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CHAPTER 2

A REVIEW ON BREAST ABNORMALITY

SEGMENTATION AND CLASSIFICATION TECHNIQUES

Segmentation or abnormality detection is the initial step in

mammographic Computer Aided Detection (CAD) system. The review on

different approaches to the segmentation and classification of mammographic

masses and microcalcifications are described in this chapter. It also describes

main features and differences of these approaches. The key objective is to

point out the advantages and disadvantages of these approaches.

2.1 INTRODUCTION

A segmentation algorithm is used to detect region of interest,

usually the part of breast or a specific kind of abnormalities like

microcalcifications or masses. It is generally known that the detection of

masses is technically difficult, because masses can be simulated or obscured

by normal breast parenchyma. Moreover, there is an outsized variability in

these lesions, which is reflected in the morphology features (shape and size of

the lesions). Variations exist in large number of features that have been used

to detect and classify them.

Microcalcifications are considered to be important indicators of

breast cancer. However, its interpretation is very difficult and 10% - 30% of

breast microcalcifications are missed during routine screening (Bird et al

1992, Burhenne et al 1994). It appears as tiny objects which can be described

14

as granular, linear, or irregular on mammograms. Although they have higher

inherent attenuation properties, they cannot be distinguished from the high

frequency noise because of their smaller size. The microcalcifications

typically range in size from 0.1 to 1.0 mm (Thor Ole Gulsrud and John Hakon

Husoy, 2001). Microcalcifications often appear in a heterogeneous

background describing the structure of the breast tissue. Some elements of the

background, like dense tissue, could also be brighter than the

microcalcifications in the fatty part of the breast. The Regions of Interest

(ROI’s) may be of low contrast. The intensity difference between suspicious

areas and their surrounding tissues can be quite slim. Dense tissues may be

easily misinterpreted as calcifications yielding a high False Positive (FP) rate,

which is a major problem with most of the algorithms.

In this chapter, lesion segmentation and classification algorithms

are reviewed in detail. In section 2.2, the algorithms which look for

segmentations of masses and microcalcifications using mammographic

images are described. Section 2.3 describes the literature study on

mammographic classification. The evaluation methodology is given in section

2.4. The summary of reviews is given in section 2.5.

2.2 BREAST ABNORMALITY SEGMENTATION

Segmentation of breast abnormality relies on the fact that pixels

inside a mass or microcalcifications have different characteristics from the

other pixels within the breast area. The characteristics used can be gray level

values, texture features, shape features or morphological features of the

lesions. The outcome of segmentation of image is a set of segments that

collectively cover the entire image, or a set of edges extracted from the image.

Each one of the pixels in a region is related with respect to some

characteristics or computed property, such as color, intensity, or texture.

15

Neighboring regions are considerably different with respect to the

characteristic(s).

In computer vision terminology, segmentation techniques can be

divided into unsupervised and supervised approaches. Supervised

segmentation, also known as model-based segmentation, relies on prior

knowledge about the object and background regions to be segmented. The

prior information is used to determine if specific regions are present within an

image or not. Alternatively, unsupervised segmentation partitions an image

into a set of regions which are distinct and uniform with respect to specific

properties, such as grey features, texture or colour features. Classical

methods used to solving unsupervised segmentation are divided in three major

groups (Fu and Mui, 1981). These are region based methods, contour based

methods and clustering methods. The rows of Table 2.1 shows the reviewed

works arranged according to their unsupervised segmentations. A detailed

description of the methods in each category is given in subsequent sections.

2.2.1 Region Based Methods

The main goal of segmentation is to partition an image into regions.

Region based segmentation relies on the principle of homogeneity, which means

there should be at least one feature which remains uniform for all pixels within a

region. The basic formulation for Region-based segmentation is:

a) 1

n

i iR R (2.1)

b) is a connected region, i=1,2,...,niR

c) iR for all i=1,2,...,njR (2.2)

d) ( ) for i=1,2,...,niP R TRUE (2.3)

e) i i jP(R ) for any adjacent region R and RjR FALSE (2.4)

16

where,

R – Region, n – number of pixels, - null set, ( )iP R - logical predicate.

More than 30 years have passed, since Zucker reviewed region

growing algorithms (Zucker 1976). Region growing is based on the

propagation of an initial seed point according to a specific homogeneity

criterion, iteratively increasing the size of the region. Since then, region

growing has seen a number of improvements, primarily due to the integration

of boundary information in the algorithm. Region growing algorithms have

been widely used in mammographic mass segmentation with the aim of

extracting the potential lesion from its background. Since early nineties,

researchers from the University of Chicago investigated the introduction of

shape information into the homogeneity criterion.

William Mark Morrow et al (1992) have developed an adaptive

method for enhancing the contrast of mammographic features of varying size

and shape. The method uses each pixel in the image as a seed to grow a

region. The size and shape of the region adapt to local image gray-level

variations, corresponding to an image feature. The contrast of every region is

calculated with respect to its individual background. Contrast is, then,

improved by applying an empirical transformation based on each region’s

seed pixel value, its dissimilarity, and its background.

With the aim to integrate the radiologist’s experiences, Huo et al

(1995) have developed a semi-automatic region growing approach. In this

approach, the growing step was automatically computed after a radiologist

had manually placed the seed point. Later, Matthew Kupinski and Maryellen

Giger (1998) have developed radial gradient index method and a probabilistic

method for segmentation of lesions. These methods are seeded segmentation

methods. In both methods, a series of image partitions is created using gray-

level information as well as prior knowledge of the shape of typical mass

lesions.

