Classification and segmentation methods
in Digital mammography -A Detailed
Study
Dr.J.Janet, S.VenkataLakshmi 1, Principal, Sri Krishna College of Engineering and
technology,
Kuniamuthur, Coimbatore, India. 2Associate professor, Department of Computer science and
Engineering, Panimalar Institute of Technology, Chennai
[email protected], [email protected]
Abstract This paper intends to present the several medical imaging techniques
contribute to the breast cancer detection and diagnosis. Some of the
noteworthy medical imaging techniques are MRI, CT, ultrasound and
mammography. Among all these techniques, mammography is more
effective and it uses a minimal dose of x-rays to analyze the breast
diseases. Mammography provides high quality mammograms, which
are beneficial to the medical world. This paper details the methods
available in the literature for early detection and the filtering methods
used, Apart from them the sections details the classification and
segmentation techniques involved in the early detection.
Keywords : Mammogram ,Classification, Segmentation ,Preprocessing
and denoising.
1. Introduction
International Journal of Pure and Applied MathematicsVolume 119 No. 12 2018, 1097-1115ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu
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The mammography is classified into two categories, which are film and digital
mammography, as cited by Gur D. (2007)[1] and Pisano E.D. et.al. (2007)[2]. The
film mammography utilizes image film to form the mammogram. Conversely,
digital mammography captures the breast electronically and the digital
mammogram is stored directly to the computer. On comparison, digital
mammography is better than the film mammography. Some of the drawbacks of the
film mammography are .The contrast of the film mammogram is not up to the
mark, the filmy mammogram cannot be enhanced, once the image is captured, the
filmy mammograms cannot be stored electronically, the filmy mammograms are
difficult to process, these drawbacks are addressed by the digital mammography,
whose advantages are listed below :
• The qualities of the mammograms are very high.
• Digital mammograms are easy to store and process.
• The requirement of x-ray dosage is very minimal, when compared to the
film mammography.
• Digital mammograms show minute details with high clarity.
The mammography can be categorized into two kinds with respect to the
mammogram analysis. They are screening and diagnostic mammography. The
screening mammography acts as a preliminary examination to detect the
abnormalities. The screening mammography captures four different views, two
views for each breast. The views being covered by screening mammography are
Cranio Caudal (CC) and Medio-Lateral Oblique (MLO) view, as stated by Del M.
et.al. (2007)[1]. Diagnostic mammography is necessary, only when the screening
mammography is observed to be abnormal. The diagnostic mammography is more
powerful than the screening mammography. The diagnostic mammography can
sieve through the abnormal regions to capture a more detailed version of
mammograms. Doctors are working to learn more about early-stage and locally
advanced breast cancer, including ways to prevent it, how to best treat it, and how
to provide the best care to people diagnosed with this disease. The areas of research
may include new options for patients through clinical trials. Hence it becomes
essential to know about the stages of the breast cancer and the researches that has
been carried out. So far in the study the following table summarises the research
about the different study that was made by some of the researchers. Automated
diagnosis systems are meant to enhance the interpretation capability of the
healthcare professional, while analyzing the medical images. In the literature,
automated diagnosis systems are observed in abundance for detecting and
diagnosing different diseases. This research thesis is supposed to detect breast
abnormalities, and thus the automated diagnosis system being proposed for
mammograms.The automated diagnosis system for detecting breast cancer
distinguish between the malignant and benign kinds of cancer, as discussed by
Giger M.L. and Karssemeijer N. (2001)[2], Giger M.L. (2000),[3], Doi K. et.al. (1999)
[4], Vyborny C.J. et.al. (2000)[5]. The introduction of automated diagnosis system in
cancer detection harvests so many benefits, such as time conservation, detection
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accuracy and stable results, stated by Doi K. (2009)[26]. The breast cancer detection
by automated diagnosis system can exploit any kind of medical images such as
mammograms, ultrasound or thermal images.
Owing to the wider usage of mammograms, the research works propose
automated diagnosis system that utilize mammogram images for detecting breast
cancer. The automated diagnosis systems are employed in two scenarios, one is to
detect and the other is to diagnose the breast cancer. The automated diagnosis
system to detect breast cancer aims to assist the radiologist in detecting the
abnormal areas in the mammograms. On the other hand, the automated diagnosis
system can differentiate between the benign and malignant kinds of cancer. The
automated diagnosis system to diagnose breast cancer is more powerful than the
automated diagnosis system for detecting breast cancer, as stated by Getty D.J.
et.al. (1988)[7], Horsch K. et.al. (2006) [8], Huo Z. et.al. (2002)[9] and Jiang Y. et.al.
