Classification and segmentation methods in Digital mammog ... · However, the Microcalcification detection system is capable of finding the microcalcification, by extracting useful

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

International Journal of Pure and Applied Mathematics Special Issue

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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

References

1. Li, Huai-Dong, et al. "Markov random field for tumor detection in

digital mammography." IEEE transactions on medical imaging 14.3

(1995): 565-576

2. Sampat, Mehul P., Mia K. Markey, and Alan C. Bovik. "Computer-

aided detection and diagnosis in mammography." Handbook of image

and video processing 2.1 (2005): 1195-1217.

3. Vasantha, M., and V. Subbiahbharathi. "Classification of

mammogram images using hyprid features." European Journal of

Scientific Research 57.1 (2011): 87-96.

4. Cheng, Heng-Da, et al. "Automated breast cancer detection and

classification using ultrasound images: A survey." Pattern

Recognition 43.1 (2010): 299-317.


1110

5. Nagi, Jawad, et al. "Automated breast profile segmentation for ROI

detection using digital mammograms." Biomedical Engineering and

Sciences (IECBES), 2010 IEEE EMBS Conference on. IEEE, 2010.

6. Vasantha, M., Dr V. Subbiah Bharathi, and R. Dhamodharan.

"Medical image feature, extraction, selection and classification."

International Journal of Engineering Science and Technology 1.2

(2010): 2071-2076.

7. Moayedi, Fatemeh, et al. "Contourlet-based mammography mass

classification using the SVM family." Computers in biology and

medicine 40.4 (2010): 373-383.

8. Rangayyan, Rangaraj M. Biomedical image analysis. CRC press,

2004.

9. Gonzalez, Rafael C., and Richard E. Woods. "Image processing."

Digital image processing 2 (2007).

10. Rehm, Kelly, et al. "Design and testing of artifact-suppressed

adaptive histogram equalization: A contrast-enhancement technique

for display of digital chest radiographs." Journal of thoracic imaging

5.1 (1990): 85-91.

11. Sivaramakrishna, Radhika, et al. "Comparing the performance of

mammographic enhancement algorithms: a preference study."

American Journal of Roentgenology 175.1 (2000): 45-51.

12. Pizer, Stephen M., et al. "Adaptive histogram equalization and its

variations." Computer vision, graphics, and image processing 39.3

(1987): 355-368.

13. W.H.Organization,World Health Organization Statistical

Information System,

14. URL: http://www.who.int/whosis/mort/en/index.html, 2006.

15. Bird, Richard E., Terry W. Wallace, and Bonnie C. Yankaskas.

"Analysis of cancers missed at screening mammography." Radiology

184.3 (1992): 613-617.

16. Burhenne, H. J., et al. "Interval breast cancers in the Screening

Mammography Program of British Columbia: analysis and

classification." AJR. American journal of roentgenology 162.5 (1994):

1067-1071.

17. Lee, San-Kan, et al. "A computer-aided design mammography

screening system for detection and classification of

microcalcifications." International journal of medical informatics 60.1

(2000): 29-57.

18. Nithya, R., and B. Santhi. "Mammogram analysis based on pixel

intensity mean features." Journal of computer science 8.3 (2012): 329.

19. Nithya, R., and B. Santhi. "Classification of normal and abnormal

patterns in digital mammograms for diagnosis of breast cancer."

International Journal of Computer Applications 28.6 (2011): 21-25.


1111

20. Jasmine, JS Leena, S. Baskaran, and A. Govardhan. "An automated

mass classification system in digital mammograms using contourlet

transform and support vector machine." International Journal of

Computer Applications 31.9 (2011): 54-60.

21. Suckling, John, et al. "The mammographic image analysis society

digital mammogram database." Exerpta Medica. International

Congress Series. Vol. 1069. 1994.

22. Nunes, Andre Pereira, Aristofanes Correa Silva, and Anselmo

Cardoso De Paiva. "Detection of masses in mammographic images

using geometry, Simpson's Diversity Index and SVM." International

Journal of Signal and Imaging Systems Engineering 3.1 (2010): 40-

51.

23. Costa, Daniel D., Lúcio F. Campos, and Allan K. Barros.

"Classification of breast tissue in mammograms using efficient

coding." Biomedical engineering online 10.1 (2011): 55.

24. Berbar, Mohamed A., Yaser A. Reyad, and Mohamed Hussain.

"Breast mass classification using statistical and local binary pattern

features." Information Visualisation (IV), 2012 16th International

Conference on. IEEE, 2012.

25. de Sousa Carvalho, Péterson Moraes, Anselmo Cardoso de Paiva, and

Aristófanes Corrêa Silva. "Classification of breast tissues in

mammographic images in mass and non-mass using Mcintosh’s

diversity index and SVM." International Workshop on Machine

Learning and Data Mining in Pattern Recognition. Springer Berlin

Heidelberg, 2012.

26. Hussain, Muhammad. "False positive reduction using Gabor feature

subset selection." Information Science and Applications (ICISA), 2013

International Conference on. IEEE, 2013.

