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Advanced image segmentation techniques for accurate isolation of abnormality to enhance breast cancer detection in digital mammographs

Vedanarayanan V1* and Nandhitha NM2

1Research Scholar, Sathyabama University, India

2Department of Electronics and Communication Engineering, Sathyabama University, India

*Corresponding Author:
Vedanarayanan V
Research Scholar, Sathyabama University, India

Accepted date: November 12, 2016

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Abstract

In recent years, extensive research is carried out in Computer Aided Interpretation of digital mammograms for breast cancer classification. Computer aided Interpretation of digital mammograms involves pre-processing, contrast enhancement, segmentation, appropriate feature extraction and classification. Though considerable research is carried out in developing contrast enhancement and image segmentation techniques, cancer regions could not be isolated and extracted efficiently. Also appropriate features which best describe the cancer characteristics were not found. Hence this work focuses on developing efficient image segmentation techniques for isolating the cancer region and also identifying suitable descriptors for describing the cancer region. Modified Expectation Maximization and modified snake algorithm are developed for isolating the abnormality. Area, Minor Axis Length, Major Axis Length, Perimeter, Orientation, Centroid, Eccentricity, EquivDiameter, Solidity and convex area are the features used for describing abnormality. Back Propagation Network is used for determining the presence and absence of cancer in mammograms. Sensitivity of the proposed techniques is 100%.

Keywords

Digital mammograms, Modified snake algorithm, Modified expectation maximization algorithm, Back propagation network

Introduction

Breast cancer is the most commonly occurring disease in women which proves fatal in most cases. In recent years, various diagnostic techniques are developed to identify breast cancer. These techniques include digital mammograms, Infrared thermographs (IR), Computed Tomography (CT), Positron Emission Tomography (PET) etc. [1]. Of all these techniques, digital mammograms are regarded as golden standard for breast cancer detection [2]. X-ray is passed through the region under observation and is detected on thin films at the other end. When X-Ray passes through cancer cells, it is absorbed and hence it appears as high intensity regions in mammograms. On the other hand, in the absence of cancer X-rays penetrate well and deep, hence that portion of the film appears dark or black. The task of radiologists is to analyse the radiographs and determine the intensity difference to identify cancer regions. Even to an expert radiologist, manual interpretation is difficult for low contrast radiographs. Hence, it is necessary to highlight the region of interest through various contrast enhancement techniques on the mammograms. Ankita et al. have done an extensive survey of different image enhancement methods for digital mammographs [3]. Image segmentation is to be performed to isolate the region of interest. Sukassini et al. had studied different segmentation techniques for digital mammographs in detail [4]. After isolating the region of interest; suitable descriptors are used to describe the cancer region. These features are used to generate exemplars which in turn can be used for training the BPN. Hence the research work aims at developing a suitable image enhancement and segmentation technique for isolating the cancer region completely.

This paper is organised as follows. Section 2 deals with research database and mammograph pre-processing. In section 3, the proposed segmentation techniques are explained. Six layered Back Propagation Network based classifier is discussed in section 4. Conclusion and future work is discussed in Section 5.

Research database and mammograph pre-processing

The steps involved in computer aided analysis of mammograms are mammograph acquisition, mammograph pre-processing, contrast enhancement, segmentation, quantitative characterization and classification [5]. In order to perform the research work a set of 50 mammograms were collected from various scan centres in Chennai, India. Of these mammograms, two are of normal persons and the remaining 48 mammograms depict cancer. Five mammograms which best describe normal and cancer conditions are shown in Figure 1.

biomedres-Five-mammographs

Figure 1. Five mammographs of normal and cancerous persons.

From the subjective analysis of these mammograms, it is found that cancer region appears has high intensity pixels in mammograms. Owing to the nature of human physiology few undesirable high intensity regions are also present in mammograms. Hence it is necessary to pre-process the radiographs. In order to reduce the computational complexity, the colour image is converted into grey scale image. After preprocessing the mammographs, contrast is enhanced using Gabor filter.

