ,௬ାଵ ൅ ‫ܩ‬஼௛௡௟ ௫,௬ିଵ ൅ ‫ܩ‬஼௛௡௟ ௫,௬ ൅ ‫ܩ‬஼௛௡௟ ௫,௬ାଵ ൅ ‫ܩ‬஼௛௡௟ ௫ାଵ,௬ିଵ ൅ ‫ܩ‬஼௛௡௟ ௫ାଵ,௬ଵ ൅ ‫ܩ‬஼௛௡௟ ௫ାଵ,௬ାଵ ൧ 3.3.6. ‫ ܁۱۳۾ۻ‬PARAMETER EXTRACTION – PHASE 6 components of the skin lesion to obtain accurate classification results [21][22]. The ‫ܵܥܧܲܯ‬ For early detection of skin cancer melanoma it is essential to extract all the color discussed considers the cylindrical coordinate representation of pixels in the fourth and fifth ‫ ܵܥܧܲܯ‬considers the Cartesian representation of pixels. Hue, Saturation and Value phase. In order to extract accurate and elaborate color parameters this phase of the representation of the pixels are considered as the Cartesian representations. The mean and from the ܴ஼௛௡௟ , ‫ܩ‬஼௛௡௟ ܽ݊݀‫ܤ‬஼௛௡௟ pixel values of the skin lesions. The hue value of a pixel is variance parameters of the hue channel, variance channel and the value channel are extracted 43 ‫ܩ| כ‬஼௛௡௟ െ ‫ܤ‬஼௛௡௟ | computed using the following definition ‫ۓ‬ቈ0 ൅ ቉ , ‫ ݈ܸܽݔܽܯ‬ൌ ܴ‫݈݄݊ܥ‬ ۖ ‫ ݈ܸܽݔܽܯ‬െ ‫݈ܸܽ݊݅ܯ‬ ۖ 43 ‫ܤ| כ‬஼௛௡௟ െ ܴ‫|݈݄݊ܥ‬ ‫݁ݑܪ‬௜,௝ ൌ ቈ85 ൅ ቉ , ‫ ݈ܸܽݔܽܯ‬ൌ ‫ܩ‬஼௛௡௟ ‫۔‬ ‫ ݈ܸܽݔܽܯ‬െ ‫݈ܸܽ݊݅ܯ‬ ۖቈ171 ൅ 43 ‫ ݈݄݊ܥܴ| כ‬െ ‫ܩ‬஼௛௡௟ |቉ ۖ , ‫ ݈ܸܽݔܽܯ‬ൌ ‫ܤ‬஼௛௡௟ ‫ە‬ ‫ ݈ܸܽݔܽܯ‬െ ‫݈ܸܽ݊݅ܯ‬ Where ‫ ݈ܸܽݔܽܯ‬ൌ ‫ݔܽܯ‬ሺܴ‫ܩ ,݈݄݊ܥ‬஼௛௡௟ , ‫ܤ‬஼௛௡௟ ሻ is the maximum value of the red green and blue channel of the pixel 26
  • 12. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME ‫ ݈ܸܽ݊݅ܯ‬ൌ ‫݊݅ܯ‬ሺܴ‫ܩ ,݈݄݊ܥ‬஼௛௡௟ , ‫ܤ‬஼௛௡௟ ሻis the minimum value of the red green and blue channel of the pixel ሼ‫ ݈ܸܽݔܽܯ‬െ ‫݈ܸܽ݊݅ܯ‬ሽ The Saturation and the value parameter of the pixel is computed using ܵܽ‫݊݋݅ݐܽݎݑݐ‬௜,௝ ൌ 255 ‫כ‬ ‫݈ܸܽݔܽܯ‬ ܸ݈ܽ‫݁ݑ‬௜,௝ ൌ ‫݈ܸܽݔܽܯ‬ ∑ௐௗ ∑ு௧ ‫݁ݑܪ‬஼௛௡௟ ሺ‫ݕ ,ݔ‬ሻ The mean of the hue channel and the is defined as ‫݌‬ு௨௘಴೓೙೗ ௟ିெ௘௔௡ ൌ ௫ୀଵ ௬ୀଵ ܹ݀ ‫ݐܪ כ‬ The variance is computed in a manner similar to the procedure described in phase 4 and is ∑௜ ‫݂݂݅ܦ‬ு௨௘಴೓೙೗ ሺ݅ ሻ defined as ‫݌‬ு௨௘಴೓೙೗ ି௏௔௥ ൌ ሺ‫ ݐܪ‬െ 1ሻ ∑ௐௗ ∑ு௧ ܵܽ‫݊݋݅ݐܽݎݑݐ‬஼௛௡௟ ሺ‫ݕ ,ݔ‬ሻ The mean and the variance of the saturation channel is defined as ‫݌‬ௌ௔௧௨௥௔௧௜௢௡಴೓೙೗ ିெ௘௔௡ ൌ ௫ୀଵ ௬ୀଵ ܹ݀ ‫ݐܪ כ‬ ∑௜ ‫݂݂݅ܦ‬ௌ௔௧௨௥௔௧௜௢௡಴೓೙೗ ሺ݅ሻ ‫݌‬ௌ௔௧௨௥௔௧௜௢௡಴೓೙೗ ି௏௔௥ ൌ ሺ‫ ݐܪ‬െ 1ሻ ∑ௐௗ ∑ு௧ ܸ݈ܽ‫݁ݑ‬஼௛௡௟ ሺ‫ݕ ,ݔ‬ሻ Accordingly the Value channel parameters are obtained using the following equations ‫݌‬௏௔௟௨௘಴೓೙೗ ିெ௘௔௡ ൌ ௫ୀଵ ௬ୀଵ ܹ݀ ‫ݐܪ כ‬ ∑௜ ‫݂݂݅ܦ‬௏௔௟௨௘಴೓೙೗ ሺ݅ ሻ ‫݌‬௏௔௟௨௘಴೓೙೗ ି௏௔௥ ൌ ሺ‫ ݐܪ‬െ 1ሻ The ‫ ܵܥܧܲܯ‬discussed in this paper discusses the extraction 21 vital parameters which would enable for classification of skin lesions especially targeted towards early detection of skin cancer melanoma. The parameter extraction and the procedure involved in extraction are achieved adopting a six phase approach discussed above. 3.4. ࡹࡲࡺࡺ CLASSIFIER Figure 1 : A MFNN Classifier Structure 27