(1999)[10]. The reason is that the automated diagnosis system can assist the
healthcare professional effectively with least false positive and false negative
results. The following section intends to review both the detective and diagnostic
automated system.
2. Automated Diagnosis System for Detection and Diagnosis
Automated diagnosis system make use of the advanced image processing techniques
to achieve the goal. The automated diagnosis system to detect breast cancer aims at
marking the abnormalities being present in the mammograms. The abnormal areas
in the mammograms are tracked by extracting the regions of interest. Li H. et.al.
(1995)[11] has stated that the techniques to extract regions of interest are based on
either pixel or region. Pixel based techniques are easy to operate, however these
techniques involve computational complexity. Conversely, region based techniques
rely on segmentation operation for extracting region of interest. Region based
techniques do not involve any computational overhead. The abnormalities in the
mammograms are found by taking two signs into account, which are mass and
microcalcification, as stated by Sampat M.P. et.al. (2005)[12]. When masses are
concerned, the mass detection algorithms intend to identify the mass being present
in the mammograms and to classify the area as normal or mass. Usually, the
masses are characterized by their shape and outline. Edge based operations are
necessary to differentiate between the normal tissue region and masses. Several
features such as shape, texture are extracted from the region of interest, in order to
distinguish between the normal breast tissue and mass. Microcalcification is
another important symptom of breast cancer. The Microcalcification are minute
calcium deposits and they appear very small in they mammograms. Owing to its
smaller size, there is a high chance that the healthcare professional may miss it out.
However, the Microcalcification detection system is capable of finding the
microcalcification, by extracting useful features from the mammograms. Several
methods are proposed for microcalcification detection in mammograms. Typically,
an automated diagnosis system for diagnosing breast cancer involves image
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processing activities such as image pre-processing, segmentation, feature extraction
and classification. The process of segmentation is the base for the diagnostic
automated diagnosis system. Basically, the image can be segmented in two ways
such as region growing and contour based techniques.
Mammogram features play an important role in classifying between the
normal and malignant kind of cancer. Texture is the most important feature that
can differentiate between the benign and malignant cancer. The breast cancer
diagnostic algorithm extracts features from the mammograms. Certain techniques
make employ feature selection techniques for minimizing the computational
overhead and to speed up the classification process. Generally, the process of
feature selection is carried out by bio-inspired algorithms. Finally, the process of
classification relies on machine learning techniques. The classifier is imparted with
the knowledge about the normal and abnormal mammograms, through the
extracted features. Based on the features being extracted from the mammograms,
the classifier can classify between the normal, benign and malignant type of cancer.
The classification task can be accomplished by k-Nearest Neighbour (k-NN),
Support Vector Machine (SVM), Extreme Learning Machine (ELM) and so on.
Artificial Neural Networks (ANN) are also employed for the process of classification.
The following section reviews the mammogram pre-processing, segmentation,
feature extraction and classification processes
3. Preprocessing methods and Filters
The central themes of mammogram pre-processing techniques are to enhance the
quality of the image and to remove unwanted information from the image.
Mammograms are intricate such that it is difficult for the healthcare professionals
to interpret. However, when sufficient time is spent in the process of mammogram
pre-processing, it is easy for the healthcare professional to infer from the
mammograms. Pre-processing prepares the image to be suitable for the forthcoming
image processing activities such as mammogram segmentation and feature
extraction. The following sections discuss about the popular mammogram pre-
processing techniques. There are many pre-processing techniques available in the
literature, but generally different filters are used for denoising. Some procedure
enhancement, registration, image alignment and so on. The figure1 shows some of
the methods which are available in the literature.
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Fig.1. Classification of Image denoising filters
3.1 Mammogram denoising
Noise is denoted as the unwanted information being present in the image.
This noise must be removed from the mammograms, in order to obtain high quality
mammograms. The noises can be removed by filters, which can be high or low pass
filter. The low pass filters denoise the mammograms effectively, however it
smoothens the edges. The edges are very important for accurate detection of
abnormal cells. In this case, high pass filters and biased low pass filters serve well,
as suggested by Vasantha M. and Bharathi V.S. (2011)[13]. Some of the filtering
techniques for image denoising are presented below.
3.3.1 Mean filter
The mean filter is one of the oldest technique to improve the quality of the
mammogram. The filter works by changing each and every pixel with the average
pixel intensity of the neighbourhood pixels. The mean filter is easy to implement,
however there are certain drawbacks associated with it. Some of them are blurred
images and noise removal inefficiency.