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Naga Mudigonda et al (2001) have developed a method for the

detection of masses in mammographic images. The method employs Gaussian

smoothing and sub sampling operations as initial processing steps. The mass

portions are segmented by establishing intensity links from the central

portions of masses into the surrounding areas. Chu et al (2002) have

proposed a region growing approach that represented as a growing tree whose

root is the selected seed. Active leaves are removed in the connection area

between adjacent regions to avoid merging adjacent structures. The authors

affirmed that this graph-based segmentation more closely matches

radiologists’ outlines of masses.

Jiang et al (2007) have developed a genetic algorithm to

automatically classify and detect microcalcification clusters. The genetic

algorithm technique is characterized by transforming input images into a

feature domain. Here, every pixel is represented by its mean and standard

deviation inside a surrounding window of size 9×9 pixel. In the feature

domain, chromosomes are constructed to populate the initial generation. The

features are extracted to enable the genetic algorithm to search for optimized

classification and detection of microcalcification.

Peter Filev et al (2008) have developed a computerized regional

registration and characterization system for analysis of microcalcification

clusters. The system consists of two stages. In the initial stage, a regional

registration procedure is used to identify the local area that may contain the

cluster. A search program is used to detect cluster candidates within the local

area. In the second stage, a temporal classifier is used to classify the region.

Alfonso et al (2009) have developed two methods called dynamic-

programming-based method and a constrained region-growing method to

segment the mass contours. The simplified versions of these contours were

employed to extract a set of six features designed for characterization of mass

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margins (contrast between foreground and background region, two measures

of the fuzziness of mass margins, coefficient of deviation of edge strength, a

measure of spiculation based on edge-signature information and a measure of

spiculation based on relative gradient orientation). Three accepted classifiers

(Fisher's linear discriminant, Bayesian classifier and a support vector

machine) were used to predict the lesions.

Claudio Marrocco et al (2010) have developed a novel system for

detection of clusters of microcalcifications. The mammogram first extracts the

elementary homogeneous regions of interest in this system. An analysis of

such regions is then performed by means of a two-stage, coarse-to-fine

categorization based on both heuristic rules and classifier combination. Giulia

Rabottino et al (2011) have developed a region growing technique to segment

the lesion region. The shape and texture features are extracted, and fuzzy

classifier is used to classify the lesion regions.

2.2.2 Contour Based Methods

Image segmentation techniques based on contour based method

have been used in the early work of Roberts (1965). Contour-based

approaches usually start with edge detection, followed by a linking process

that seeks to exploit curvilinear continuity. However, the identification of

regions based on the edge detection is far from trivial. The algorithms for

edge detection do not usually possess the ability of the human vision system

to complete interrupted edges. Therefore, sometimes edges which are not the

transition from one region to another are detected. The properly detected

edges often present gaps at places where the transitions between regions are

not abrupt enough. Hence, detected edges might not essentially form a set of

closed connected curves that surround distinct regions.

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The Table 2.1 shows number of publications is trying to detect

masses in mammogram using contour based methods. The algorithms for

finding edges are based on filtering the image in order to enhance relevant

edges prior to the detection stage. The location of edges, in Petrick et al (1996),

is based on a Gaussian–Laplacian edge detector, after which the image is

enhanced by an adaptive density-weighted contrast enhancement filter.

Judy Kilday et al (1993) have developed an interactive

segmentation procedure to identify the tumor boundary using a thresholding

technique. The several features are extracted based on the gross and fine

shape describing properties of the tumor boundaries. Joachim Dengler et al

(1993) have used a two-stage algorithm for spot detection and shape

extraction. The topology and the number of the spots are determined by using

weighted difference of Gaussians filter and the shape by means of

morphological filters. Parr et al (1994) used Gabor filters to locate the

spicules of stellated lesions.

Kobatake and Yoshinaga (1996) have described an approach, which

starts with a sub-image containing a possible mass lesion. It looks for spicules

using gradient information in three steps: First, the morphological line-

skeletons are extracted in order to detect long and thin anatomical structures

(like spicules). Second, a modified hough transform is used to extract lines

passing near the centre of the mass, and finally the algorithm automatically

select objects based on the number of line skeletons that satisfy the second step.

Fauci et al (2005) and Cascio et al (2006) have looked for the

contours of the mass using an iterative algorithm. At each local maxima a

threshold was selected which is used to draw an intensity contour. The

threshold value is based on user interaction and histogram information.

Consequently, the area of the selected region is refined by adjusting the

threshold. Stelios Halkiotis et al (2007) have used the mathematical

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morphology tools for the extraction of microcalcifications even if the

microcalcifications are located on a non-uniform background.

Table 2.1 Mammographic unsupervised segmentation techniques

Author Class

Features

Texture ShapeGray

LevelGradient

Region Based Methods

Huo et al (1995) Mass

Matthew Kupinski et al (1998) Mass

Mudigonda et al (2001) Mass

Peter Filev et al (2008) MC

Alfonso et al (2009) Mass

Claudio Marrocco et al (2010) MC

Giulia Rabottino et al (2011) Mass

Chu et al (2002) Mass

Contour-Based Methods

Joachim Dengler (1993) MC

Judy Kilday et al (1993) Mass

Parr et al (1994) Mass

Petrick et al (1996) Mass

Kobatake and Yoshinaga (1996) Mass

Fauci et al (2005) Mass

Cascio et al (2006) Mass

Stelios Halkiotis et al (2007) MC

Clustering Methods

Kobatake and Murakami (1996) Mass

Li et al (1995) Mass

Heng-Da cheng et al (1998) MC

Lei Zheng and Chan (2001) Mass

Wei Qian et al (2002) MC

Cao et al (2008) Mass

Suliga et al (2008) Mass

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2.2.3 Clustering Methods

Clustering methods are one of the most commonly used techniques

for image segmentation, as discussed in Jain et al (1999). The lesion detection

and segmentation can be seen from the reviewed approaches shown in

Table 2.1. Based on the work of Jain et al (1999), clustering techniques can be

divided into hierarchical and partitional algorithms. The hierarchical methods

produce a nested series of partitions, while partitional methods produce only a

single partition. The hierarchical methods can be more accurate even in small

data sets. The partitional methods are used in applications involving large

datasets, like the ones related to images.

