Mammographic Images Using Anfis Biology Essay

Published:

Breast cancer is one of the leading cancers in woman worldwide both in developed and developing nations as per the records from World Health Organization WHO. Many studies have shown that mammography is very effective for the breast cancer diagnosis. Mass detection poses a big challenge in detection because of its varying shape and density. Mass segmentation plays an important step for the cancer detection. Notable researches were done and still moving towards the effective detection of masses in mammograms. In this paper a novel segmentation of mass and its classification using Adaptive Neuro Fuzzy Inference System (ANFIS) is proposed. The segmentation includes two main steps. First, a rough initial segmentation through iterative thresholding, and second, an active contour based segmentation. The relevant statistical features are extracted and the classification is done by using ANFIS, which yields an accuracy of 91.30%.

Keywords

Mammography, Mass segmentation, Iterative thresholding, Active contour, ANFIS.

Introduction

Lady using a tablet
Lady using a tablet

Professional

Essay Writers

Lady Using Tablet

Get your grade
or your money back

using our Essay Writing Service!

Essay Writing Service

Over the past two decades, cancer has been one of the biggest threats to human life and it is expected to become the leading cause of death over the next few decades. There is no way of preventing cancer but can be examined early.

A mammogram is an X-ray of the breast. One can diagnose at very early stage and hence there is a chance for healthy survival. Breast cancer can be traced from mammogram before it can be felt. When mammography is combined with clinical breast exam the chances for finding cancer are even greater. For women with dense breast tissue, digital mammography may be more accurate than standard mammography. There are many other breast imaging tests [5] that can provide valuable information, however, these find difficult to tell the difference between dense breast tissue, benign (non-cancerous) lumps and cancer. And, sometimes they miss tiny calcium deposits that are the earliest sign of a tumor. Thus mammogram plays as a vital tool in breast cancer detection and identification [10]. Masses are defined as space occupying lesions that are described by their shape and marginal properties. A benign is characterized by smooth margination, whereas a malignancy is characterized by an indistinct border that becomes more spiculated with time. Many researches were done to segment the mass in the mammograms [9], [8], [13], [6]. Of which Active contour based segmentation provides the best way to segment the images whose background and foreground that are statistically different and homogeneous [7]. The contour based segmentation retains the original information of region, edge and shape of the mass [11], [6]. The region based active contour [4], [1] model holds good than the edge based contour model [11], [14]. Considering all the above advantages of the region based active contour, the segmentation technique combines the classical segmentation technique with the region based active contour. The rest of the paper is organized as follows. Section II details about the proposed method of mass segmentation and classification. Section III discusses about the statistical features extracted from the segmented images. Result and discussion are drawn in section IV. Conclusion is produced in section V.

Proposed Method of Mass Classification

Mammographic images are collected from the Imaging center, Coimbatore and from Mammographic Image Analysis Society (MIAS). The images are subjected to preprocessing which includes cropping (256 X 256) and enhanced by performing histogram equalization. Then the preprocessed image is subjected to iterative active contour based segmentation [15]. From the segmented image Gray Level Co-occurrence Matrix (GLCM) is formulated. Features are extracted from GLCM. These features are fed to the ANFIS for classification.

Classification using ANFIS

Mammogram Image

Feature Extraction

Pre-processing

Region Based Active Contour

Iterative Thresholding

Figure 1: Block Diagram of the Proposed Mass Segmentation and Classification

The block diagram of the proposed iterative active contour based segmentation and classification is shown in figure 1. The result obtained in various stages of preprocessing are depicted in figure 2.

(a) (b) (c)

Figure 2: (a) Original Image, (b) Cropped Image and (c) Histogram Equalized Image

The preprocessed image and the iterative thresholded image are shown in figure 3 (a) and (b) respectively. The average of the maximum pixel and minimum pixel value is set as initial threshold (T1). The value above threshold is set as foreground and below the threshold value as background. Average value of foreground and background is obtained (T2). If the value of T1 is equal to T2, T1 is the final threshold obtained. If T1 is not equal to T2, then T2 is assigned with T1.

Lady using a tablet
Lady using a tablet

Comprehensive

Writing Services

Lady Using Tablet

Plagiarism-free
Always on Time

Marked to Standard

Order Now

(a) (b)

Figure 3: (a) Preprocessed Image, (b) Iterative Thresholded Image

The segmentation technique involves initialization of the mask. The square mask of the size 111 x 111 is defined within the mass. This mask is allowed to deform until the minimum energy is encountered. The binary segmented image is obtained. This is compared with the gray scale preprocessed image and then the gray level segmented image is obtained. Figure 4 shows the results obtained in various stages of segmentation.

