Breast abnormality detection through statistical feature analysis using infrared thermograms

Abstract

The utilization of medical infrared thermography in breast abnormality detection is mostly due to its radiation-free, non-invasive and painless nature. Infrared breast thermography is an alternative breast imaging modality that can detect those tumors or early changes which are undetectable by the gold standard method X-ray mammography. However, breast cancer is a highly treatable disease, with 97% chances of survival if getting detected earlier. Thus, early detection of breast cancer using infrared breast thermography may improve the survival rate of breast cancer patients. The temperature pattern in both breasts of a healthy breast thermogram is closely symmetrical. Hence, a small asymmetry in the temperature pattern of the left and right breast may signify a breast abnormality. There are a series of texture features that play a vital role in asymmetry analysis of breast thermograms. This paper mainly emphasizes on investigating those statistical features, which can adequately differentiate the healthy breast thermograms from pathological breast thermograms. A survey work on texture features used by various authors for asymmetry detection is provided in this work. Our analysis is performed on 30 healthy and 30 abnormal breast thermograms of existing DMR (Database for Mastology Research) Database. The analysis and experimental results show that among the first order statistical features, the mean difference, skewness, entropy and standard deviation are the most efficient features that contribute most towards the asymmetry detection.