The traditional partitional clustering algorithm is the K-means

algorithm (MacQueen 1967), which is characterized by simple

implementation and low complexity. The Fuzzy C-Means (FCM) algorithm

(Bezdek, 1981) is an extension of the K-means algorithm which allows each

pattern of the image to be associated with every cluster using a fuzzy

membership function. Chang wen chen et al (1998) have developed a robust

segmentation algorithm for three dimensional image data. The algorithm

based on a novel combination of adaptive K-means clustering and knowledge

based morphological operations.

Heng-Da Cheng et al (1998) have used a novel approach to

microcalcification detection based on fuzzy logic technique. In this approach,

microcalcifications are first enhanced based on their brightness and non-

uniformity. Then, the irrelevant breast structures are excluded by a curve

detector. Finally, microcalcifications are located using an iterative threshold

selection method. The shapes of microcalcifications are reconstructed and the

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isolated pixels are removed by employing the mathematical morphology

technique.

In contrast to fuzzt C-means which improves K-means using a

fuzzy approach of the energy function, the Dogs and Rabbit (DaR) algorithm

performs a more robust seed placement. The DaR was used by Lei Zheng and

Chan (2001) to obtain an initial set of regions which subsequently were used

to initialize a Markov Random Field (MRF) approach.

Andrew et al (2001) have proposed a method called discrete values

clustering algorithm with applications to bio-molecular data. Tapas Kanungo

et al (2002) have developed a simple and efficient implementation of

Lioyd’s K-means clustering algorithm for image analysis. Wei Qian et al

(2002) have developed a distance-based and dense-to-sparse grouping method

for detection of microcalcification clusters. The grouping result should be

independent of the size, shape and orientation of real clusters. The

application, namely cluster-oriented analysis including an adaptive

segmentation method and cluster level feature extraction scheme is employed.

Ng et al (2006) have developed a method for medical image segmentation

using K-means clustering and improved watershed algorithm.

Cao et al (2008) have investigated a robust information clustering

(RIC) algorithm incorporating spatial information for breast mass. The

detection system employs RIC algorithm based on the raw region of interest

extracted from global mammogram by two steps of adaptive thresholding.

Pixels on the fuzzy margin of a mass and noisy data were identified by RIC.

The identified pixels (outliers) were recalculated by incorporating spatial

distance information. It takes into account of the influence of a neighborhood

of 3x3 window. The nine texture features are extracted from the region of

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interest and SVM classifier is used to further classification of the region of

interest.

Suliga et al (2008) have developed a new pixel based clustering

method for the analysis of digital mammograms. The image pixels are

described only by their intensity (gray level). Therefore, the available

information is limited to one dimension. The Markov random field based

technique is suitable for performing clustering in an environment which is

described by poor or limited data. This method is a statistical classification

method that labels the image pixels based on the description of their statistical

and contextual information.

2.2.4 Model Based Methods

The model based segmentation methods initially train the system to

detect specific objects. Subsequently, the system has to be able to detect and

classify new images depending on the presence or absence of similar object.

The training system covers examples with and without the object (lesion)

present. Thus:

From mammograms containing a lesion, the system learns the

probable location and the variation in shape and size of the

lesion.

From mammograms not containing a lesion, the systems can

learn features that represent normal tissue.

Based on both training aspects, the system learns what features to

look for when presented with a new image. Table 2.2 shows publications

based on such strategy.

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One of the most commonly used model-based segmentation

methods is pattern matching. In pattern matching, the training is usually based

on images containing the object to detect. Pattern matching has been used in

segmentation of mammographic images by Lai et al (1989) and by

Constantinidis et al (2001). Freixenet et al (2008) have developed a model to

use a probabilistic template matching method to detect lesions. The shape and

deformations of a deformable template were learnt from real mass examples.

Subsequently, a Bayesian scheme was used to adapt the learnt deformable

template to the real contours of the mammogram. On the other hand,

Hatanaka et al (2001) effectively used the same approach to detect masses

with a partial loss of region, i.e. those masses located at the border of the

image or on the boundary between the pectoral muscle and the breast.

Tianhu Lei and Wilfred Sewchand (1992) have developed an

unsupervised stochastic model based image segmentation technique for X-ray

computed tomography image. This model utilizes the finite normal mixture

distribution and the underlying Gaussian random field as the stochastic image

model. The number of tissue classes in the observed image is detected by

information theoretic criteria. The parameters of the model are estimated by

expectation-maximization and classification-maximization algorithms. Image

segmentation is performed by Bayesian classifier.

Zhengrong Liang et al (1994) have developed a statistical

procedure to classify tissue types and to segment the corresponding tissue

regions. The strategy assumes that the distribution of image intensities related

with each tissue type can be expressed as a multivariate likelihood function.

