Matlab / Projects / Project / Image processing

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MATLAB 2011
1. Face Recognition by Information jointly contained in image Image
Exploring space, scale and orientation domains can Processing
Information Jointly provide rich important clues not seen in
in Space, Scale and either individual of these domains. The
Orientation position, spatial frequency and orientation
selectivity properties are believed to have
an important role in visual perception. This
paper proposes a novel face representation
and recognition approach by exploring
information jointly in image space, scale
and orientation domains. Specifically, the
face image is first decomposed into
different scale and orientation responses by
convolving multiscale and multi-orientation
Gabor filters. Second, local binary pattern
analysis is used to describe the neighboring
relationship not only in image space, but
also in different scale and orientation
responses. This way, information from
different domains is explored to give a good
face representation for recognition.
Discriminant classification is then
performed based upon weighted histogram
intersection or conditional mutual
information with linear discriminant
analysis techniques. Extensive experimental
results on FERET, AR, and FRGC ver 2.0
databases show the significant advantages
of the proposed method over the existing
ones.
2. Detection of We present methods for the detection of Image
Architectural sites of architectural distortion in prior Processing
Distortion in Prior mammograms of interval-cancer cases. We
Mammograms hypothesize that screening mammograms
obtained prior to the detection of cancer
could contain subtle signs of early stages of
breast cancer, in particular, architectural
distortion. The methods are based upon
Gabor filters, phase portrait analysis, a
novel method for the analysis of the angular



spread of power, fractal analysis, Laws'
texture energy measures derived from
geometrically transformed regions of
interest (ROIs), and Haralick's texture
features. With Gabor filters and phase
portrait analysis, 4224 ROIs were
automatically obtained from 106 prior
mammograms of 56 interval-cancer cases,
including 301 true-positive ROIs related to
architectural distortion, and from 52
mammograms of 13 normal cases. For each
ROI, the fractal dimension, the entropy of
the angular spread of power, 10 Laws'
measures, and Haralick's 14 features were
computed. The areas under the receiver
operating characteristic curves obtained
using the features selected by stepwise
logistic regression and the leave-one-ROI-
out method are 0.76 with the Bayesian
classifier, 0.75 with Fisher linear
discriminant analysis, and 0.78 with a
single-layer feed-forward neural network.
Free-response receiver operating
characteristics indicated sensitivities of
0.80 and 0.90 at 5.8 and 8.1 false positives
per image, respectively, with the Bayesian
classifier and the leave-one-image-out
method.
3. Enhanced With the widespread use of digital cameras, Image
Assessment of the freehand wound imaging has become Processing
Wound-Healing common practice in clinical settings. There
Process by Accurate is however still a demand for a practical
Multiview Tissue tool for accurate wound healing
Classification assessment, combining dimensional
measurements and tissue classification in a
single user-friendly system. We achieved
the first part of this objective by computing
a 3-D model for wound measurements
using uncalibrated vision techniques. We
focus here on tissue classification from
color and texture region descriptors



computed after unsupervised segmentation.
Due to perspective distortions, uncontrolled
lighting conditions and view points, wound
assessments vary significantly between
patient examinations. The main
contribution of this paper is to overcome
this drawback with a multiview strategy for
tissue classification, relying on a 3-D model
onto which tissue labels are mapped and
classification results merged. The
experimental classification tests
demonstrate that enhanced repeatability
and robustness are obtained and that
metric assessment is achieved through real
area and volume measurements and wound
outline extraction. This innovative tool is
intended for use not only in therapeutic
follow-up in hospitals but also for
telemedicine purposes and clinical
research, where repeatability and accuracy
of wound assessment are critical.
4. A New Supervised This paper presents a new supervised Image
Method for Blood method for blood vessel detection in digital Processing
Vessel retinal images. This method uses a neural
Segmentation in network (NN) scheme for pixel
Retinal Images by classification and computes a 7-D vector
Using Gray-Level composed of gray-level and moment
and Moment invariants-based features for pixel
Invariants-Based representation. The method was evaluated
Features on the publicly available DRIVE and STARE
databases, widely used for this purpose,
since they contain retinal images where the
vascular structure has been precisely
marked by experts. Method performance on
both sets of test images is better than other
existing solutions in literature. The method
proves especially accurate for vessel
detection in STARE images. Its application
to this database (even when the NN was
trained on the DRIVE database)
outperforms all analyzed segmentation



