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Breast Cancer Lesion Detection From Cranial-Caudal View of Mammogram Images Using Statistical and Texture Features Extraction

Breast Cancer Lesion Detection From Cranial-Caudal View of Mammogram Images Using Statistical and Texture Features Extraction

Kavya N (Center for Imaging Technologies, M.S Ramaiah Institute of Technology, Bengaluru, India), Sriraam N (Center for Imaging Technologies, M.S Ramaiah Institute of Technology, Bengaluru, India), Usha N (Center for Imaging Technologies, M.S Ramaiah Institute of Technology, Bengaluru, India), Bharathi Hiremath (Dept of Surgery, M.S Ramaiah Medical College and Hospital, Bengaluru, India), Anusha Suresh (Dept of Radiology, M.S Ramaiah Medical College and Hospitals, Bengaluru, India), Sharath D (Center for Imaging Technologies, M.S Ramaiah Institute of Technology, Bengaluru, India), Venkatraman B (Health, Safety and Environmental Group, IGCAR Kalpakkam, India), and Menaka M (Health, Safety and Environmental Group, IGCAR Kalpakkam, India)
Copyright: © 2023 |Pages: 13
DOI: 10.4018/978-1-6684-7136-4.ch033
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Abstract

Breast cancer is the most common cancer among women in the world today. Mammography screening gives vital information about normal and abnormal regions. The task is to detect the lesion in mammograms using computer-aided diagnosis techniques. The automated detection of cancer decreases the mortality rate and manual error. In this work, the statistical (mean, variance, skewness, kurtosis, energy and entropy) and tamura features (coarseness, contrast and directionality) were extracted from the Cranial-Caudal (CC) view of mammogram images collected from the M.S. Ramaiah Memorial Hospital, Bangalore. The support vector machine was used for classification. Different support vector machine kernels were used and results were tabulated. The highest accuracy was obtained for linear and quadratic kernels with 95.7% with sensitivity of 100% and specificity of 91%.
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Background

Many research works have been carried out in the development of computer aided diagnostic techniques for the identification of abnormalities in mammogram images. The methods aimed at classifying the normal and abnormal mammogram images. Many studies have been carried out to show how the CAD techniques help in diagnosing the breast cancer. Different methods were tabulated in Table 1. Many researchers used publicly available database and some have collected from hospital and scanning centers. Texture features, shape features, clustering methods etc. have been used for feature extraction.

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