Classification of Digital Mammograms Into Masses and Non-Masses using Texture Combination Features and SVM
Sumit Chopra1, V. K. Banga2
1Sumit Chopra*, Ph. D Scholar, IKG PTU Kapurthala.
2Dr. V.K.Banga, Principal and Professor(ECE), ACET, Amritsar, Punjab, India.
Manuscript received on October 12, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 1750-1758 | Volume-9 Issue-1, November 2019. | Retrieval Number: J94070881019/2019©BEIESP | DOI: 10.35940/ijitee.J9407.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper presents a framework for grouping of mammogram images into carcinogenic and non- carcinogenic images. In this paper, image is enhanced using nonlinear contrast enhancement algorithm and then segmented using internal and external mask segmentation. The features used in the proposed work are based on the texture feature which means that the segmentation results will have minimal impact on the classification results of mammograms into masses and non – masses. The features used are Contrast, Energy, Homogeneity, Entropy, Mean Intensity, Standard Deviation, Taxonomic distinctness, Taxonomic distance and learning takes place by Support Vector Machine. The algorithm was applied on Mammographic Image Analysis Society database and when compared with contemporary techniques , the results were improved.
Keywords: Mammograms, Content Based Image Retrieval (CBIR), Support Vector Machine(SVM), Segmentation.
Scope of the Article: Classification