approaches. Its effectiveness and
robustness with different image conditions,
together with its simplicity and fast
implementation, make this blood vessel
segmentation proposal suitable for retinal
image computer analyses such as
automated screening for early diabetic
retinopathy detection.
5. Graph Run-Length The histopathological examination of tissue Image
Matrices for specimens is essential for cancer diagnosis Processing
Histopathological and grading. However, this examination is
Image Segmentation subject to a considerable amount of
observer variability as it mainly relies on
visual interpretation of pathologists. To
alleviate this problem, it is very important
to develop computational quantitative tools,
for which image segmentation constitutes
the core step. In this paper, we introduce an
effective and robust algorithm for the
segmentation of histopathological tissue
images. This algorithm incorporates the
background knowledge of the tissue
organization into segmentation. For this
purpose, it quantifies spatial relations of
cytological tissue components by
constructing a graph and uses this graph to
define new texture features for image
segmentation. This new texture definition
makes use of the idea of gray-level run-
length matrices. However, it considers the
runs of cytological components on a graph
to form a matrix, instead of considering the
runs of pixel intensities. Working with colon
tissue images, our experiments
demonstrate that the texture features
extracted from “graph run-length matrices”
lead to high segmentation accuracies, also
providing a reasonable number of
segmented regions. Compared with four
other segmentation algorithms, the results
show that the proposed algorithm is more



effective in histopathological image
segmentation.
6. X-ray Categorization In this study we present an efficient image Image
and Retrieval on the categorization and retrieval system applied Processing
Organ and Pathology to medical image databases, in particular
Level, Using Patch- large radiograph archives. The methodology
Based Visual Words is based on local patch representation of the
image content, using a “bag of visual words”
approach. We explore the effects of various
parameters on system performance, and
show best results using dense sampling of
simple features with spatial content, and a
nonlinear kernel-based support vector
machine (SVM) classifier. In a recent
international competition the system was
ranked first in discriminating orientation
and body regions in X-ray images. In
addition to organ-level discrimination, we
show an application to pathology-level
categorization of chest X-ray data, the most
popular examination in radiology. The
system discriminates between healthy and
pathological cases, and is also shown to
successfully identify specific pathologies in
a set of chest radiographs taken from a
routine hospital examination. This is a first
step towards similarity-based
categorization, which has a major clinical
implications for computer-assisted
diagnostics
7. Standard Deviation This letter proposes a new technique of Image
for Obtaining the restoring images distorted by random- Processing
Optimal Direction in valued impulse noise. The detection process
the Removal of is based on finding the optimum direction,
Impulse Noise by calculating the standard deviation in
different directions in the filtering window.
The tested pixel is deemed original if it is
similar to the pixels in the optimum
direction. Extensive simulations prove that
the proposed technique has superior
performance, when compared to other



existing methods, especially at high noise
rates.
8. Removal of High A modified decision based unsymmetrical Image
Density Salt and trimmed median filter algorithm for the Processing
Pepper Noise restoration of gray scale, and color images
Through Modified that are highly corrupted by salt and pepper
Decision Based noise is proposed in this paper. The
Unsymmetric proposed algorithm replaces the noisy pixel
Trimmed Median by trimmed median value when other pixel
Filter values, 0's and 255's are present in the
selected window and when all the pixel
values are 0's and 255's then the noise pixel
is replaced by mean value of all the
elements present in the selected window.
This proposed algorithm shows better
results than the Standard Median Filter
(MF), Decision Based Algorithm (DBA),
Modified Decision Based Algorithm
(MDBA), and Progressive Switched Median
Filter (PSMF). The proposed algorithm is
tested against different grayscale and color
images and it gives better Peak Signal-to-
Noise Ratio (PSNR) and Image
Enhancement Factor (IEF).
9. IMAGE Resolution In this correspondence, the authors propose Image
Enhancement by an image resolution enhancement Processing
Using Discrete and technique based on interpolation of the
Stationary Wavelet high frequency subband images obtained by
Decomposition discrete wavelet transform (DWT) and the
input image. The edges are enhanced by
introducing an intermediate stage by using
stationary wavelet transform (SWT). DWT
is applied in order to decompose an input
image into different subbands. Then the
high frequency subbands as well as the
input image are interpolated. The estimated
high frequency subbands are being
modified by using high frequency subband
obtained through SWT. Then all these
subbands are combined to generate a new
high resolution image by using inverse DWT



(IDWT). The quantitative and visual results
are showing the superiority of the proposed
technique over the conventional and state-
of-art image resolution enhancement
techniques.
10. Automatic Optic Disc Under the framework of computer-aided Image
Detection From eye disease diagnosis, this paper presents Processing
Retinal Images by a an automatic optic disc (OD) detection
Line Operator technique. The proposed technique makes
use of the unique circular brightness
structure associated with the OD, i.e., the
OD usually has a circular shape and is
brighter than the surrounding pixels whose
intensity becomes darker gradually with
their distances from the OD center. A line
operator is designed to capture such
circular brightness structure, which
evaluates the image brightness variation
along multiple line segments of specific
orientations that pass through each retinal
image pixel. The orientation of the line
segment with the minimum/maximum
variation has specific pattern that can be
used to locate the OD accurately. The
proposed technique has been tested over
four public datasets that include 130, 89,
40, and 81 images of healthy and
pathological retinas, respectively.
Experiments show that the designed line
operator is tolerant to different types of
retinal lesion and imaging artifacts, and an
average OD detection accuracy of 97.4% is
obtained.
11. Wavelet-Based In this letter, we propose an efficient one- Image
Image Texture nearest-neighbor classifier of texture via Processing
Classification Using the contrast of local energy histograms of
Local Energy all the wavelet subbands between an input
Histograms texture patch and each sample texture
patch in a given training set. In particular,
the contrast is realized with a discrepancy
measure which is just a sum of