The procedure, further assumes that the underlying tissue regions are

piecewise contiguous and can be characterized by a markov random field

prior. In classifying the tissue types, the strategy models, the likelihood of

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realizing the images as a finite multivariate-mixture function. The class

parameters associated with the tissue types are estimated by maximum

likelihood. The estimation fits the class parameters to the image data via

expectation-maximization algorithm. The number of classes related with the

tissue types is determined by the information criterion of minimum

description length. The procedure segments, the tissue regions, are given the

estimated class parameters, by maximum a posteriori probability.

One of the approaches using model based strategy was the work of

Nico Karssemeijer and te Brake (1996). They initially found spicules using

second order Gaussian derivatives operators. If a line-like structure is present

at a given site, the method provides an estimation of the orientation of these

structures, whereas in other cases the image noise will generate a random

orientation. With this information they constructed two new features that

formed the input for the classification stage.

Chang et al (1996) have developed a simple method for detecting

suspicious regions based on five rules. The selected regions should contain:

(1) a global maximum in a Gaussian smoothed image; (2) a local maximum in

the original image; (3) a local maximum in the image coming from the

subtraction of two smoothed images (one using a Gaussian filter and the other

using a box filter); and either (4) a small suspicious region of low contrast; or

(5) a small suspicious region of high contrast. There is a series of approaches

which model the masses using statistical approaches. For instance, Reyer

Zwiggelaar et al (1999) detected spiculated lesions as the union of two

techniques: the first one modeled the centre of the mass using a directional

recursive median filter, while the second technique modeled the surrounding

pattern of linear structures applying a multi-scale directional line detector.

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The combination of both the methods results in a probability image, and the

detection is performed by thresholding the resulting probability image.

Huai Li et al (2001) first applied an image enhancement algorithm

using morphological filtering. Subsequently, they employed a finite

generalized Gaussian mixture distribution to model the histogram. They

incorporated the EM algorithm to determine the optimal number of image

regions and the kernel shape in the finite generalized Gaussian mixture model.

The final step was the use of Bayesian relaxation labeling to perform the

selection of suspected masses. In a recent approach, Szekely et al (2006) used

a decision tree to classify a sliding window to contain mass or normal tissue.

Consequently, a markov random field is used to refine the obtained

segmentation.

Table 2.2 Mammographic model based segmentation techniques

Author

Features

Texture ShapeGray

LevelGradient

Lai et al (1989)

Nico Karssemeijer and te Brake (1996)

Chang et al (1996)

Reyer Zwiggelaar et al (1999)

Constantinidis et al (2001)

Hatanaka et al (2001)

Huai Li et al (2001)

Szekely et al (2006)

Oliver et al (2006)

Freixenet et al (2008)

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2.2.5 Other Segmentation Methods

Shuk-Me1 Lai et al (1989) have developed a method for detecting

circumscribed masses in mammograms. It relies on a combination of criteria

used by experts including the shape, brightness, contrast, and uniform density

of mass areas. The method uses modified median filtering to enhance

mammogram images and template matching to detect masses. In the template

matching step, suspicious areas are picked by thresholding the cross-

correlation values and a percentile method is used to determine a threshold for

each film. In addition, neighborhood test and histogram test are designed to

remove false alarms from the resulting candidates.

Nafi Gurcan et al (1997) have developed a new method for

detection of microcalcifications. In this method, the mammogram image is

first processed by a sub-band decomposition filter bank. The band pass sub-

image is divided into overlapping square regions in which skewness and

kurtosis as measures. The detection method utilizes these two parameters. A

region with high positive skewness and kurtosis is marked as a region of

interest.

Hidefumi Kobatake et al (1999) have developed for detection of

malignant tumors in digitized mammograms. The method uses iris filter to

enhance the images, and parameters based on boundary characteristics are

extracted to classify the malignant and other tumors. This iris filter can

enhance rounded convex regions such as tumors.

Reyer Zwiggelaar et al (1999) have developed a statistical models

to detect spiculated lesions. The model described a generic method of

representing patterns of linear structures, which relies on the use of factor

analysis to separate the systematic and random aspects of a class of patterns.

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The model consists of the appearance of central masses using local scale-

orientation signatures. It is based on recursive median filtering and

approximated using principal component analysis.

Giuseppe Boccignone et al (2000) have developed a new method

for computer aided detection of microcalcifications in digital mammograms.

The detection is performed on the wavelet transformed image. The

calcifications are separated from the background by exploiting the evaluation

of Renyi's information at the different decomposition levels of the wavelet

transform.

Reyer Zwiggelaar et al (2004) have described a method for

detecting linear structures in mammograms, and for classifying them into

anatomical types (vessels, spicules, ducts, etc). They described different

methods for extracting linear structures. Each method provides an estimate at

each pixel of both line-strength and orientation. In principle, these methods

can be used for detecting either dark or bright linear structures. They

investigated different approaches such as Line Operator, Orientated Bins,

Gaussian Derivatives and Ridge Detector for detecting linear structures.

Gaussian filtering and sub-sampling methods are used to enhance the images.

The line-strength and orientation images are used to extract a simple

representation of linear structure. Finally, they classified the linear structures

into vessels, spicules and ducts, etc.

Rafayah Mousa et al (2005) have developed two techniques based

on wavelet analysis and fuzzy-neural approaches for breast cancer diagnosis.

These techniques are mammography classifier based on globally processed

image and locally processed image (region of interest). The system is

classified normal from abnormal and mass or microcalcification. They have

29

investigated and analyzed wavelet transform for image enhancement, features

extraction and the adaptive neuro-fuzzy inference system algorithm for

classification process.

