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Tumour Classification in Graph-Cut Segmented Mammograms Using GLCM Features-Fed SVM

  • C. A. Ancy
  • Lekha S. Nair
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 695)

Abstract

Mammograms are customarily employed as one of the reliable computer-aided detection (CAD) techniques. We propose an efficient modified graph-cut (GC) segmented, grey-level co-occurrence matrix (GLCM)-based support vector machine (SVM) technique, for classification of tumour. In this work, SVM classification was carried out in single-view mammograms, subsequent to preprocessing, GC segmentation and GLCM feature extraction. Segmentation of pectoral muscles was done first, followed by segmentation of tumour, using kernel space mapped normalized GCs. We believe this process is the first of its kind used in mammograms. A suitably large number of features were extracted from GLCM, using Haralick method, which in turn increased the training efficiency. The proposed method was tested on 322 different mammograms from Mammographic Image Analysis Society (MIAS) and hence successfully verified to provide efficient results. High accuracy rates were achieved by combining best methods at each stage of diagnosis.

Keywords

Gamma expansion Normalized graph cuts Mammograms Haralick method GLCM SVM 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa VidyapeethamAmrita UniversityAmritapuriIndia

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