  • 13. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME The classification of the testing data set of dermoscopic images requires a supervised training procedure to be incorporated. A multilayer feed forward neural network (‫)ܰܰܨܯ‬ with the back propagation algorithm for training is adopted for the purpose of classification. of the neural network. A sample ‫ ܰܰܨܯ‬is shown in Fig 1. The ‫ ܰܰܨܯ‬consists of a set of The parameters extracted from the testing image set are directly provided to the input neurons input neurons ,‫ ܯ‬number of hidden layers and an output layer.The ‫ ܰܰܨܯ‬classifier is a machine learning algorithm. The learning and classification is achieved based on the layer represented as ܽఊ where ߛ ൌ 1,2,3, … c୰ and c୰ is the trained ‫ ݎ‬classes. weightsࣱ which are changed in the neighboring layers. The input to the neuron in every ࣿࣾషభ The output of the neuron is defined as ܽࣼ ൌ ݂൫߮ࣼ ൯ ൌ ݂ ቌ ෍ ࣱࣼࣻ ܽࣻ ሺࣾିଵሻఊ ቍ ࣾఊ ࣾఊ ࣾ ࣻୀ଴ ݉represents the index of the hidden layer i.e. 1 ൑ ݉ ൑ ‫ܯ‬ Where ݊௠ represents the number of neurons in݉௧௛ layer ݆ represents the index ݆ | 1 ൑ ݆ ൑ ݊௠ ߮ ࣼ represents the weighted sum of input values for ࣼ ௧௛ neuron in layer ܽ ࣱࣼ௜ represents the weight between ࣼ ௧௛ neuron in ݉௧௛ layer and ݅ ௧௛ neuron in ሺ݉ െ 1ሻ௧௛ layer γ ௠ The classification error ߜࣼ ം observed at the output layer is obtained from the following ࣧ definition ܽࣼ ൌ ݂ ′ ൫߮ࣼ ൯ߜࣼ ൌ ݂ ′ ൫߮ࣼ ൯൫‫ࣼݕ‬ െ ‫ ࣼݕ‬൯ ࣾఊ ெఊ ఊ ெఊ ࣴఊ ఊ The sum of square errors observed through the ‫ ܰܰܨܯ‬is defined as 1 ௔ ߦఊ ൌ ෍൫ߜ௝ ൯ ఊ ଶ 2 ࣼୀଵ ௔ࣾశభ The error existent at the output layers is propagated back and is defined as ൌ݂ ൫߮ࣼ ൯ ෍ ߜℓ ሺࣾାଵሻఊ ሺࣾାଵሻ ߜࣼ ࣱℓࣼ ࣾఊ ᇱ ࣾఊ ℓୀଵ The errors propagated is minimized by altering the weights of the neighboring neurons and ∆ஓ ࣱ௝௜ ൌ ߟߜࣼ ‫ࣻݑ‬ ௠ ࣾఊ ሺࣾିଵሻఊ the weight update function is defined as ୡ౨ ୡ౨ 1 ௔ The classification error that exist through ଶ ߦ஼௟௔௦௦ ൌ ෍ ߦఓ ൌ ෍ ෍ቀ‫ ࣼݕ‬െ ߮ ࣼ ቁ ୸ஓ ஓ 2 ఓୀଵ ఓୀଵ ࣼୀଵ The ‫ ܰܰܨܯ‬Classifier is designed to minimize the classification error by effective weight adjustments achieved due to the back propagation algorithm. 28
  • 14. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME The ‫ ܵܥܧܲܯ‬adopts machine learning techniques for classification. The ‫ ܰܰܨܯ‬based classifier is adopted for classification which classifies based on the supervised learning imparted using the back propagation algorithm. Parameter extraction is achieved using a six phase approach to define the skin lesion. The training dataset constitutes of a set of dermoscopic images which are pre-classified with the aid of dermatologists. The images set. The ‫ ܰܰܨܯ‬classifier is trained using the training data set. The testing set consists of defined in terms of the parameters and the classified type constitute towards the training data dermoscopic images that need to be diagnosed. The diagnostic results are represented as using the ‫ ܵܥܧܲܯ‬parameter extraction phases. The resulting dataset is termed as the test classes. To enable diagnosis the test images are represented as a set of parameters