Ryohei Nakayama et al (2006) have developed a computerized

scheme for detecting early-stage microcalcification clusters. The system first

developed a novel filter bank based on the concept of the Hessian matrix. The

mammogram images were decomposed into several sub-images. The eight

features were extracted from each sub-image. The Bayes discriminant

function was employed for distinguishing normal and abnormal region.

Tomasz Arodz et al (2006) have developed a new computer-aided

detection system for small field digital mammography. The system first

processes the image using filter that is sensitive to microcalcification contrast

shape. Then, enhance the mammogram contrast by using wavelet-based

sharpening algorithm. Finally, the output image is presented to radiologist, for

visual analysis.

Guillaume Kom et al (2007) have developed a CAD system for

detection of masses in mammograms by local adaptive thresholding. In order

to enhance the image, a mass pattern-dependent enhancement approach was

designed based on the linear transformation of pixels values. This approach is

implemented by two linear functions. Finally, the mass area is segmented by

using local adaptive thresholding method.

Whi-Vin Oh et al (2009) have developed a CAD system for the

automatic detection of clustered microcalcifications in digitized

mammograms. The system consists of three main steps. First, the breast

region is segmented from original mammogram using contrast property of

30

grey level co-occurrence matrix. Second, the potential microcalcification

pixels in the mammograms are detected by foveal method. Third, in order to

reduce false-positive rate, the individual microcalcifications are detected by a

set of eight features extracted from the potential individual microcalcification

objects.

Mohamed Meselhy Eltoukhy et al (2010) have developed an

approach for breast cancer diagnosis in digital mammogram using curvelet

transform. After decomposing the mammogram images in curvelet basis, a

special set of the biggest coefficients is extracted as feature vector. The

Euclidean distance is then used to construct a supervised classifier.

Maciej Mazurowski et al (2011) have developed a CAD system for

mammographic masses that uses a mutual information-based template

matching scheme with intelligently selected templates. The system proceeds

in several steps. First, it starts with reducing the spatial resolution of the

original images to 0.4 mm per pixel. Second, a simple breast segmentation

algorithm is utilized based on global thresholding of the images to segment

the breast region. The global threshold is established separately for each

mammographic image via peak detection in the image histogram. Third, a

mask is created that includes the 30% highest intensity pixels within the

breast. The pixels within the mask are considered as suspicious enough in the

further steps. For each pixel within the mask, a likelihood score is calculated

of that pixel corresponding to an abnormality. Finally, iterative multi-level

thresholding algorithm is applied to extract the suspicious region.

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2.3 LITERATURE STUDY ON MAMMOGRAPHIC

CLASSIFICATION

Image classification analyzes the numerical properties of various

image features and organizes data into categories. Classification algorithms

typically employ two phases of processing: training and testing. The

characteristic properties of typical image features are isolated in training

phase. The feature-space partitions are used to classify image features in

testing phase.

Classifiers play a crucial role in the implementation of computer-

aided diagnosis of mammography. The features or a subset of features are

employed by classifiers to classify the suspicious regions into normal and

lesions. After the features have been extracted and selected, they are input

into a classifier to categorize the images into lesion/non-lesion or

benign/malignant classes. Majority of the publications focuses on classifying

malignant and benign lesions (usually called lesion classification), and some

of the articles focus on classifying lesions and non-lesions (usually called

lesion detection), and only a few of them focus on both. A detailed

description of the classification methods is given in subsequent section.

Artificial Neural Networks (ANN)

ANNs are the collection of mathematical models that imitate the

properties of biological nervous system and the functions of adaptive

biological learning. They are made of many processing elements that are

highly interconnected together with the weighted links that are similar to the

synapses. Unlike linear discriminants, ANNs usually use non-linear mapping

functions as decision boundaries. The advantages of ANNs are their

capability of self-learning, and often suitable to solve the problems that are

32

too complex to use the conventional techniques, or hard to find algorithmic

solutions.

It includes an input layer, an output layer and one or more hidden

layers between them. Depending on the weight values of ( , )j i and ( , )k j ,

the inputs are either amplified or weakened to obtain the solution in the best

way. The weights are determined by training the ANN using the known

samples.

Baoyu Zheng et al (1996) have developed a system for detection of

microcalcification using mixed feature neural networks. The features

computed in both the spatial and spectral domain and use spectral entropy as a

decision parameter. Back propagation with Kalman filtering is employed as

classifier.

Jong Kook Kim and Hyun Wook Park (1999) have developed a

texture-analysis method for detection of microcalcification. Textural features

are extracted by spatial gray-level dependence method, gray-level run-length

method, and the gray-level difference method. A three-layer back propagation

neural network is used as a classifier.

Wang et al (1998) have developed a probabilistic neural network

based technique for segmentation of brain tissues from magnetic resonance

images. The new technique uses suitable statistical models for both the pixel

and context images. It also formulates the problem in terms of model-

histogram fitting and global consistency labeling. The quantification is

achieved by probabilistic self-organizing mixtures and the segmentation by a

probabilistic constraint relaxation network.

33

Songyang Yu and Ling Guan (2000) have developed a computer-

aided diagnosis system for detection of clustered microcalcifications. The

system consists of two steps. First, the potential microcalcification pixels in

the mammograms are segmented out by using mixed features consisting of

wavelet features, gray level, and statistical features. Second, the individual

microcalcifications are detected by using a set of 31 features extracted from

the individual microcalcification objects. The discriminatory power of these

features is analyzed using general regression neural networks via sequential

forward and sequential backward selection methods. The classifiers used in

these two steps are both multilayer feed forward neural networks.

