S. Prabha* Pages 1 - 8 ( 8 )
Breast cancer is the second leading cause of cancer death among women preceded by cervix cancer. It has been reported that at the early stage of detection there is 85% of chance of getting cured whereas only 10% of chance at later stage diagnosis. The current screening modalities are expensive, intricate imaging measures and unhealthy from radiation exposure. Therefore, a screening tool that is non-invasive, no connection with body, free from radiation such as Medical Thermography are necessary. It is reported that the sensitive and specificity of medical thermography is high largely in dense breast tissues The clinical interpretation primarily depends on the asymmetrical analysis of these thermograms subjectively. The appearance of an asymmetric thermal image may indicate the pathological conditions. For earlier detection of breast cancer, it is essential to identify the advanced methods in image processing techniques which enhance the diagnostics significance. In that analysis, the required breast region is unglued from the background image. The segmented image is separated into symmetrical left and right breast tissues. The statistical and histogram features extracted from both the regions are used to identify the abnormal thermograms using machine learning algorithms. From literature, it is reported that the thermal images are inherently low contrast images and have low single to noise ratio. Moreover, they are amorphous in nature and no clear edges are seen. The difficulty lies in the detection of lower breast boundaries and inframammary folds. So, in general the first attempt is made in improving the signal to noise ratio and contrast of the image which helps to extract the true regions of breast tissues. Then, asymmetry analysis of the normal and abnormal breast tissues is performed using different techniques. This work demonstrates the review of few image processing methods or the development which are elaborated in the detection of breast cancer from thermal images.
Breast Thermography, Segmentation, Feature Extraction, Medical images and Breast cancer
Department of ECE, Hindustan Institute of Technology and Science, Chennai