extracted dataset. The test dataset is provided to the trained ‫ ܰܰܨܯ‬for classification. The subsequent ܸܵ‫ ܯ‬classifiers. section of this paper discusses the experimental verification and comparisons with the 4. EXPERIMENTAL STUDY AND COMPARISONS The ‫ ܵܥܧܲܯ‬discussed in this paper was realized on the MATLAB platform. In order to evaluate the ‫ ܵܥܧܲܯ‬training and testing dataset are to be created. To construct the training and testing datasets dermoscopic images are obtained from the “The Atlas of Dermoscopy” [25]. The atlas considered is a collection of over 2000 skin lesion images obtained from a reference to evaluate the classification accuracy of the ‫ ܵܥܧܲܯ‬system proposed. The use of various patients. The atlas also provides the classification and diagnosis data which is used as the atlas is charted as others researchers too have used the same atlas to evaluate their ‫ ܵܥܧܲܯ‬proposed is compared with the popular ܸܵ‫ ܯ‬classifiers as proposed by Xiaojing proposed diagnosis mechanisms especially skin cancer melanoma [20] [26]. The Yuan et al.[18]. CLASSIFICATION ERROR 14 13.33 % 12 CLASSIFICATION ERROR IN % 10 8 6 4 2 < 1% 0 MEPCS - CLASSIFICATION ERROR SVM CLASSIFIER - CLASSIFICATION ERROR Figure 2 : Classification Error Observed 29
  • 15. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME The training dataset consists of pre-classified dermoscopic images. We have considered a set of 45 dermoscopic images from the “Atlas of Dermoscopy” as the training data set. The training dataset consist of skin lesion images of primarily 3 classes namely “Advanced Melanoma”, “Early Melanoma” and “Non-Melanoma”. The 21 parameters extracted per image using the 6 phase approach contribute to each record of the training data set along with The ‫ ܵܥܧܲܯ‬and the ܸܵ‫ ܯ‬Classifier was trained using similar training datasets. Similar test the classified name. A similar approach is followed to construct the testing dataset. data sets were evaluated used the ‫ ܵܥܧܲܯ‬and ܸܵ‫ ܯ‬classifier. The classification results obtained are provided to the Receiver Operating Characteristic (ܴܱ‫ܥ‬ሻ analysis tool. The error classification error of the ܸܵ‫ ܯ‬classifier is greater than the error exhibited by the proposed in classification obtained is graphically shown in Fig 2. From the figure it is evidentthat the ‫ ܵܥܧܲܯ‬system.The ‫ ܵܥܧܲܯ‬and the ܸܵ‫ ܯ‬classifier classifiers are compared on the basis of the receiver operating characteristic (ܴܱ‫ )ܥ‬analysis. The ܴܱ‫ ܥ‬analysis is carried out using a The ܴܱ‫ ܥ‬analysis plot obtained is shown in Fig 3. tool developed using C#.Net programming language on the Visual Studio 2010 platform. Receiver Operating Characteristic (ROC) - CURVE 1 0.9 0.8 0.7 SENSITIVITY 0.6 0.5 0.4 0.3 0.2 MPECS SVM CLASSIFIER 0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Figure 3 : ‫ ۱۽܀‬Curve Analysis 1- SPECIFICITY