3.3.2 Adaptive mean filter
Mean filter encounters the image blurring issue and adaptive mean filter
overcomes this issue. Cheng, Heng-Da, et.al. (2010)[14], presents an adaptive mean
filter, which ensures the stability of the image by pixel averaging and filtering
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concepts. The adaptive mean filter preserves the edges, through which the noises
are removed successfully from the image.
3.3.3 Median filter
Median filter is a non-linear filter that effectively denoises an image.
However, the edges of the image are conserved. There are several flavours of
median filter weighted median filter, Centre weighted median filter, max-median
filter and so on.
3.3..4 Adaptive median filter
Adaptive median filter follows the window size of rectangular shape. Usually,
the window size is determined as that encloses the corresponding pixel. The
pixel value of the image edges are marked as zero. The value of the corresponding
pixel is substituted by the median value of the neighbourhood values. The main
advantage of this kind of filter is that it preserves the edges and denoises the image
effectively. Besides this, the size of the window is not fixed and is adaptable, as
stated by Nagi J. (2010)[15].
3.2 Mammogram contrast enhancement
The pre-processing phase not only deals with image denoising, but also
strives to enhance the contrast. There are several techniques to enhance the
contrast of the mammograms and they are histogram equalization, Contrast
Limited Adaptive Histogram Equalization (CLAHE) and so on. The following
sections present the overview of these techniques.
3.2.1 Histogram Equalization
The histogram equalization technique scatters the gray levels over the image,
so as to arrive at a consistent histogram. Based on the histogram of the image, the
contrast is enhanced. The intensities of the image are equally distributed. This
equal intensity distribution enhances the image contrast in a better way. This
technique is practical in brighter or darker images. This technique can focus on the
image abnormalities effectively, as stated by Vasantha M. and Bharathi V.S.
(2010)[16].Consider an image with gray level ranging from . Let be the
gray level of the image, whose contrast has to be enhanced. For each and every pixel
the associated gray level is computed to measure . The value of is
increasing and is represented as follows.
. . . (1)
In the above equation, ranges from and . is the probability of the
presence of gray level in the image, is the count of pixels with intensity . The
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is the representation of histogram, as presented by Rangayyan R.M. (2004)[17]
and Gonzalez R.C. and Woods R.E. (2007)[18].
3.2.2 Adaptive histogram equalization
Several modified techniques are proposed in the literature to arrive at
enhanced histogram equalization. One of the important variant of histogram
equalization is adaptive histogram equalization. The only difference between the
histogram equalization and adaptive histogram equalization is that the adaptive
histogram equalization technique computes several histograms from different
regions of the image. This quality improves the local contrast of the images, as
claimed by Rehm K.G. et.al. (1990)[19]. However, Sivaramakrisha R. et.al.
(2000)[20] claimed that this technique suffers from over amplification of noise.
3.2.3 CLAHE
Pizer S.M. et.al. (1987)[21] proposed the CLAHE technique, which is an
enhancement of adaptive histogram equalization. This technique can denoise an
image as well. Sometimes, adaptive histogram equalization may over enhance the
pixels. This problem is well-addressed by CLAHE by trimming the histogram to a
certain limit. This technique works by computing the local histogram of each and
every pixel, with respect to the neighbourhood pixels. Finally, the histogram is
trimmed to a particular limit and the pixels are redistributed. The following section
presents the process of segmentation techniques.
3.4 MAMMOGRAM SEGMENTATION TECHNIQUES
Segmentation is the process of decomposing the image to a desired level,
which makes the image processing task easier. Usually, this process can follow
three different ways to achieve the task of segmentation. They are pixel, boundary
and region based segmentation techniques. The following subsections explain these
segmentation techniques. The figure 3.2 presents the detailed view of segmentation
methods.
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Fig.2. Classification of Segmentation
3.4.1 Pixel based segmentation
Pixel based segmentation is carried out by means of histogram, without
considering any contextual information. For instance, the threshold based
segmentation technique implies that the pixels in certain range form a cluster. The
cluster can be formed by taking the gray level, colour level or intensity into account.
These techniques do not take the spatial information of the image into
consideration. Hence, these techniques cannot deal with the noisy and blurred
images. The major drawback of this technique is the overlapping of gray level
pixels.