27. Nguyen, M. P., et al. "An alternative approach to reduce massive

false positives in mammograms using block variance of local

coefficients features and support vector machine." Procedia Computer

Science 20 (2013): 399-405.

28. Moayedi, Fatemeh, et al. "Contourlet-based mammography mass

classification using the SVM family." Computers in biology and

medicine 40.4 (2010): 373-383.

29. Görgel, Pelin, Ahmet Sertbas, and Osman N. Ucan.

"Mammographical mass detection and classification using local seed

region growing–spherical wavelet transform (lsrg–swt) hybrid

scheme." Computers in biology and medicine 43.6 (2013): 765-774.

30. Chong, C-W., P. Raveendran, and R. Mukundan. "The scale

invariants of pseudo-Zernike moments." Pattern Analysis &

Applications 6.3 (2003): 176-184.


1112

31. Mukundan, Ramakrishnan, and K. R. Ramakrishnan. Moment

functions in image analysis—theory and applications. World

Scientific, 1998.

32. Gulsrud, Thor Ole, and John Hakon Husoy. "Optimal filter-based

detection of microcalcifications." IEEE transactions on biomedical

engineering 48.11 (2001): 1272-1281.

33. Lee, San-Kan, et al. "A computer-aided design mammography

screening system for detection and classification of

microcalcifications." International journal of medical informatics 60.1

(2000): 29-57.

34. Nakayama, Ryohei, et al. "Computer-aided diagnosis scheme using a

filter bank for detection of microcalcification clusters in

mammograms." IEEE Transactions on Biomedical engineering 53.2

(2006): 273-283.

35. Kavitha, K., and N. Kumaravel. "A Comparitive Study of Various

MicroCalcification Cluster Detection Methods in Digitized

Mammograms." Systems, Signals and Image Processing, 2007 and

6th EURASIP Conference focused on Speech and Image Processing,

36. Multimedia Communications and Services. 14th International

Workshop on. IEEE, 2007.

37. Halkiotis, Stelios, Taxiarchis Botsis, and Maria Rangoussi.

"Automatic detection of clustered microcalcifications in digital

mammograms using mathematical morphology and neural

networks." Signal Processing 87.7 (2007): 1559-1568.

38. Bhattacharya, Mahua, and Arpita Das. "Fuzzy logic based

segmentation of microcalcification in breast using digital

mammograms considering multiresolution." Machine Vision and

Image Processing Conference, 2007. IMVIP 2007. International.

IEEE, 2007.

39. Pal, Nikhil R., et al. "A multi-stage neural network aided system for

detection of microcalcifications in digitized mammograms."

Neurocomputing 71.13 (2008): 2625-2634.

40. Lakshmi, S. Venkata, J. Janet, and Thomas Bellarmine. "Analysis of

tone mapping operators on high dynamic range

images." Southeastcon, 2012 Proceedings of IEEE (2012): 1-6.

41. Harirchi, Farshad, et al. "Two-level algorithm for MCs detection in

mammograms using Diverse-Adaboost-SVM." Pattern Recognition

(ICPR), 2010 20th International Conference on. IEEE, 2010

42. Oliver, Arnau, et al. "Automatic microcalcification and cluster

detection for digital and digitised mammograms." Knowledge-Based

Systems 28 (2012): 68-75.

43. Zhang, Xinsheng, and Xinbo Gao. "Twin support vector machines and

subspace learning methods for microcalcification clusters detection."


1113

Engineering Applications of Artificial Intelligence 25.5 (2012): 1062-

1072.

44. Shen, Jun. "Orthogonal Gaussian-Hermite moments for image

characterization." Proc. SPIE, Intelligent Robots and Computer

Vision XVI: Algorithms Techniques, Active Vision, and Materials

Handling, Pittsburgh, USA (1997): 875-894.

45. Wang, Lin, Hongmin Suo, and Mo Dai. "Fingerprint image

segmentation based on gaussian-hermite moments." International

Conference on Advanced Data Mining and Applications. Springer

Berlin Heidelberg, 2005.

46. Wang, Lin, and Mo Dai. "An effective method for extracting singular

points in fingerprint images." AEU-International Journal of

Electronics and Communications 60.9 (2006): 671-676.

47. Wang, Lin, and Mo Dai. "Application of a new type of singular points

in fingerprint classification." Pattern recognition letters 28.13 (2007):

1640-1650.

48. Zheng, Xia, Mo Dai, and Mingquan Zhou. "Gauss-Hermite moments

application in medical image segmentation." Visual Information

Engineering, 2008. VIE 2008. 5th International Conference on. IET,

2008.

49. Punithavathani, D. Shalini, K. Sujatha, and J. Mark Jain.

"Surveillance of anomaly and misuse in critical networks to counter

insider threats using computational intelligence." Cluster Computing 18.1 (2015): 435-451..

50. Ma, Li, et al. "Local intensity variation analysis for iris recognition."

Pattern recognition 37.6 (2004): 1287-1298.


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Classification and segmentation methods in Digital mammog ... · However, the Microcalcification detection system is capable of finding the microcalcification, by extracting useful