Advanced image segmentation techniques

On the contrast enhanced images, segmentation techniques are used to isolate the abnormality region [6]. In this work modified expectation Maximization technique and modified snake algorithm are used.

Modified expectation maximization method: Expectation maximization algorithm is widely used when the data set contains missing or hidden values. Most biological datasets have this kind of problem. This is due to the problems in the observations or collection of data’s. All kind of biological analysis is carried on the data’s obtained with limited test only. Expectation maximization algorithm involves in guessing an initial value and increasing the probability of acceptance of that initial parameter [7]. This is done in an iterative way. Maximization step is used to evaluate the parameter and if it is not good then discarded and a new guess is done. In contrast to the conventional EM technique, in improved EM, the mean of the missing value is estimated. This kind of statistical estimation helps in reducing the errors which occur in Markov models. The steps involved in Improved EM are shown below:

• Read the image and convert it to grey scale

• Calculate the upper and lower threshold values of the Enhanced image

• Adjust the mean value based on the threshold values

• Evaluate the no of clusters (E step)

• Update the cluster mean value (M step)

• Based on the clusters, the abnormality is segmented.

The original and the output images are shown in Figure 2. From the second column of Table 1, it is found that the abnormality regions are isolated accurately.

Image Area Major axis length Orientation Perimeter Centroid Extrema Convex area Solidity Eccentricity Equiv diameter Minor axis length
1_small.jpg 14162 183.2055 57.6488 587 209.2732 231.5 15165 0.9339 0.8319 134.2819 101.6591
2_small.jpg 13506 177.79 -86.2263 665 172.1331 168.5 14880 0.9077 0.8248 131.1349 100.5351
3_small.jpg 10644 756.7651 -85.3654 2230 115.5615 1.5 150076 0.0709 0.8149 116.4146 438.6437
4_small.jpg 10444 760.9611 -86.0597 2230 117.6051 1.5 150076 0.0696 0.8131 115.3157 443.0261
5_small.jpg 10937 765.1262 -87.6051 2220 115.4141 1.5 150076 0.0729 0.8133 118.006 445.1632
6_small.jpg 10686 756.6839 -84.7703 2220 116.4376 1.5 150076 0.0712 0.8145 116.6441 439.0256
7_small.jpg 10682 749.747 -84.2881 2220 119.2128 1.5 150076 0.0712 0.8075 116.6222 442.2759
8_small.jpg 24447 297.6198 67.1191 2220 236.1883 1.5 150076 0.7288 0.907 176.4281 125.3241
9_small.jpg 10407 769.2389 -87.0142 2220 216.8673 1.5 150076 0.0693 0.817 115.1113 443.6133
10_small.jpg 10725 769.9002 88.2337 2220 217.0283 1.5 150076 0.0715 0.8117 116.8567 449.6466
11_small.jpg 1054 124.6638 8.9493 327 38.1214 1.5 150076 0.4081 0.9746 36.6332 27.9239
12_small.jpg 10872 744.0258 -83.3116 2210 29.2201 1.5 150076 0.0724 0.8063 117.6548 440.069
13_small.jpg 1478 51.1846 11.1636 256 230.2463 1.5 150076 0.7549 0.4747 43.3803 45.0494
14_small.jpg 745 100.215 -16.5445 279 25.2148 1.5 150076 0.2884 0.9321 30.7988 36.2867
15_small.jpg 10688 772.4566 89.4908 2220 116.3498 1.5 150076 0.0712 0.8183 116.655 444.0151
16_small.jpg 11196 761.9056 -85.7378 2220 218.1693 1.5 150076 0.0746 0.8121 119.3951 444.5366
17_small.jpg 10991 756.1269 -85.2428 2210 219.5143 1.5 150076 0.0732 0.0732 118.297 440.3588
18_small.jpg 12377 772.5146 -80.797 2440 189.6908 1.5 150076 0.0825 0.7725 125.5344 490.5579
19_small.jpg 10318 769.9965 -85.8297 2220 212.6296 1.5 150076 0.0688 0.8156 114.618 445.5694
20_small.jpg 10461 754.2772 -84.6163 2220 219.5664 1.5 150076 0.0697 0.8135 115.4095 438.6398
21_small.jpg 9784 758.9153 78.2869 2060 39.9543 1.5 150076 0.0737 0.8505 111.6126 399.2046
22_small.jpg 11170 727.2664 -80.0906 2220 116.3659 1.5 150076 0.0744 0.8008 119.2564 435.5757
23_small.jpg 10715 761.108 -85.4885 2220 117.6103 1.5 150076 0.0714 0.8145 116.8022 441.5587
24_small.jpg 18565 691.2519 69.0644 3220 220.7975 1.5 150076 0.1237 0.7437 153.7455 462.1258
25_small.jpg 571 52.6903 -7.5053 169 1079 1.5 150076 0.5292 0.8501 26.9633 27.751

