Role of normalization of breast thermogram images and automatic classification of breast cancer

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3 Citations (Scopus)

Abstract

Breast thermography is a non-invasive imaging technique used for early detection of breast cancer based on temperatures. Temperature matrix of breast provides minute variations in temperatures, which is significant in early detection of breast cancer. The minimum, maximum temperatures and the their range may be different for each breast thermogram. Normalization of temperature matrices of breast thermograms is essential to bring the different range of temperatures to the common scale. In this article, we demonstrate the importance of temperature matrix normalization of breast thermograms. This paper also proposes a novel method for automatically classifying breast thermogram images using local energy features of wavelet sub-bands. A significant subset of features is selected by a random subset feature selection (RSFS) and genetic algorithm. Features selected by RSFS method are found to be relevant in detection of asymmetry between right and left breast. We have obtained an accuracy of 91%, sensitivity 87.23% and specificity 94.34% using SVM Gaussian classifier for normalized breast thermograms. Accuracy of classification between a set of hundred normalized and corresponding set of non-normalized breast thermograms are compared. An increase in accuracy of 16% is obtained for normalized breast thermograms in comparison with non-normalized breast thermograms.

Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalVisual Computer
DOIs
Publication statusPublished - 20-10-2019

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Temperature
Set theory
Feature extraction
Classifiers
Genetic algorithms
Imaging techniques

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

Cite this

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title = "Role of normalization of breast thermogram images and automatic classification of breast cancer",
abstract = "Breast thermography is a non-invasive imaging technique used for early detection of breast cancer based on temperatures. Temperature matrix of breast provides minute variations in temperatures, which is significant in early detection of breast cancer. The minimum, maximum temperatures and the their range may be different for each breast thermogram. Normalization of temperature matrices of breast thermograms is essential to bring the different range of temperatures to the common scale. In this article, we demonstrate the importance of temperature matrix normalization of breast thermograms. This paper also proposes a novel method for automatically classifying breast thermogram images using local energy features of wavelet sub-bands. A significant subset of features is selected by a random subset feature selection (RSFS) and genetic algorithm. Features selected by RSFS method are found to be relevant in detection of asymmetry between right and left breast. We have obtained an accuracy of 91{\%}, sensitivity 87.23{\%} and specificity 94.34{\%} using SVM Gaussian classifier for normalized breast thermograms. Accuracy of classification between a set of hundred normalized and corresponding set of non-normalized breast thermograms are compared. An increase in accuracy of 16{\%} is obtained for normalized breast thermograms in comparison with non-normalized breast thermograms.",
author = "Dayakshini Sathish and Surekha Kamath and Keerthana Prasad and Rajagopal Kadavigere",
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AU - Kamath, Surekha

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AU - Kadavigere, Rajagopal

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N2 - Breast thermography is a non-invasive imaging technique used for early detection of breast cancer based on temperatures. Temperature matrix of breast provides minute variations in temperatures, which is significant in early detection of breast cancer. The minimum, maximum temperatures and the their range may be different for each breast thermogram. Normalization of temperature matrices of breast thermograms is essential to bring the different range of temperatures to the common scale. In this article, we demonstrate the importance of temperature matrix normalization of breast thermograms. This paper also proposes a novel method for automatically classifying breast thermogram images using local energy features of wavelet sub-bands. A significant subset of features is selected by a random subset feature selection (RSFS) and genetic algorithm. Features selected by RSFS method are found to be relevant in detection of asymmetry between right and left breast. We have obtained an accuracy of 91%, sensitivity 87.23% and specificity 94.34% using SVM Gaussian classifier for normalized breast thermograms. Accuracy of classification between a set of hundred normalized and corresponding set of non-normalized breast thermograms are compared. An increase in accuracy of 16% is obtained for normalized breast thermograms in comparison with non-normalized breast thermograms.

AB - Breast thermography is a non-invasive imaging technique used for early detection of breast cancer based on temperatures. Temperature matrix of breast provides minute variations in temperatures, which is significant in early detection of breast cancer. The minimum, maximum temperatures and the their range may be different for each breast thermogram. Normalization of temperature matrices of breast thermograms is essential to bring the different range of temperatures to the common scale. In this article, we demonstrate the importance of temperature matrix normalization of breast thermograms. This paper also proposes a novel method for automatically classifying breast thermogram images using local energy features of wavelet sub-bands. A significant subset of features is selected by a random subset feature selection (RSFS) and genetic algorithm. Features selected by RSFS method are found to be relevant in detection of asymmetry between right and left breast. We have obtained an accuracy of 91%, sensitivity 87.23% and specificity 94.34% using SVM Gaussian classifier for normalized breast thermograms. Accuracy of classification between a set of hundred normalized and corresponding set of non-normalized breast thermograms are compared. An increase in accuracy of 16% is obtained for normalized breast thermograms in comparison with non-normalized breast thermograms.

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