D05222528

IOSR Journal of Engineering (IOSRJEN) www.iosrjen.org
ISSN (e): 2250-3021, ISSN (p): 2278-8719
Vol. 05, Issue 02 (February. 2015), ||V2|| PP 25-28
International organization of Scientific Research 25 | P a g e
Implementation of Segmentation and Classification Techniques
for Mammogram Images
Neeta Jog 1
, Arvind Pandey 2
Lecturer , Medical Electronics, G.P.Miraj, Miraj, Maharashtra, India1
Professor, Computer Department, PVPIT College of Engg, Budhgaon, Maharashtra, India2
Abstract: Mammography is an important research field. Mammography Image classification is an area of
interest to most of the researchers today. The aim of this paper is to detect the Mammography image for its
malignancy. Different methods can be used to detect the malignancy. This paper represents GLDM andGabor
feature extraction methods along with SVM and K-NN classifiers.Experiments were conducted on MIAS
database. The results show that combination of GLDM feature extractor with SVM classifier is found to give
appropriate results.
Keywords: Mammography; image classification; GLDM; Gabor;SVM ;K-NN
I. INTRODUCTION
Cancer is uncontrolled growth of cells. Breast cancer is the uncontrolled growth of cells in the breast
region. Breast cancer is the second leading cause of cancer deaths in women today. Early detection of the cancer
can reduce mortality rate. Mammography has reported cancer detection rate of 70-90% which means 10-30% of
breast cancers are missed with mammography [1].Early detection of breast cancer can be achieved using Digital
Mammography, typically through detection of Characteristics masses and/or micro calcifications .A
mammogram is an x-ray of the breast tissue which is designed to identify abnormalities. Studies have shown
that radiologists can miss the detection of a significant proportion of abnormalities in addition to having high
rates of false positives .Therefore, it would be valuable to develop a computer aided method for mass/tumour
classification based on extracted features from the Region of Interest (ROI) in mammograms [3]. Pattern
recognition in image processing requires the extraction of features from regions of the image, and the processing
of these features with a pattern recognition algorithm. Features are nothing but observable patterns in the image
which gives some information about image. For every pattern classification problem, the most important stage is
Feature Extraction. The accuracy of the classification depends on the Feature Extraction stage. The motto
behind computer aided analysis is not to replace the Radiologists but to have a second opinion and thus provide
an efficient support in decision making process of the radiologist. Much research has been done in
mammography towards detecting one or more abnormal structures: circumscribed masses [5], speculated lesions
[6] and micro-calcifications [4].Other researchers have focused on classifying the breast lesions as benign or
malignant. There are different feature descriptors such as GLDM, (Gray Level Difference Method), LBP (Local
Binary Patterns), GLRLM(Grey level Run Length Method),Harralick, Gabor texture features and there are
classification methods such as SVM,C4.5,K-NN Classifier.
In this paper we have used a GLDM and Gabor feature extraction method over set of mammography images and
then tested their performance on SVM and K-NN classification algorithms.The paper is organised as follows
with section 2 gives explanation about the pre-processing stage where as section 3 describes the feature
extraction methods which are used in the experiment.Section 4 comes up with the overview of classification
methods.Section 5 provides explanation of experiments and brief discussion of results. Section 6 gives
conclusion derived from this work.
II. PREPROCESSING
Pre-processing stage is a step used to increase image quality of Mammograms as they are very difficult to
interpret .An histogram equalization can be used to adjust the image contrast so that anomalies can be better
emphasized.
III. FEATURE EXTRACTION
Feature extraction involves simplifying the amount of resources required to describe a large set of data
accurately. In image processing, a different set of features can be used to extract the visual information from a
given image. Because digital mammography images are specific, not all visual features can be used to correctly
describe the relevant image patch. All classes of suspected tissue are different by their shape and tissue
composition. This is why the most suitable visual feature descriptors for this kind of images are based on shape