3.4.2 Boundary based segmentation
The boundary based segmentation techniques operate by detecting the
boundary or edges and the detected edges are connected together to build the
outline. Hence, there are two steps involved in boundary based segmentation, which
are boundary detection and inter connection. The boundary of the image object is
detected by means of several techniques such as sobel, prewitts, zero-cross and
canny based edge detection techniques. As soon as the edges are detected, they are
interconnected by taking the direction and magnitude of the gradient vector. Out of
all these techniques, sobel is found as the best to detect edges.
3.4.3 Region based segmentation techniques
These segmentation techniques take the gray level of the neighbourhood
pixels into account. This makes sense that the pixel values of a region are similar to
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each other. Some of the popular region based segmentation techniques are split and
merge and seeded region growing.
3.4.3.1 Split and merge
The split and merge technique decomposes the image until all the pixels of
the region meet a homogeneity condition. After the completion of this step, the
regions that share the same homogeneity condition are merged together.
3.4.3.2 Seeded region growing
This technique is based on the seed points. Initially, the seed points are
selected by taking the gray level or some other parameter into account. This is
followed by scanning the neighborhood pixels of the seed point pixel. By this way,
the region grows until the homogeneity condition is satisfied. Thus, the basics of
segmentation process are described. The following part presents the feature
extraction of mammograms.
3.5 FEATURE EXTRACTION TECHNIQUES
Feature extraction is the most important step of any image processing
activity. As far as mammograms are concerned, there are four different feature sets
which can be extracted. They are unique mammogram features, statistical texture
features, multi-scale texture features and fractal dimension features. The following
subsections elaborate these feature sets.
3.5.1 Unique mammogram features
The unique mammogram features represent the characteristic features of a
particular mammogram, which can be geometrical or structural features. Some of
the significant features of this category are area, average gray level of the pixels,
perimeter, elongation, eccentricity, contrast, mean intensity, orientation, and
direction and so on. Figure 2 shows the different approaches of feature selection
based on region and contour.
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Texture Features
Spectral Features
Geometrical Features
Statistical feature
Fourier Transformation
Feature selection
Spatial Features based
Stereo matching
Color Features based
Contour
Based
Region
Based
Fig.3. Overview of Feature selection Approaches
3.5.2 Statistical texture features
These statistical texture features are also called as co-occurrence features.
These co-occurrence features are extracted from the co-occurrence matrix and these
matrices are called as spatial gray level dependence matrices. Some of the popular
techniques of this method are Surrounding Region Dependence Method (SRDM),
Spatial Gray Level Dependence Method (SGLDM), Gray Level Run Length Method
(GLRLM), Gray Level Difference Method (GLDM) and so on.
3.5.3 Multi-scale texture features
The multi-scale texture features are gained from multi-resolution analysis of
the images. Usually, wavelet based methods are utilized and the features are
extracted from different scales of the wavelet transform. For instance, features such
as energy, entropy and norm are extracted in varying scales to analyse the images.
3.5.4 Fractal dimension features
The fractal dimension features are extracted by keeping the image in fractal
mode. These features can signify the image surface, which can be smooth or rough.
The fractal features are utilized to train the classifier, in order to classify between
the normal and abnormal areas of an image. Thus, a short summary of feature
extraction techniques are presented and the next section intends to provide details
about image classification.
3.6 IMAGE CLASSIFICATION
Classifiers play a significant role in distinguishing between the suspicious
and normal areas of an image. The classifiers take the feature sets as input and
gains knowledge out of them. Usually, the classifier encounters two stages, which
are training and testing.
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In the training stage, the classifier is made to learn with the extracted
feature sets, such that the knowledge about different classes. In the testing stage, a
query or a test image is passed. Based on the acquired knowledge from the training
phase, the classifier is capable of distinguishing between different classes. Some of
the popular classification techniques for images are neural networks, linear
classifier, quadratic classifier, bayesian classifier, fuzzy decision tree, binary
decision tree, k-NN classifier, SVM, ELM and so on. Hence, the basics of image
classification are presented. The forthcoming section presents the literature that is
closely related to the proposed approaches.