The classification accuracy and the efficiency of the ‫ ܵܥܧܲܯ‬Classifier along with the ܸܵ‫ ܯ‬Classifier are diagrammatically shown in Fig 4 and Fig 5. The ‫ ܵܥܧܲܯ‬systemexhibits an 81% accuracy in classification as compared to the 75% accuracy exhibited by the ܸܵ‫ܯ‬ Classifier. The ‫ ܵܥܧܲܯ‬system proposed in this paper improves the efficiency in classification by about 7.4% when compared to the ܸܵ‫ ܯ‬classifier.The experimental study the ‫ ܵܥܧܲܯ‬system proposed in this paper is capable of accurately classifying skin lesions conducted and from the results discussed in this section of the paper, it can be concluded that exhibiting early melanotic symptoms when compared to the popularly used ܸܵ‫ ܯ‬classifier systems. 30
  • 16. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME CLASSIFIER ACCURACY 0.81 0.8 0.79 0.78 ACCURACY 0.77 0.76 0.75 0.74 0.73 0.72 MEPCS ACCURACY SVM CLASSIFIER ACCURACY Figure 4 : Classification Accuracy Comparisons 0.83 CLASSIFIER EFFICIENCY 0.82 0.81 0.8 EFFECIENCY 0.79 0.78 0.77 0.76 0.75 0.74 0.73 MPECS EFFICIENCY SVM CLASSIFIER EFFICIENCY Figure 5: Classification Efficiency Comparisons 5. CONCLUSION This paper highlights the benefits and the requirement for early detection of skin cancer paper relies on a supervised ‫ ܰܰܨܯ‬based classifier to accurately classify skin lesions and enable melanoma. The Multi Parameter Extraction and Classification System (‫ )ܵܥܧܲܯ‬discussed in this early detection of skin cancer melanoma. The ‫ ܵܥܧܲܯ‬comprehensively defines skin lesions in terms of the ‫ ܵܥܧܲܯ‬is discussed in this paper. The ‫ ܵܥܧܲܯ‬system is trained using the Back Propagation of a parameter sets extracted using the six phase approach. The training and testing phase algorithms Algorithm. The trained classifier of the ‫ ܵܥܧܲܯ‬is used for analysis and diagnosis of skin lesions on unknown types. The classification preeminence of the ‫ ܵܥܧܲܯ‬is proved over the popular SVM classifier systems.The results obtained and discussed prove that the ‫ ܵܥܧܲܯ‬system proposed in this paper aids the detection of skin cancer melanoma in the naïve stages thereby improving computer aided diagnosis mechanisms and early treatment initiatives to be adopted by dermatologists for the benefit of patients suffering from the life threatening melanotic skin cancer diseases. 31
  • 17. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME REFERENCES [1] R. Marks, “Epidemiology of melanoma,” Clin. Exp. Dermatol., vol. 25, pp. 459–463, 2000 [2] World Health Organization, Ultraviolet Radiation and the INTERSUN Programme. (2007. Dec.). [Online]. Available: http://www.who.int/uv/faq/skincancer/en/ [3] Imran Ali, Waseem A. Wani and KishwarSaleem,"Cancer Scenario in India with Future Perspectives",Cancer Therapy Vol 8, 56-70, 2011 [4] SinhaR,Anderson DE, Mc Donald SS and Greenwald P, "Cancer Risk and Diet in India",J Postgrad Med 2003,49: 222-228 [5] American Cancer Society,"Cancer Facts and Figures 2012". 