Flash News
Welcome to IARC

Publisher Login

Latest News
Welcome to IARC- JCR Report

Submit your Journal to get IARC-JCRR Indexing and Impact Factor
 

Impact Factor calculated by IARC on the basis of Journal Citation Reference (JCR) Report.

 

Contact: iarcdpi@gmail.com

 

ABNORMALITY DETECTION IN BREAST THERMAL IMAGES WHEN APPLIED TO A TEMEPRATURE CHANGE
G.Bhavani Bharathi*, S. Chitra, M. Lavanya

Published in: International Journal of Scientific Review and Research in Engineering and Technology
Volume- 2, Issue-1, pp.123-131, Dec 2016
DPI :-> 16.10069.IJSRRET.2016.V2I1.123131.1367



Abstract
Breast cancer is the most commonly diagnosed form of cancer in women. Early diagnosis of Breast cancer is the key to improve survival rate. Due to the potential hazards of conventional breast imaging modalities, alternate techniques such as breast thermography are being evaluated. Breast thermography is a non-invasive diagnostic procedure that images the breasts to aid in the early detection of breast cancer. Breast thermal image is a visual representation of skin surface temperature of the breast. Abnormality detection by conventional breast thermography is less accurate as the imaging is done in a limited number of views. Also the segmentation of breast region from the chest wall is difficult. Hence classification of a breast thermogram as normal or abnormal is a challenging task by direct visual interpretation and image processing techniques as well. Rotational thermo mammography overcomes these difficulties as the imaging protocol incorporates complete imaging of each breast and application of external cold stress. This paper features an exhaustive analysis on spatial domain image features for automatic detection of breast abnormality in rotational infrared breast thermograms from the perspective of cold stress. Statistical features and Haralick texture features are extracted and fed to the support vector machine for automatic classification of normal and abnormal breast conditions.

Key-Words / Index Term
Rotational Breast Thermography, cold stress, Haralick Texture features, statistical features, Support Vector Machine (SVM).

How to cite this article
G.Bhavani Bharathi*, S. Chitra, M. Lavanya , “ABNORMALITY DETECTION IN BREAST THERMAL IMAGES WHEN APPLIED TO A TEMEPRATURE CHANGE”, International Journal of Scientific Review and Research in Engineering and Technology, 2, Issue-1, pp.123-131, Dec 2016. DPI:16.10069.IJSRRET.V2.I1.1367