2 Citations (Scopus)

Abstract

Breast cancer is the leading cancer in women worldwide. Early detection can reduce the mortality rate of breast cancer. Breast thermography is a non-invasive and simple imaging technique used for early detection of breast cancer. Feature extraction and selection of appropriate features play a major role in computer-aided detection of breast cancer using breast thermograms. In this article, texture features are extracted from automatically segmented breast thermograms by computing neighbourhood grey tone difference matrix (NGTDM) and run length matrix (RLM). Significance of these features in differentiating the abnormal breast from the normal breast is found by statistical test. NGTDM extracted coarseness, busyness, complexity, strength and RLM extracted long run emphasis and run percentage are found to be significant by statistical test. Extracted features are computationally less expensive and attained an average accuracy of 80%, sensitivity of 94% and specificity of 71.4% using back propagation neural network classifier.

Original languageEnglish
Pages (from-to)104-118
Number of pages15
JournalInternational Journal of Bioinformatics Research and Applications
Volume14
Issue number1-2
DOIs
Publication statusPublished - 01-01-2018

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Breast
Textures
Breast Neoplasms
Statistical tests
Feature extraction
Backpropagation
Classifiers
Early Detection of Cancer
Neural networks
Imaging techniques
Sensitivity and Specificity
Mortality
Neoplasms

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Health Informatics
  • Clinical Biochemistry
  • Health Information Management

Cite this

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abstract = "Breast cancer is the leading cancer in women worldwide. Early detection can reduce the mortality rate of breast cancer. Breast thermography is a non-invasive and simple imaging technique used for early detection of breast cancer. Feature extraction and selection of appropriate features play a major role in computer-aided detection of breast cancer using breast thermograms. In this article, texture features are extracted from automatically segmented breast thermograms by computing neighbourhood grey tone difference matrix (NGTDM) and run length matrix (RLM). Significance of these features in differentiating the abnormal breast from the normal breast is found by statistical test. NGTDM extracted coarseness, busyness, complexity, strength and RLM extracted long run emphasis and run percentage are found to be significant by statistical test. Extracted features are computationally less expensive and attained an average accuracy of 80{\%}, sensitivity of 94{\%} and specificity of 71.4{\%} using back propagation neural network classifier.",
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AU - Kamath, Surekha

AU - Prasad, Keerthana

AU - Kadavigere, Rajagopal

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