symmetrized Kullback-Leibler divergences
between the input and sample local energy
histograms on all the wavelet subbands. It is
demonstrated by various experiments that
our proposed method obtains a satisfactory
texture classification accuracy in
comparison with several current state-of-
the-art texture classification approaches.
12. A Ringing-Artifact This paper proposes a new ringing-artifact Image
Reduction Method reduction method for image resizing in a Processing
for Block-DCT-Based block discrete cosine transform (DCT)
Image Resizing domain. The proposed method reduces
ringing artifacts without further blurring,
whereas previous approaches must find a
compromise between blurring and ringing
artifacts. The proposed method consists of
DCT-domain filtering and image-domain
post-processing, which reduces ripples on
smooth regions as well as overshoot near
strong edges. By generating a mask map of
the overshoot regions, we combine a ripple-
reduced image and an overshoot-reduced
image according to the mask map in the
image domain to obtain a ringing-artifact
reduced image. The experimental results
show that the proposed method is
computationally faster and produces
visually finer images than previous ringing-
artifact reduction approaches.
13. Automatic Exact Histogram equalization, which aims at Image
Histogram information Processing
Specification for maximization, is widely used in different
Contrast ways to perform contrast
Enhancement and enhancement in images. In this paper, an
Visual System automatic exact
Based Quantitative histogram specification technique is
Evaluation proposed and used for global
and local contrast enhancement of images.
The desired histogram
is obtained by first subjecting the image
histogram to a modification



process and then by maximizing a measure
that represents increase
in information and decrease in ambiguity. A
new method of
measuring image contrast based upon local
band-limited approach
and center-surround retinal receptive field
model is also devised in
this paper. This method works at multiple
scales (frequency bands)
and combines the contrast measures
obtained at different scales
using �� -norm. In comparison to a few
��
existing methods, the effectiveness
of the proposed automatic exact histogram
specification
technique in enhancing contrasts of images
is demonstrated
through qualitative analysis and the
proposed image contrast measure
based quantitative analysis.
14. Fast Sparse Image Compressed sensing is a new paradigm for Image
Reconstruction signal Processing
Using recovery and sampling. It states that a
Adaptive Nonlinear relatively small number
Filtering of linear measurements of a sparse signal
can contain most of
its salient information and that the signal
can be exactly reconstructed
from these highly incomplete observations.
The major
challenge in practical applications of
compressed sensing consists
in providing efficient, stable and fast
recovery algorithms which,
in a few seconds, evaluate a good
approximation of a compressible
image from highly incomplete and noisy
samples. In this paper,
we propose to approach the compressed
sensing image recovery



problem using adaptive nonlinear filtering
strategies in an iterative
framework, and we prove the convergence
of the resulting
two-steps iterative scheme. The results of
several numerical experiments
confirm that the corresponding algorithm
possesses the
required properties of efficiency, stability
and low computational
cost and that its performance is competitive
with those of the state
of the art algorithms.
15. Binary Tissue A pressure ulcer is a clinical pathology of Medical
Classification on localized Imaging
Wound Images With damage to the skin and underlying tissue
Neural Networks caused by pressure,
and Bayesian shear, or friction. Diagnosis, treatment, and
Classifiers care of pressure
ulcers are costly for health services.
Accurate wound evaluation
is a critical task for optimizing the efficacy
of treatment and
care. Clinicians usually evaluate each
pressure ulcer by visual
inspection of the damaged tissues, which is
an imprecise manner
of assessing the wound state. Current
computer vision approaches
do not offer a global solution to this
particular problem. In this
paper, a hybrid approach based on neural
networks and Bayesian
classifiers is used in the design of a
computational system for
automatic tissue identification in wound
images. A mean shift
procedure and a region-growing strategy
are implemented for
effective region segmentation. Color and
texture features are
extracted from these segmented regions. A