Paul Sajda et al (2002) have developed a Hierarchical

Pyramid/Neural Network (HPNN), that learns to exploit image structure at

multiple resolutions for detecting clinically significant features. Networks are

trained using a novel error function for the supervised learning of image

search, when the position of the objects to be found is uncertain or ill defined.

Christoyianni et al (2002) have implemented a CAD system

consists of a feature extraction module that extracts a gray level and texture

features from the regions of interest and a classification module based on a

Radial Basis Function Neural Network (RBFNN) classifier that can classify

any normal and abnormal region in digital mammograms.

Papadopoulos et al (2002) have developed a hybrid intelligent

system for the identification of microcalcification clusters in digital

mammograms. The reduction of false positive cases is performed using an

intelligent system containing two subsystems: a rule-based and a neural

network sub-system. In the first step of the classification, 22 features are

computed from individual or cluster of microcalcifications. Further reduction

in the number of features is achieved through principal component analysis.

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Cheng et al (2004) have used a new Fuzzy Neural Network (FNN)

approach to detect malignant mass lesions on mammograms. The FNN used

four layers. The first layer is the input layer consisting of four input fuzzy

neurons. The second layer has four ordinary neurons. The third layer consists

of N maximum fuzzy neurons. The number of fuzzy neurons in the third layer

is determined during the training process and varies with the network

parameters and data distribution. The fourth layer has two maximum fuzzy

neurons and one competitive fuzzy neuron.

Papadopoulos et al (2005) have developed a novel computer-based

automated method for the characterization of microcalcification clusters in

digitized mammograms. The system has been implemented in three stages: (a)

the cluster detection stage to identify clusters of microcalcifications, (b) the

feature extraction stage to compute the important features of each cluster and

(c) the classification stage, which provides with the final characterization. In

the classification stage, a rule-based system, an artificial neural network and a

support vector machine have been implemented and evaluated using receiver

operating characteristic analysis.

Celia Varela et al (2007) have investigated the behavior of an iris

filter at different scales. After iris filter was applied, suspicious regions were

segmented by means of an adaptive threshold. Suspected regions were

characterized with features based on the iris filter output and, gray level,

texture, contour-related, and morphological features extracted from the image.

A back propagation neural network classifier is trained to reduce the number

of false positives.

Pasquale et al (2007) have developed for the mass characterization

and is mainly based on a segmentation algorithm and sixteen features based

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on shape, size and intensity of the segmented masses are extracted. The

multi-layered perceptron neural network was used to train the system.

Stelios Halkiotis et al (2007) have developed a new algorithm for the

detection of clustered microcalcifications using mathematical morphology and

artificial neural networks. Mathematical morphology provides tools for the

extraction of microcalcifications even if the microcalcifications are located on a

non-uniform background. Each candidate object is marked as such, using a

binary image. The original mammogram is used for the final feature extraction.

The neural network classifier with multi-layer perception (MLP) and radial

basis function neural networks is employed to detect microcalcifications. The

gray level and statistical features are used as input vector.

Anna Karahaliou et al (2008) have investigated texture properties

of the tissue surrounding microcalcification clusters. The tissue surrounding

microcalcifications is defined on original and wavelet decomposed images,

based on a redundant discrete wavelet transform. Gray-level texture and

wavelet co-efficient texture features at three decomposition levels that are

extracted from surrounding tissue regions of interest. The ability of each

feature set in differentiating malignant from benign tissue is investigated

using a probabilistic neural network.

Brijesh Verma et al (2008) have used network architecture and a

new learning algorithm for the classification of mass abnormalities. The idea

is based on the introduction of an additional neuron in the hidden layer for

each output class. The additional neurons for benign and malignant classes

help in improving memorization ability without destroying the generalization

ability of the network. Brijesh Verma et al (2009) have developed a novel soft

cluster neural network technique for the classification of suspicious areas in

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digital mammograms. The technique introduces the concept of soft clusters

within a neural network layer and combines them with least squares for

optimizing neural network weights.

Sung-Nien Yu et al (2010) have investigated the performance of

clustered microcalcifications recognition in digital mammograms by using

combined model-based and statistical textural features. In the first stage, a

wavelet filter and two thresholds were used to detect suspicious

microcalcifications from the mammograms. In the second stage, textural

features based on Markov random field (MRF) and fractal models together

with statistical textural features were extracted from the suspicious MCs and

were classified by a three-layer back propagation neural network.

Jinchang Ren et al (2011) have developed a method for early

detection of breast cancer through classification of microcalcification clusters

from mammograms. The method used an improved neural classifier, in which

balanced learning with optimized decision making are introduced to enable

effective learning from imbalanced samples. In ANN classifier, the outputs

are continuous values rather than binary symbols. Conventional methods use

simple thresholding in decision making. If the outputs are larger than a chosen

threshold, a positive sample is detected. Otherwise, it is decided as negative.

However, this simple thresholding suffers uneven distribution of the training

outputs and leads to poor performance. To overcome this drawback, the

author used optimized decision making using optimal thresholding method.

Linear Discriminant Analysis (LDA)

LDA is a traditional method for classification. The main idea of this

method is to construct the decision boundaries directly by optimizing the error

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criterion to separate the classes of objects. If there are n classes, and linear

discriminant analysis classifies the observations as the following n linear

functions:

( ) . , 1 i n.T

i i ig x W x c (2.5)

where T

iW is the transpose of a coefficient vector, x is a feature vector and ic

is a constant as the threshold. The values of T

iW and ic are determined through

the analysis of a training set. Once the values of T

iW and ic are determined,

they can be used to classify the new observations. The observation is

abnormal, if ( )ig x is positive, otherwise it is normal.