Figure 4: Image Results Obtained in Various Stages of Mass Segmentation

This method of segmentation is preferred as it holds good when compared to the existing techniques like Fuzzy C Means based segmentation and Level set based segmentation [15].

Feature Extraction

From the gray level segmented image shown in Figure 4, GLCM are formulated. Four matrices are formulated for,, and orientations. The average of the above four matrix is formulated as mean GLCM. From the formulated GLCM sixteen statistical features are extracted and are listed in table 1. From these 16 features the optimal features are obtained by calculating the variance and the features with higher value of variance are selected and are shown in table 2. These include Contrast, Dissimilarity, Sum average, Sum variance and Correlation [2].

Table 1: Feature Selection from GLCM

Feature Reduction

Feature Extracted from GLCM

Normal

Mdb123

(x)

Benign

Mdb69

(y)

Malignant

Mdb267

(z)

Mean

M=(x+y+z)/3

Variance

((M-x)+(M-y)+(M-z))/3

Contrast

0.3806

0.4526

0.0732

0.2988

0.18

Correlation

-1.3026

-1.2566

-1.2613

-1.2735

0.0194

Dissimilarity

0.0568

0.0709

1.0459

0.3904

0.4378

Energy

0.5347

0.4869

0.4844

0.502

0.01228

Entropy

-0.4232

-0.3958

-0.3939

-0.4043

0.0117

Homogeneity

0.9922

0.9894

0.9869

0.9895

0.0054

Maximum Probability

0.6438

0.4977

0.5036

0.5483

0.0636

Sum average

9.0698

6.9194

7.1435

7.7109

1.0859

Sum variance

120.6634

90.7037

93.4084

101.5918

12.7143

Sum entropy

0.3298

0.4143

0.4213

0.3884

0.039

Difference variance

0.3806

0.4526

0.6032

0.4758

0.0789

Difference entropy

0.0618

0.0866

0.0990

0.0824

0.0138

Information measure of correlation

0.9844

0.9868

0.9867

0.9859

0.0010

Inverse difference normalized

0.9961

0.9950

0.9936

0.9949

0.0008

Inverse difference moment normalized

0.9966

0.9959

0.9945

0.9956

0.0005

Auto correlation

41.6196

31.8959

32.8250

35.4478

4.1145

Table 2: Sample Features Extracted

Image Category

Features

Normal

mdb047

Benign

mdb025

Malignant

mdb264

Contrast

0.3658

0.3125

0.7847

Dissimilarity

0.0550

0.0446

0.1202

Sum average

7.4156

7.0528

7.1306

Sum variance

97.8043

93.1580

92.7942

Auto correlation

34.1819

32.5812

32.6761

Result and Discussion

The statistical features are extracted from 61 images. Table 3 illustrates the number of images considered for training and testing. Number of training samples considered are 39. For testing 22 samples are considered. Three triangular membership functions are used. The testing error for the triangular membership function is 0.4603 which is lesser when compared with other membership functions (0.5701-trapeziodal, 1.1941-gbell, 0.8864-gaussian, and 1.483-gaussian2). Total number of rules framed is 729. The classification result obtained through ANFIS is shown in the figure 5.

. Testing data

* FIS output

Figure 5: ANFIS-Classification Plot

Table 3: Classification Result of ANFIS

Image category

No. of training images

No. of testing images

Error

Normal

16

7

0

Benign

13

8

2

Malignant

10

7

0

With the information available from table 3 sensitivity, specificity and overall accuracy of the proposed classification system are calculated using the equations (1), (2), (3) and it is tabulated in table 4.

Sensitivity: X 100 (1)

Specificity: X 100 (2)

Accuracy: X 100 (3)

Table 4: Performance Metrics

Metrics

Value (%)

Sensitivity

100

Specificity

87.5

Accuracy (Overall)

91.30

The sensitivity obtained is 100%, the specificity obtained is 87.5% and the overall classification accuracy obtained through ANFIS is 91.30%.

Conclusion

In this paper a new computer aided mass segmentation and classification scheme is proposed. Five statistical features were selected for the classification of mass. The classification is done by using ANFIS. The implementation of the proposed method was carried out using MATLAB software. Result shows that the suggested features can give acceptable accuracy for the classification of mass. The result shows that the overall accuracy is 91.30%. With increase in the number of test samples the accuracy may still be improved to a great extent.

Lady using a tablet
Lady using a tablet

This Essay is

a Student's Work

Lady Using Tablet

This essay has been submitted by a student. This is not an example of the work written by our professional essay writers.

Examples of our work