2012, American Cancer Society, Inc. pp 1-68 [6] Brian D’Alessandro, Atam P. Dhawan,"3D Volume Reconstruction of Skin Lesions for Melanin and Blood Volume Estimation and Lesion Severity Analysis",IEEE Transactions on Medical Imaging, July 2012 DOI : 10.1109/TMI.2012.2209434 [7] Ruksar Fatima, Mohammed Zafar Ali Khan, A. Govardhan and Kashyap D Dhruve."Computer Aided Multi-Parameter Extraction System to Aid Early Detection of Skin Cancer Melanoma ",IJCSNS International Journal of Computer Science and Network Security,Vol. 12 No. 10 pp. 74-86.2012 [8] G. Argenziano, H. P. Soyer, S. Chimenti, R. Talamini, R. Corona, F. Sera, M. Binder, L. Cerroni, G. De Rosa, G. Ferrara, R. Hofmann-Wellenhof,M. Landthaler, S.W.Menzies, H. Pehamberger, D. Piccolo, H. S. Rabinovitz, R. Schiffner, S. Staibano, W. Stolz, I. Bartenjev, A. lum, R. Braun, H. Cabo, P. Carli, V. De Giorgi, M. G. Fleming, J. M. Grichnik, C. M. Grin, A. C. Halpern, R. Johr, B. Katz, R. O. Kenet, H. Kittler, J. Kreusch, J. Malvehy, G. Mazzocchetti, M. Oliviero, F. Ozdemir, K. Peris, R. Perotti, A. Perusquia, M. A. Pizzichetta, S. Puig, B. Rao, P. Rubegni, T. Saida, M. Scalvenzi, S. Seidenari, I. Stanganelli, M. Tanaka, K. Westerhoff, I. H. Wolf, O. Braun-Falco, H. Kerl, T. Nishikawa, K. Wolff, and A. W. Kopf, “Dermoscopy of pigmented skin lesions: Results of a consensus meeting via the Internet,” J. Amer. Acad. Dermatol., vol. 48, no. 5, pp. 680–693, 2003. [9] V. T. Y. Ng, B. Y. M. Fung, and T. K. Lee, “Determining the asymmetry of skin lesion with fuzzy borders,” Comput. Biol. Med., vol. 35, pp. 103–120, 2005 [10] M. E. Celebi, Y. A. Aslandogan,W. V. Stoecker, H. Iyatomi, H. Oka, and X. Chen, “Unsupervised border detection in dermoscopy images,” Skin Res. Technol., vol. 13, no. 4, pp. 454–462, 2007 [11] S. E. Umbaugh, R. H. Moss, and W. V. Stoecker, “An automatic color segmentation algorithm with application to identification of skin tumor borders,” Comput. Med. Imag. Graph., vol. 16, pp. 227–235, 1992 [12] V. T. Y. Ng, B. Y. M. Fung, and T. K. Lee, “Determining the asymmetry of skin lesion with fuzzy borders,” Comput. Biol. Med., vol. 35, pp. 103–120, 2005 [13] G. Betta, G. Di Leo, G. Fabbrocini, A. Paolillo, and M. Scalvenzi, “Automated application of the “7-point checklist” diagnosis method for skin lesions: Estimation of chromatic and shape parameters,” in Proc. Instrum. Meas. Technol. Conf. (IMTC 2005), May, vol. 3, pp. 1818– 1822. [14] Tasoulis, S.K.; Doukas, C.N.; Maglogiannis, I.; Plagianakos, V.P.; , "Classification of dermatological images using advanced clustering techniques," Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE , vol., no., pp.6721-6724, Aug. 31 2010-Sept. 4 2010 doi: 10.1109/IEMBS.2010.5626242 32
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