Document Detail

Automatic detection of abnormal breast thermograms using asymmetry analysis of texture features.
MedLine Citation:
PMID:  23194447     Owner:  NLM     Status:  Publisher    
Thermography is a non-invasive imaging modality that represents surface temperature variations of the skin in the form of images called thermograms. The surface temperature around the area of cancerous cells is slightly higher than normal tissues and this area is seen as hot spots on thermograms. In normal breast thermograms, symmetric heat patterns are observed in both breasts, but in the case of unilateral abnormality, asymmetry is observed. As the intensity variations in thermograms represent surface temperature changes, texture features that would enhance thermal asymmetry, between right and left breasts, have been studied. The texture features are extracted from the breast region and fed to a back propagation neural network for automatic detection of abnormal breast thermograms. The classifier is able to classify abnormal and normal thermograms with an accuracy of 85.19%. From the results of the study, it is inferred that thermography has the potential to detect breast cancer and can be used as an adjunct tool to mammography.
Sheeja V Francis; M Sasikala
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-11-30
Journal Detail:
Title:  Journal of medical engineering & technology     Volume:  -     ISSN:  1464-522X     ISO Abbreviation:  J Med Eng Technol     Publication Date:  2012 Nov 
Date Detail:
Created Date:  2012-11-30     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  7702125     Medline TA:  J Med Eng Technol     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Department of Electronics and Communication Engineering, Anna University , Chennai-600025, Tamilnadu , India.
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