MMSHRs: a morphology model of suspicious hyperthermic regions for degree of severity prediction from breast thermograms

The presence of suspicious hyperthermic regions (SHRs) in breast thermograms is a prominent indicator of breast pathology, for which delineation and analysis of SHRs have a crucial role in early detection of breast abnormalities. A novel approach for breast abnormality grading, namely the morphology model of suspicious hyperthermic regions (MMSHRs), is proposed here. The proposed model first segments SHRs from breast-thermograms and then analyzes their morphology to grade the thermograms according to their degree of severity. To segment SHRs, a simple but effective method that computes the similarity score of each pixel with the highest intensity value is designed. . The performance of the proposed segmentation method is tested on both public and in-house-captured datasets. With the optimal values of seven evaluation metrics, the proposed segmentation method outperforms other state-of-the-art segmentation methods. The values of evaluation metrics further justify that the proposed SHRs segmentation method addresses all the limitations regarding infrared breast thermogram segmentation, and reduces the under-segmentation and over-segmentation of SHRs. Following segmentation of SHRs, the MMSHRs extract the corresponding morphological features, allowing the classification of thermograms into mild and severely abnormal with the classification accuracy of 91% and area under the receiver operating characteristic curve of .9998.

Automated summary

This paper proposes a novel approach for grading breast abnormalities using a morphology model of suspicious hyperthermic regions (MMSHRs). The method segments and analyzes the morphology of suspicious hyperthermic regions in breast thermograms to classify the thermograms according to their severity. The classification accuracy of the method is 91% and the area under the receiver operating characteristic curve is 0.9998.