Hybrid intelligent techniques for segmentation of breast thermograms

Abstract

The incidence of breast cancer has rapidly increased over the past few decades in India and the mortality rate is more than other countries across the entire world. These facts have motivated the development of new technologies or modification of the existing technologies for the identification of breast cancer before it metastasizes to the neighboring tissues. Breast thermography is a promising frontline breast screening method, which is noncontact, cheap, quick, economic, and painless. The use of thermal imaging for the identification of breast abnormality is based on the principle that the temperature distribution in precancerous tissue and its surrounding area are always higher than that in normal breast tissue. However, the accurate interpretation and classification of the breast thermograms for proper diagnostic decision-making is a major problem. Proper segmentation of hottest region from the segmented breast region plays a key part in the diagnosis of breast cancer that calls for the application of hybrid intelligent methods in the segmentation of hottest region. The shape and size of the hottest regions are used to determine the degree of malignancy of the tumor and classify its type. Hybrid intelligent systems have been successfully applied in the classification of breast thermal images over the last few years. In this chapter, we have proposed a sequential hybrid intelligent technique for the segmentation of the hottest region and also shown the significance of hybrid intelligence systems over the conventional methods for the segmentation of hottest region. A detailed review related to the segmentation of breast region and the segmentation ofhottest region is included in this chapter. In addition, this chapter also contains the detailed overview of the principles, reliability, and predictive ability of the breast thermogram in early diagnosis of breast cancer.