Table 1. Quantitative characterization of abnormality isolated by EM method.

biomedres-segmented-outputs

Figure 2. Original and segmented outputs (Improved EM method).

Modified snake algorithm: In order to reduce the computational complexity modified snake algorithm is used for segmenting the abnormality. In general snakes are used for identifying the abnormality of irregular shape. Snake algorithm uses an energy minimising function [8-12]. Snake algorithm is mostly widely used for segmentation of images which contains irregular shape objects. Snake algorithm uses an energy function. This function is a combination of two forces namely internal and external. The internal force is calculated from the shape of the region of interest and the external force is calculated from a higher knowledge about that image. The energy function focuses on three components namely Curvature, Continuity and image gradient. If the edges are smooth then the curvature value should be kept high. In contrast to the conventional snake algorithm, snakes are initially generated by using the histograms. Steps involved in the proposed algorithm are as follows: Initially from the colour image, the mask image is generated. After generating the mask image, it is applied on the original image and the dissimilarity matrix is generated. After a threshold value the snake adjusts itself to reflect the actual abnormality region. The original and the output images are shown in Figure 3. From the figures in the last column of Table 2, it is found that this technique also isolates the abnormality effectively.

Image Area Major Axis length Orientation Perimeter Centroid Extrema Convex area Solidity Eccentricity Equiv Diameter Minor Axis Length
1_small.jpg 2782 381.1786 -83.6799 1130 57.1736 0.5 37632 0.0739 0.8205 59.516 217.9055
2_small.jpg 4390 96.0545 -88.9618 269 85.8852 0.5 4564 0.9619 0.7841 74.7631 59.6162
3_small.jpg 2715 379.1589 -84.98 1110 58.3153 0.5 37632 0.0721 0.815 58.7949 219.7077
4_small.jpg 2744 381.3371 -86.4567 1110 59.9016 0.5 37632 0.0729 0.8108 59.1081 223.1942
5_small.jpg 3343 384.9321 78.1146 1180 73.5965 0.5 37632 0.0888 0.7686 65.2414 246.2714
6_small.jpg 2702 379.6748 -84.7985 1110 58.5848 0.5 37632 0.0718 0.8158 58.654 219.5654
7_small.jpg 3047 385.895 87.2854 1140 68.7939 0.5 37632 0.081 0.7837 62.2861 239.6949
8_small.jpg 9478 161.303 74.8697 459 119.2717 0.5 10906 0.8691 0.8429 109.8534 86.7846
9_small.jpg 4230 96.3049 -70.9107 280 28.918 0.5 4501 0.9398 0.7887 73.388 59.2063
10_small.jpg 5058 349.3489 -64.4684 1300 71.123 0.5 37632 0.1344 0.7307 80.2499 238.483
11_small.jpg 7618 124.1984 76.8212 374 96.6533 0.5 8191 0.93 0.756 98.4862 81.2948
12_small.jpg 9137 160.5529 58.741 449 112.257 0.5 10191 0.8966 0.8724 107.8591 78.4716
13_small.jpg 4045 91.9671 -55.8968 261 106.308 0.5 4255 0.9506 0.7849 71.7653 56.9841
14_small.jpg 3723 95.6584 86.5118 254 117.6068 0.5 3860 0.9645 0.8494 68.8496 50.4768
15_small.jpg 4988 105.6116 81.4447 291 136.9493 0.5 5268 0.9468 0.8035 79.6927 62.878
16_small.jpg 6377 305.4289 -60.2024 1310 62.0182 0.5 37632 0.1695 0.6957 90.108 219.4082
17_small.jpg 4338 96.2214 55.7126 263 103.7937 0.5 4466 0.9713 0.7928 74.319 58.6471
18_small.jpg 15128 251.6818 -88.3735 780 50.2688 0.5 37632 0.402 0.7101 138.7861 177.2085
19_small.jpg 11209 168.7713 89.31 448 36.3406 0.5 11309 0.9912 0.8461 119.4644 89.9691
20_small.jpg 2902 372.6751 -83.9865 1110 114.1082 0.5 37632 0.0771 0.8109 60.786 218.0975
21_small.jpg 2908 381.1175 86.1361 1110 110.5519 0.5 37632 0.0773 0.8076 60.8488 224.7676
22_small.jpg 2915 360.3139 -80.5463 1110 58.5674 0.5 37632 0.0775 0.7902 60.922 220.8233
23_small.jpg 16137 188.9875 88.4375 521 122.0525 0.5 16260 0.9924 0.7989 143.3397 113.6575
24_small.jpg 19470 240.0864 79.177 780 112.3087 0.5 37632 0.5174 0.734 157.4483 163.0669
25_small.jpg 168 193.9897 0 334 84.5 0.5 168 1 1 14.6255 1.1547