Judy Kilday et al (1993) have developed a method for classification

of lesions using computerized image analysis. An interactive segmentation

procedure is used to identify the tumor boundary. The several features were

extracted based on shape describing properties of the tumor boundaries. The

LDA was used to select the features for classification.

Bruce et al (1999) have used the discrete wavelet transform

modulus-maxima (mod-max) method, and is utilized for the extraction of

mammographic mass shape features. These shape features are used in a

classification system to classify masses as round, nodular, or stellate. The

discriminating power of the shape features were analyzed via Linear

Discriminant Analysis (LDA). The classification system utilized a simple

Euclidean metric to determine class membership.

Naga Mudigonda et al (2001) have used gray level, shape and

gradient features as the input vector and LDA was used for pattern

classification. Sahiner et al (2001) have used two sets of texture,

morphological, and spiculation features as input vectors and a classifier based

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on stepwise feature selection and linear discriminant analysis was trained and

tested.

Bayesian Network

Bayesian network uses a probabilistic approach to determine an

optimal classification for a given database. It builds an “acyclic” graph in

which the nodes represent the features variables, and connections between

nodes represent direct probabilistic influences between the variables. Each

variable must have at least two discrete states and each state is associated with

a probability value. The total of the probability values for all states equals to 1

for each node. If there is no path between any two nodes, it indicates the

probabilistic independence of two variables.

Charles et al (1997) have used history of five patients, two physical

findings and fifteen mammographic features as input vector and Bayesian

network is used as classifier. Zheng et al (1999) have used Bayesian Belief

Network (BBN) in a computer-assisted diagnosis scheme for mass detection

in digitized mammograms. After initial processing of image segmentation and

adaptive topographic region growth in their scheme, 304 true-positive and

1,586 false-positive regions are identified in the testing set. A BBN was

constructed based on 12 local and four global features in order to classify

these regions as positive or negative for mass.

Sung-Nien Yu et al (2006) have developed a technique for

detection of microcalcifications. At first, all suspicious microcalcifications are

preserved by thresholding a filtered mammogram via a wavelet filter.

Subsequently, Markov random field parameters based on the Derin–Elliott

model are extracted from the neighborhood of every suspicious

microcalcification. The texture features are extracted and Bayesian classifier

was used for computer experiments.

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Imad Zyout et al (2009) have developed a new framework that

integrates Bayesian classifier and a pattern synthesizing scheme for detecting

microcalcification clusters. This frame work extracts textural, spectral, and

statistical features of each input mammogram and generates models of real

microcalcifications (MC) to be used as training samples through a simplified

learning phase of the Bayesian classifier. Followed by an estimation of the

classifier’s decision function parameters, a mammogram is segmented into the

identified targets (MCs) against healthy tissue.

Support Vector Machines (SVM)

Support Vector Machine is a supervised learning technique that

seeks an optimal hyperplane to separate two classes of samples. Kernel

functions are used to map the input data into a higher dimension space where

the data are supposed to have a better distribution, and then an optimal

separating hyperplane in the high-dimensional feature space is chosen.

Tomasz Arodz et al (2005) have used the Gabor filters for

extracting feature vectors from images and SVM had used as classifier.

Papadopoulos et al (2005) and Fu et al (2005) have used shape, texture and

gray level features as input vector and SVM is used for classification of

microcalcifications.

Jacob Levman et al (2008) have developed a CAD system for

classification of lesions on MR images. A key component of CAD system is

the selection of an appropriate classification function for separating malignant

and benign lesions. This study is to evaluate the effects of variations in

temporal feature vectors and kernel functions on the separation of malignant

and benign lesions by support vector machines. They also demonstrated the

support vector machine approach as offering significant flexibility in the

design of a computer aided-detection system.

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Defeng Wang et al (2009) have introduced a structured SVM model

to determine if each mammographic region is normal or cancerous by

considering the cluster structures in the training set. Various types of features,

including curvilinear features, texture features, Gabor features, and multi-

resolution features, are extracted from the sample images. Recursive feature

elimination algorithm is used to select the salient features.

Llado et al (2009) have developed method for detection and

classification of masses in mammogram. The key point of this method is the

use of local binary patterns for representing the textural properties of the

masses. Further, they extend the basic local binary patterns histogram

descriptor into a spatially enhanced histogram. It encodes both the local

region appearance and the spatial structure of the masses. Support vector

machines are then used for classifying the true masses from the ones being

actually normal parenchyma.

Subashini et al (2010) have developed a method for assessment of

breast tissue density in digital mammograms. Gray level thresholding and

connected component labeling is used to eliminate the artifacts and pectoral

muscles from the region of interest. Statistical features are extracted from

these regions which signify the important texture features of breast tissue.

These features are fed to the support vector machine (SVM) classifier to

classify it into any of the three classes namely fatty, glandular and dense

tissue.

Ioan Buciu and Alexandru Gacsadi (2011) have used the Gabor

wavelets to filter the images and directional features are extracted at different

orientation and frequencies. Principal Component Analysis is employed to

reduce the dimension of filtered and unfiltered high-dimensional data.

Proximal support vector machines are used to, finally, classify the data.