Based on Characters used
Based on Training Samples
Based on the Assumptions Used
Pixel Information Based Methods
Based on Number of Outputs
Based on Spatial Information
Cla
ssif
ica
tio
n
Me
tho
ds
Motion Based
Shape Based
Supervised
Un-Supervised
Parametric
Non Parametric
Pixel/Sub pixel
Pre-field
Hard Classifier
Soft Classifier
Spectral
Contextual
ANN, SVM, Decision Tree, Expert System
Centroids, Geometric shapes, Skeleton, Contours
Pixel distance, Parallel piped, Maximum likehood
K-Mean, Fuzzy C mean Clustering
Linear Discriment Analysis,Maximum Like hood
Temporal tracking
Spectral Mixture Analysis ,Subpixel ,Fuzzy-set Classification
GIS-Based Classification
maximum likelihood, minimum distance, artificial neural network, decision tree,
Fuzzy –Classification
minimum distance, artificial neural network
frequency-based contextual classifier.
Fig.4. Classification methods based on different approaches
3.7 state-of-the-art literature
Recently, so many works have been proposed to detect the breast cancer at an
early stage, by means of mammograms. This section presents the summary of the
existing works, which are the driving force of this thesis.
3.7.1 Automated diagnosis system for Mammogram Abnormality Detection and
Diagnosis
A technique to classify the mammogram abnormalities as normal and
abnormal is presented by Nithya R. and Santhi B. (2012)[22]. This work utilizes the
statistical features such as mean, variance, standard deviation etc., and pixel
intensity. Neural network is employed as the classifier. The experimental results
are compared between the statistical features and pixel intensity. The results show
that the accuracy rate of pixel intensity is more than the statistical features.
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Nithya R. and Santhi B. (2011) [23] employed Gray Level Co-occurrence
Matrix (GLCM) to extract features. For the purpose of classification, neural network
is used. A system to detect the abnormalities in mammograms is presented by
Jasmine J.S.L. and Baskaran S. (2011)[24]. This work extracts features from non-
subsampled contourlet. SVM is utilized to classify between the normal and
abnormal regions of the mammograms.Suckling J. et.al. (1994)[25] presented a new
technique to locate the abnormal masses in mammograms. The regions of interest
are extracted from the mammograms before proceeding with feature extraction and
classification. The regions of interest are extracted by k-means algorithm and
template matching technique. While extracting features, simpson’s diversity index
is utilized for different shapes. SVM is utilized as the classifier. The work proposed
by Nunes A.P. et.al. (2010)[26] utilizes shape descriptors for abnormality detection
in mammograms. A scheme to differentiate the mammograms with respect to mass
is presented by Costa D. et.al. (2011)[27] and the work extracts the region of
interest and then the features are extracted. The process of feature extraction is
done by Principal Component Analysis (PCA) and Gabor wavelet. SVM is exploited
for classification purposes. A methodology to differentiate the abnormalities of
mammograms is proposed by Berbar M.A. et.al. (2012)[28]. This work extracts the
regions of interest from the mammograms, followed by which features are extracted.
The features being considered are mean, standard deviation, energy, entropy,
asymmetry and smoothness in association with Local Binary Patterns (LBP). This
work employs k Nearest Neighbour (k-NN) and SVM as classifiers and the
performance of the classifier is evaluated. The mass and non-mass regions of
mammograms are detected by Carvalho P.M.S. et.al. (2012)[29]. The texture
features are extracted from the areas of interest by an index, which is calculated by
histogram, GLCM and Gray Level Run Length Matrix (GLRLM). This work is
claimed to be more accurate.Hussain M. (2013)[30] proposed a technique to classify
the mass and the normal regions of breast tissue. Initially, the ROIs are extracted
from the mammogram image. The ROIs are then treated in multiple directions and
scales by Gabor filter. The classifier being employed is SVM.-Nguyen M. et.al.
(2013)[31] a breast cancer classification system is developed, which is trained by
Block Variance of Local Coefficients (BVLC). These texture features help the SVM
to differentiate between the mass and non-mass regions of the mammograms.
A texture feature based breast cancer classification system is presented by
Moayedi F. et.al. (2010)[32], which consists of three important phases such as pre-
processing, feature extraction and classification. The pre-processing step removes
the unwanted portions of the mammogram. The texture features are extracted by
means of contourlet and the features are enhanced by means of genetic algorithm.
SVM classifier is utilized in this work. A automated diagnosis system is presented
by Gorgel P. et.al. (2013)[33], breast cancer diagnosis is presented. Initially, ROIs
are extracted from the mammograms, followed by which feature extraction takes
place by applying spherical wavelet transformation. Finally, SVM is employed for
attaining the classification.Nakayama R.et.al. (2006)[34] presented a technique to
detect microcalcification. Initially, the mammograms are partitioned by filter bank
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and then the regions of interest are extracted. Eight different features are extracted
from each and every region of interest. Bayes discriminant function is employed to
differentiate between the normal and abnormal presence in mammograms.
