Mammogram Classification Using Gray-Level Co-occurrence Matrix for Diagnosis of Breast Cancer

Breast cancer is one of the most common forms of cancer in women worldwide. Most cases of breast cancer can be prevented through screening programs aimed at detecting abnormal tissue. So, early detection and diagnosis is the best way to cure breast cancer to decrease the mortality rate. Computer Aided Diagnosis (CAD) system provides an alternative tool to the radiologist for the screening and diagnosis of breast cancer. In this paper, an automated CAD system is proposed to classify the breast tissues as normal or abnormal. Artifacts are removed using ROI extraction process and noise has been removed by the 2D median filter. Contrast-Limited Adaptive Histogram Equalization (CLAHE) algorithm is used to improve the appearance of the image. The texture features are extracted using Gray Level Co-occurrence Matrix (GLCM) of the region of interest (ROI) of a mammogram. The standard Mammographic Image Analysis Society (MIAS) database images are considered for the evaluation. K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Artificial Neural Network (ANN) are used as classifiers. For each classifier, the performance factor such as sensitivity, specificity and accuracy are computed. It is observed that the proposed scheme with 3NN classifier outperforms SVM and ANN by giving 95% accuracy, 100% sensitivity and 90% specificity to classify mammogram images as normal or abnormal.

[1]  L Puneeth,et al.  Classification of Mammograms using Texture Features , 2014 .

[2]  Rabi Narayan Panda,et al.  Efficient CAD system based on GLCM & derived feature for diagnosing Breast Cancer , 2015 .

[3]  N. Pradeep,et al.  Segmentation and Feature Extraction of Tumors from Digital Mammograms , 2012 .

[4]  Indirajith Selvamani and G. Tholkappia Arasu Computer Aided System for Detection and Classification of Breast Cancer , 2015 .

[5]  Ian W. Ricketts,et al.  The Mammographic Image Analysis Society digital mammogram database , 1994 .

[6]  Carlos J. García-Orellana,et al.  Detection and classification of masses in mammograms using ICA , 2013 .

[7]  Esmat Rashedi,et al.  Detection of cancer tumors in mammography images using support vector machine and mixed gravitational search algorithm , 2016, 2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC).

[8]  V Gowri,et al.  Segmentation of Mammogram Using Tumor-Cut Algorithm , 2013 .

[9]  C. Rekha,et al.  Approaches For Automated Detection And Classification Of Masses In Mammograms , 2014 .

[10]  K.Malathi and R.Nedunchelian COMPARISION OF VARIOUS NOISES AND FILTERS FOR FUNDUS IMAGES USING PRE-PROCESSING TECHNIQUES , 2014 .

[11]  Biswajit Pathak,et al.  TEXTURE ANALYSIS BASED ON THEGRAY-LEVEL CO-OCCURRENCEMATRIX CONSIDERING POSSIBLEORIENTATIONS , 2013 .

[12]  R Nithya,et al.  Classification of Normal and Abnormal Patterns in Digital Mammograms for Diagnosis of Breast Cancer , 2011 .

[13]  K. VAIDEHI,et al.  AN INTELLIGENT CONTENT BASED IMAGE RETRIEVAL SYSTEM FOR MAMMOGRAM IMAGE ANALYSIS , 2015 .

[14]  L. Tabár,et al.  Mammography and breast cancer: the new era , 2003, International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics.

[15]  Karel J. Zuiderveld,et al.  Contrast Limited Adaptive Histogram Equalization , 1994, Graphics Gems.

[16]  S. Deepa S. Deepa,et al.  Textural Feature Extraction and Classification of Mammogram Images using CCCM and PNN , 2013 .

[17]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[18]  Leen-Kiat Soh,et al.  Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices , 1999, IEEE Trans. Geosci. Remote. Sens..