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Wener Borges Sampaio et al (2011) have developed a

computational methodology to detect breast masses. The first stage of the

methodology aims to improve the mammogram image. This stage consists in

removing objects outside the breast, reducing noise and highlighting the

internal structures of the breast. Next, cellular neural networks are used to

segment the regions that might contain masses. These regions have their

shapes analyzed through shape descriptors (eccentricity, circularity, density,

circular disproportion and circular density) and their textures analyzed

through geo-statistic functions. Support vector machines are used to classify

the candidate regions as masses or non-masses.

Stylianos Tzikopoulos et al (2011) have developed a segmentation

and classification scheme for mammograms, based on breast density

estimation and detection of asymmetry. First, image preprocessing and

segmentation techniques are applied. It includes breast boundary extraction

algorithm and pectoral muscle segmentation scheme. Features for breast

density categorization are extracted and support vector machines are

employed for classification.

Binary Decision Tree

A Binary Decision Tree recursively divides the feature space into

two subspaces by selecting a threshold to separate input data into two classes

each time. An ordered list of binary threshold operations on the features is

organized as a tree. Each node has a threshold associating with one or more

features to divide the data into its two descendents. The process stops when it

only contains patterns of one class. Comparing with neural networks, the

decision tree approach is much simpler and faster.

Li et al (1995) have developed a method called modified markov

random field for initial reliable segmentation of region of interest. Further, the

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regions are classified into suspicious and normal by a fuzzy binary decision

tree based on a series of radiographic and density-related features.

Sheng Liu et al (2001) have developed novel multiresolution

scheme for the detection of spiculated lesions in digital mammograms. First, a

multiresolution representation of the original mammogram is obtained using a

linear phase non-separable two-dimensional wavelet transform. A set of

features is then extracted at each resolution in the wavelet pyramid for every

pixel. This approach addresses the difficulty of predetermining the

neighborhood size for feature extraction to characterize objects that may

appear in different sizes. Detection is performed from the coarsest resolution

to the finest resolution using a binary tree classifier.

Lei Zheng and Andrew Chan (2001) have used the Dogs-and-

Rabbits clustering algorithm to initiate the segmentation at the Low Level

sub-band of a three-level discrete wavelet transform decomposition of the

mammogram. A binary tree-type classification strategy is applied at the end to

determine whether a given region is suspicious for cancer.

Other Classification Techniques

Nico Karssemeijer et al (1996) have developed a method for

detection of stellate distortions in mammograms. This method is based on

statistical analysis of a map of pixel orientations. Orientations of the image

intensity map are determined at each pixel using a multiscale approach.

Different classifiers such as K-nearest neighbor, decision tree, neural network

and Bayesian have been compared.

Subhash Bagui et al (2003) have developed a new generalization of

the rank nearest neighbor rule for multivariate data for diagnosis of breast

cancer. The several features such as radius (mean of distances from center to

43

points on perimeter), texture (standard deviation of gray-scale values),

perimeter, area, smoothness (local variation in radius lengths), compactness,

concavity (severity of concave portions of the contour, concave points

(number of concave portions of the contour), symmetry and fractal dimension

are used to classify the breast cancer.

Liyang Wei et al (2005) have used the machine-learning technique

called relevance vector machine for detection of microcalcifications. The

system formulated microcalcification detection as a supervised-learning

problem, and applied relevance vector machine as a classifier. A two-stage

classification network is applied to increase the computation speed.

Thangavel and Kaja Mohidee (2009) have developed a novel

association rule mining approach for classification of microcalcifications. In

this system, the shape features are extracted from the digital mammograms.

With these feature values, association rules are constructed to develop a rule

based system for classification of microcalcifications. A novel

Multidimensional Genetic Association Rule Miner is used for rule

construction.

2.4 EVALUATION METHODOLOGY

In mammography, the most common evaluation methodology is to

compare the results obtained by the algorithms to those obtained by a set of

experts, which is considered ground-truth.

Free Receiver Operating Characteristic (FROC) is based on a

region based analysis (Chakraborty et al 2007). The FROC paradigm is,

nowadays, being increasingly used in the assessment of medical imaging

systems, particularly in the evaluation and comparison of CAD systems

(Bornefalk 2005). It is a plot of operating points showing the tradeoff between

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the true positive rates versus the average number of false positives per image

(Egan et al 1961).

The FROC curve is the plot of true positive rate versus number of

false positives per image. Thus, FROC seeks location information from the

observer (CAD system), rewarding it when the reported disease is marked in

the appropriate location and penalizing it when it is not. This task is more

relevant to the clinical practice of radiology, where it is not only important to

identify disease, but also to offer further guidance regarding other

characteristics (such as location) of the disease. Each image or case can

contain any number of lesions. Each correctly located true-positive detection

and each false-positive location report is scored independently. The horizontal

axis cannot be normalized to range from 0.0 to 1.0, because the maximum

possible number of false-positives on each image or case is unknown. A

FROC curve is shown in Figure 2.1.

Figure 2.1 FROC curve (Source: www.devchakraborty.com)

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2.5 SUMMARY

This chapter presents and reviews different approaches to automatic

segmentation and classification of mammographic lesions. It describes several

algorithms, pointing out their specific features. It is clearly shown that a few

algorithms are contour based, probably due to the fact that lesions often have

not a definite one. Moreover, some region based and clustering algorithms

take shape, gray level or texture information into account to segment lesions.

Most of the model based algorithms require the use of a classifier which

implies training the system. The classifier based approach mainly

characterized by single or double features. In this case, algorithms used in the

literature obtained good detection results on one type of lesions, but it

generated unreasonable detection results on other types of lesions. Three or

more features used in classifiers seems to be an all-round lesion segmentation

approach which copes equally well with the small and the large lesions.