set of multilayer
perceptrons is trained with inputs
consisting of color and texture
patterns, and outputs consisting of
categorical tissue classes which
are determined by clinical experts. This
training procedure is
driven by a -fold cross-validation method.
Finally, a Bayesian
committee machine is formed by training a
Bayesian classifier
to combine the classifications of the neural
networks. Specific
heuristics based on the wound topology are
designed to significantly
improve the results of the classification. We
obtain high
efficiency rates from a binary cascade
approach for tissue identification.
Results are compared with other similar
machine-learning
approaches, including multiclass Bayesian
committee machine
classifiers and support vector machines.
The different techniques
analyzed in this paper show high global
classification accuracy
rates. Our binary cascade approach gives
high global performance
rates (average sensitivity ��__ __, specificity
��__ __, and
accuracy ��____) and shows the highest
average sensitivity
score (�� 86.3%) when detecting necrotic
tissue in the wound
16. Removal of Artifacts We present a segmentation-based post-
from JPEG processing method to remove compression
Compressed artifacts from JPEG compressed
Document document images. JPEG compressed images
Images typically exhibit ringing and blocking
artifacts, which can be
objectionable to the viewer above certain



compression levels. The ringing is more
dominant around textual
regions while the blocking is more visible in
natural images. Despite extensive research,
reducing these artifacts
in an effective manner still remains
challenging. Document images are often
segmented for various reasons. As a
result, the segmentation information in
many instances is available without
requiring additional computation. We
have developed a low computational cost
method to reduce ringing and blocking
artifacts for segmented document
images. The method assumes the textual
parts and pictorial regions in the document
have been separated from
each other by an automatic segmentation
technique. It performs simple image
processing techniques to clean
out ringing and blocking artifacts from
these regions.
17. A Low-Cost VLSI Image and video signals might be corrupted
Implementation for by impulse
Efficient noise in the process of signal acquisition
Removal of Impulse and transmission.
Noise In this paper, an efficient VLSI
implementation for removing impulse
noise is presented. Our extensive
experimental results show
that the proposed technique preserves the
edge features and obtains
excellent performances in terms of
quantitative evaluation
and visual quality. The design requires only
low computational
complexity and two line memory buffers. Its
hardware cost is quite
low. Compared with previous VLSI
implementations, our design
achieves better image quality with less
hardware cost. Synthesis



results show that the proposed design
yields a processing rate of
about 167 M samples/second by using
TSMC 0.18 m technology.
18. EVALUATION OF Microaneurysms (MAs) are the earliest sign
RETINAL VESSEL of diabetic
SEGMENTATION retinopathy and manifest as small reddish
METHODS FOR spots on the retina.
MICROANEURYSMS Generally, algorithm design for MAs
DETECTION detection starts by
separating the vascular system from the
background for a
posterior analysis of candidate MAs
presence. Following
this approach, this paper assesses three
different methods
for vessel segmentation and how they affect
posterior MAs
detection. The robustness in developing
automatic screening
systems for MAs detection is discussed and
a methodology
to detect candidate MAs in retinal images is
introduced. The
algorithm combines different vessel
segmentation methods
with region growing to evaluate which is
the best to provide
candidate MAs detection
19. Secret Protecting privacy for exchanging Signal
Communication information Processing
Using through the media has been a topic
JPEG Double researched by many people.
Compression Up to now, cryptography has always had its
ultimate role in
protecting the secrecy between the sender
and the intended receiver.
However, nowadays steganography
techniques are used
increasingly besides cryptography to add
more protective layer
to the hidden data. In this letter, we show



that the quality factor
in a JPEG image can be an embedding space,
and we discuss the
ability of embedding a message to a JPEG
image by managing
JPEG quantization tables (QTs). In
combination with some permutation
algorithms, this scheme can be used as a
tool for secret
communication. The proposed method can
achieve satisfactory
decoded results with this straightforward
JPEG double compression
strategy.
20. Fast Vanishing Point Vision-based road detection in unstructured Image
Detection in environments is a challenging problem as Processing
Unstructured there are hardly any discernible and
invariant features that can characterize the
road or its boundaries in such
environments. However, a salient and
consistent feature of most roads or tracks
regardless of type of the environments is
that their edges, boundaries and even ruts
and tire tracks left by previous vehicles on
the path appear to converge into a single
point known as the vanishing point. Hence,
estimating this vanishing point plays a
pivotal role in the determination of the
direction of the road. In this paper, we
propose a novel methodology based on
image texture analysis for fast estimation of
the vanishing point in challenging and
unstructured roads. The key attributes of
the methodology consist of Optimal Local
Dominant Orientation Method (OLDOM)
that uses joint activities of only four Gabor
filters to precisely estimate the local
dominant orientation at each pixel location
in the image plane, weighting of each pixel
based on its dominant orientation, and an
adaptive distance based voting scheme for
estimation of the vanishing point. A series



of quantitative and qualitative analyses are
presented using natural data sets from the
DARPA Grand Challenge projects to
demonstrate the effectiveness and accuracy
of the proposed methodology.

Matlab / Projects / Project / Image processing

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