Table 2. Quantitative characterization of abnormality isolated by Modified snake method.

biomedres-Modified-snake-method

Figure 3. Original and segmented outputs (Modified snake method).

In both cases, abnormality is described by Area, Minor Axis Length, Major Axis Length, Perimeter, Orientation, Centroid, Eccentricity, EquivDiameter, solidity and Convex area. These descriptors are shown in Tables 1 and 2 for improved EM and Modified snake algorithm [13,14]. These parameters are used as input parameters for generating the exemplars to train the neural network.

BPN based classifier: Back propagation network (BPN) is a kind Multi-layer Artificial Neural network which tries to mimic the human decision making process. Back propagation neural network is a supervised training algorithm [15-17]. Back Propagation Network (BPN) uses Gradient descent function which is also known as squashing function to reduce the training error. Back propagation network is much faster than conventional perceptron network. This network uses iterative differentiable activation function so that the error is squashed or reduced to a minimum value. At this stage the desired output matches with the actual output of the network. It has three phases. They are [18] feed forward [19] Error calculation and Error back propagation process [20] weight updating. BPN is used widely for its mathematical simplicity. A six layered Back Propagation Network is used for classification. Number of neurons in the input layer is 11 and one neuron is used at the output layer. Three hidden layers with 22, 11 and 5 neurons are used. “Tansigmoidal” and “purelinear” are the activation functions used at the hidden and the output layers respectively. Learning and momentum parameters are 0.1 and 0.6 respectively. Two different datasets are used for training and testing the neural network. Sensitivity is calculated for exemplars obtained from two segmentation techniques. Relationship between the desired and the actual values are shown in Table 3. From the Table 3, it is found that the sensitivity is 100% for both the segmentation techniques [21].

S. No Desired Actual values for improved EM method Actual values for improved snake method
1 1 1 1
2 1 1 1
3 1 1 1
4 1 1 1
5 1 1 1
6 1 1 1
7 1 1 1
8 1 1 1
9 1 1 1
10 1 1 1
11 1 1 1
12 0 0 0

Table 3. Relationship between the desired and the actual values.

Conclusion and Future work

Two different advanced image segmentation techniques namely improved EM and modified snake algorithm are developed successfully. Region and boundary descriptors are used for quantitatively characterizing the abnormality. These descriptors are used for generating exemplars to train and test the neural network. Based on trained data, the proposed six layered architecture has successfully classified the abnormality from the digital mammographs. Sensitivity of the classifier is 100% for both the two different set of exemplars.

Acknowledgement

The authors acknowledge the various scan centres in Chennai- India which provided the real time images for their research work.

References