Tıp alanında kanserli hücrelerin tespiti @ hasan abdi
1.
2. Presentation Contents
Introduction
Medical Imaging Technologies
The Role of Imaging in Cancer Care
Lung cancer Detection Approach
Work Performed and Results
Image Enhancement
Gabor Filter
Fast Fourier Transform
Image Segmentation
Thresholding approach
Features Extraction
3.
Image processing is one of most growing research area these
days and now it is very much integrated with the medical and
biotechnology field
Cancer is one of the most dangerous disease for which still
proper treatment is not available. World Health Organization
(WHO) mentioned that cancer accounted 13% of all death in
the world in 2004
Cancer is a tumor that grows larger than 2mm in every 3
months and multiplies out of control. It also spreads to other
parts of the body and destroys the healthy tissue.
4.
X-Rays (X-Işınları)
Mammography (Mamografi )
Ultrasound (Ultrason )
Computed Tomography (Bilgisayarlı Tomografi)
Magnetic Resonance Imaging (Manyetik Rezonans
Görüntüleme)
Nuclear Medicine (Planar and SPECT Gamma Imaging, PET)
(Nükleer Tıp (Planar ve SPECT Gama Görüntüleme, PET)
5. the role of medical imaging in cancer is
something of an anomaly
On the one hand, imaging plays a vital role in
detecting and treating virtually all types of
cancer
6. The roles are as follows : Imaging Detects Cancer Early
Imaging Enables Less-Invasive Cancer Diagnosis
and Treatment
Imaging Fosters More Effective Management of
Cancer
Imaging Fosters Efficiencies and Savings in Cancer
Care
Imaging Keeps Workers Productive
7. Lung cancer is the most dangerous and widespread cancer in
the world according to stage of discovery of the cancer cells in
the lungs
Lung cancer image processing stages as follows
görüntü yakalama
görüntü geliştirme
görüntü bölünme
Özelikler çıkarma
8. the aim of image enhancement is to improve the
interpretability or perception of information included in the
image for human viewers, or to provide better input for
other automated image processing techniques.
Image enhancement techniques can be divided into two
broad categories:
Spatial domain methods
and frequency domain methods.
9. Image enhancement stage we use the following
three techniques:
Gabor filter,
Auto-enhancement
Fast Fourier transform techniques
10. A Gabor filter is a linear filter whose impulse
response is defined by a harmonic function
multiplied by a Gaussian function.
The follwoıng Figure shows a) the original image
and (b) the enhanced image using Gabor Filter.
11. Fast Fourier Transform technique operates on
Fourier transform of a given image.
Fast Fourier Transform is used here in image
filtering (enhancement).
13. Image segmentation is an essential process for most
image analysis subsequent tasks
Segmentation divides the image into its constituent
regions or objects. Segmentation of medical images in
2D
The goal of segmentation is to simplify and/or change
the representation of the image into something that
is more meaningful and easier to analyse.
14. Thresholding is a non-linear operation that
converts a gray-scale image into a binary image
where the two levels are assigned to pixels that
are below or above the specified threshold value
15. Threshold values based on this method will be
between 0 and 1, after achieving the threshold
value; image will be segmented based on it. Figure
4 shows the result of applying thresholding
technique.
16. Image features Extraction stage is an important
stage that uses algorithms and techniques to
detect and isolate various desired portions or
shapes (features) of a given image.
To predict the probability of lung cancer
presence, the following two methods are used:
binarization and masking
17. Binarization approach depends on the fact that
the number of black pixels is much greater than
white pixels in normal lung images,
18. Masking approach depends on the fact that the
masses(means areas affected by cancer) are
appeared as white connected areas inside ROI
(lungs), as they increase the percent of cancer
presence increase.
The appearance of solid blue colour indicates
normal case while appearance of RGB masses
indicates the presence of cancer
19. Figure 8 shows normal and abnormal images
resulted by implementing Masking approach using
MATLAB
20. Combining Binarization and Masking approaches
together will lead us to take a decision whether
the case is normal or abnormal according to the
mentioned assumptions in the previous two
approaches.