Implementation of Segmentation and Classification Techniques for Mammogram Images
and texture. We can use different feature extraction methods and test them on variety of classifiers. We are
using GLDM feature descriptor.
GLDM
The GLDM method calculates the Gray level difference method Probability Density functions for the
given image. This technique is usually used for extracting statistical texture features of a digital mammogram.
From each density functions five texture features are defined: Contrast, Angular Second moment, Entropy,
Mean and Inverse Difference Moment.Contrast is defined as the difference in intensity between the highest and
lowest intensity levels in an image thus measures the local variations in the grey level. Angular second moment
is a measure of homogeneity. If the difference between gray levels over an area is low then those areas are said
to be having higher ASM values. Mean it gives the average intensity value. Entropy is the average information
per intensity source output. This parameter measures the disorder of an image. When the image is not texturally
uniform, entropy is very large. Entropy is strongly, but inversely, correlated to energy. Inverse difference
moment IDM measures the closeness of the distribution of elements in the Gray level Co-occurrence Matrix
(GLCM) to the GLCM diagonal. To describe the Gray level difference method, let g (n,m) be the digital picture
function. For any given displacement δ=(Δn, Δm),where Δn and Δm are integers, let gδ (n,m)=|g(n,m)-g(n+
Δn,m+ Δm)|. Let f(|δ) be the estimated probability density function associated with the possible values of gδ,ie,
f(i|δ)=P(gδ(n,m)=i herein our possible forms of vector δ will be considered,(0,d),(-d,d),(d,0),(-d,d),where d is
inter sample distance. we refer f(|δ) as gray level difference density function.
Gabor Texture Feature
Gabor texture feature is a linear filter .Basically, Gabor texture feature is a group of wavelets,with each
wavelet capturing energy at a capturing energy at a specific frequency and a specific direction. From this group
of energy distributions the texture feature representing the image can be extracted.Thus a set of Gabor filters
with different frequencies and orientations may be helpful for extracting useful features from an image. Gabor
filters have been widely used in pattern analysis applications.Frequency (scale) and orientation representations
of Gabor filters are similar to those of the human and mammalian visual system, and they have been found to be
particularly appropriate for texture analysis.
IV. CLASSIFICATION METHODS
There are innumerous classification methods for automated classification of samples. In this paper it’s decided
to work with most popular classification algorithm: SVM and K-NN
Support Vector Machines
The Support Vector machines were introduced by Vladimir Vapnik and colleagues. Support Vector
machines (SVM’s) are a relatively new learning method used for binary classification. The basic idea is to find a
hyper plane which separates the D-Dimensional data perfectly into its two classes. However, since example data
is often not linearly separable, SVM’s introduce the notion of a kernel induced feature space which casts the
data into a higher dimensional space where the data is separable. Namely, the primary goal of SVM classifiers is
classification of examples that belong to one of two possible classes.
However, SVM classifiers could be extended to be able to solve multiclass problems as well. One of the
strategies for adapting binary SVM classifiers for solving multiclass problems is one-against-all (OvA) scheme.
It includes decomposition of the M-class problem (M>2) into series of two-class problems. The basic concept is
to construct M SVMs where the i-th classifier is trained to separate the class i from all other (M-1) classes.
This strategy has a few advantages such as its precision, the possibility for easy implementation and the speed in
the training phase and the recognition process. That is reason for its wide use.
K-NN
In pattern recognition, the k-nearest neighbor algorithm (k-NN) is a method for classifying objects
based on closest training examples in the feature space. k-NN is a type of instance-based learning, or lazy
learning where the function is only approximated locally and all computation is deferred until classification. The
k-nearest neighbor algorithm is amongst the simplest of all machine learning algorithms: an object is classified
by a majority vote of its neighbors,with the object being assigned to the class most common amongst its k
nearest neighbors (k is a positive integer,typically small). If k = 1, then the object is simply assigned to the class
of its nearest neighbor. The training examples are vectors in a multidimensional feature space,each with a class
label. The training phase of the algorithm consists only of storing the feature vectors and class labels of the
training samples.In the classification phase, k is a user-defined constant, and an unlabeled vector (a query or test
point) is classified by assigning the label which is most frequent among the k training samples nearest to that
query point.

V. RESULT
TableI and fig1 depicts the classification precision of two classifiers in two class problem with the
GLDM descriptor.The results show that GLDM descriptor for various displacements with SVM classifier
provides the best classification accuracy of 95.83% .Thus as displacements in GLDM are increased we get the
best classification accuracy.In case of GLDM descriptor with the K-NN classifiers results seen are with
maximum accuracy of 50% which is not at par with the SVM and GLDM combination of results with 95.83%
.Thus we see the GLDM and SVM combination giving better results than GLDM and K-NN combination.
Sr.no. Displacement SVM K-NN
1 8 62.50 41.67
2 10 75.00 45.83
3 12 70.83 50.00
4 16 87.50 41.67
5 20 95.83 33.33
Table I.Classification precision for the two-class problem
Fig 1. Displacement Verses Percentage Accuracy
Table II and fig2 depicts the classification precision of two classifiers in two class problem with the Gabor
texture feature descriptor.The results show that Gabor texture feature descriptor for various orientations with
SVM classifier provides the best classification accuracy of 71.83% .Thus as orientations in Gabor are increased
we get the best classification accuracy. In case of Gabor texture feature descriptor with the K-NN classifiers
results seen are with maximum accuracy of 58.33% which is not at par with the SVM and Gabor texture feature
descriptor combination of results with 71.83% .Thus we see the Gabor and SVM combination giving better
results than Gabor and K-NN combination.
Sr.no. Orientation SVM K-NN
1 1 66.67 45.83
2 2 70.83 58.33
3 3 62.5 37.5
4 4 71.83 50.0
Table II.Classification precision for the two-class problem
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40
60
80
100
120
8 10 12 16 20
svm
knn

Fig 2. Orientation Verses Percentage Accuracy
VI. CONCLUSION
Digital mammography is the most common method or early breast cancer detection.Automated analysis of these
images is very important,since manual analysis of these images is slow.Today manual analysis of only eight
slides of mammography images per day is permitted for the radiologists it being very costly and inconsistent.
In this paper we made analysis on two classifiers,using two different descriptors for feature
extraction.According to the examination,we can conclude that the best classification accuracy was achieved,in
the case of GLDM descriptor.
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