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Diversified Insulin-Associated Beta-Behavioral and Endogenously Triggered Exposed Symptoms (DIABETES) Model of Diabetes in India Read chapter Read first chapter

2015 | OriginalPaper | Chapter

A Sparse-modeled ROI for GLAM Construction in Image Classification Problems—A Case Study of Breast Cancer

Authors : K. Karteeka Pavan, Ch. Srinivasa Rao

Published in: Computational Intelligence Techniques for Comparative Genomics

Publisher: Springer Singapore

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Abstract

Image segmentation is a process to determine regions of interest (ROI) in mammograms. Mammograms can be classified by extracting textural features of ROI using Gray Level Aura Matrices (GLAM). Scientists are selecting a fixed window size for all ROIs to find respective GLAM, though the masses will not occur in regular two-dimensional geometries. This paper makes an attempt to replicate the problem but by choosing arbitrary shape of masses as they occur. It is found that this kind of natural selection of the arbitrary shape yielded drastic reduction in time complexity by adopting the method suggested by us.

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Metadata
Title
A Sparse-modeled ROI for GLAM Construction in Image Classification Problems—A Case Study of Breast Cancer
Authors
K. Karteeka Pavan
Ch. Srinivasa Rao
Copyright Year
2015
Publisher
Springer Singapore
Book
Computational Intelligence Techniques for Comparative Genomics

Print ISBN: 978-981-287-337-8

Electronic ISBN: 978-981-287-338-5

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-981-287-338-5_4

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