Another work to detect microcalcification in mammograms is presented by
Kavitha K. and Kumaravel N. (2007) [35], which utilized filter bank, Discrete
Cosine Transform (DCT) and Bayesian classifier. However, the performance of the
work is tested over only 40 mammograms. Halkiotis S. et.al. (2007)[56] presented a
method to detect microcalcifications by clubbing mathematical morphology and
Artificial Neural Networks (ANN). The work by Bhattacharya M. and Das A.
(2007)[37] presented wavelet based microcalcification detection. Pal N.R. et.al.
(2008)[38] presented a multi-phase classification system for microcalcification
detection. Initially, the calcified regions are identified by Back Propagation Neural
Network (BPNN), the outcome of BPNN is cleansed by connected component
analysis and the thinner particles are eliminated. Finally, the classification is done
by taking the density into account.Another microcalcification detection technique is
proposed by Oh W.V. et.al. (2008)[39], in which Gray Level Co-occurrence Matrix
(GLCM) is exploited to segment the mammograms. The microclacification samples
are extracted by foveal method and the eight different features. A double layered
system for microcalcification detection is proposed by Harirhi F. et.al. (2010)[40].
Initially, six features from wavelet and two gray level features are utilized to detect
sample microcalcification by multilayer neural network. The so detected sample
microcalcifications are processed further and 25 features are extracted. The 25
features are reduced to 10 by Geometric Linear Discriminant Analysis (GLDA).
Finally, SVM is utilized as the second level classifier.
Oliver A. et.al. (2012)[41] local features are extracted from the mammograms
and a boosted classifier is employed to distinguish between the normalities and
abnormalities. The work presented by Zhang X. and Gao X. (2012)[42] enhances the
sample microcalcification by means of filter. The features are then extracted by
subspace learning algorithm and the classification is achieved by Twin SVM
(TWSVM).A mammogram classification model is proposed by Eltoukhy M.M. et.al.
(2012)[43] which used wavelet and curvelet for feature extraction process is
proposed. The features being extracted are sieved through by statistical t-test and
the Support Vector Machine (SVM) is employed as the classifier. Christoyianni I.
et.al. (2002)[44] presented a mammogram classification scheme, which extracts
features from the regions of interest. The remarkable features are then selected by
independent component analysis. Finally, the abnormal regions are detected by the
neural network. A system to detect microcalcification in mammograms is presented
by Karahaliou A. et.al. (2007)[45] and is based on wavelets. The texture features of
first order statistical, co-occurrence matrices, run length matrices and energy
measures are calculated. Finally, k-NN classifier classifies between the
mammograms.
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Gardezi S.J.S.et.al. (2014)[46]a texture feature based mammogram
classification model is presented, which uses Grey Level Co-occurrence Matrix
(GLCM) features being extracted from curvelet bands. Simple logistic classifier is
employed for the purpose of classification. The work presented by Oliver A. et.al.
(2007)[47] utilized Local Binary Pattern (LBP) and Co-occurrence matrix to extract
texture features from the regions of interest. Additionally, this work incorporates
Leave One Out (LOO) strategy with the SVM, in order to attain good classification
rate. This work is extended by Oliver A. et.al. (2007B)[48], so as to reduce the false
positive rates by utilizing LBP texture features. Paquerault S. et.al. (2002) [49] the
regions of interest are segmented and then the features are extracted by texture
and morphological feature extractors. This work has shown minimal false positive
rate. Duarte Y. et.al. (2014)[50] utilized Completed Local Binary Pattern (CLBP)
and wavelets for feature extraction and then the classification process is followed.
7. Limitations and Future Research
The study is confined to some of the Limitations like ANN needs careful
temperature inspection, Digital Mammography-Frequent intensity changes may not
give 100% diagnostic results. Advantages of the methods are ANN - BP neural
network has been provided with fairly good results of classification and statistical
parameter, Automated Image segmentation - Only lower breast part is taken into
account and upper body parts are filtered out. Neural Network and Morphology.
Complete removal of spike noise through morphology., K-NN and Fuzzy means -
Change of intensity is used as a discriminating feature Screening Mammography -
Diagnosis is based on the roughness (between normal and tumor tissues) This paper
presents the fundamentals of mammography, automated diagnosis system for
breast cancer detection and diagnosis. Additionally, the outline of image pre-
processing, segmentation, feature extraction and classification techniques are
presented. Finally, the related works with respect to the proposed approaches of
this research are presented
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