Temperature profile guided segmentation for detection of early subclinical inflammation in arthritis knee joints from thermal images

Abstract

In arthritis, subclinical inflammation referred to the clinical condition when rheumatologists are in confusion about the presence of inflammation using clinical and pathological observations. Application of Thermal imaging in detection of subclinical inflammation is highlighted in this literature. Segmentation of the hotspot area from the thermal image is the initial step for further analysis of the hotspot. Analysis of the hotspot will help in prediction of the subclinical inflammation, impact of inflammation. Methodologies reported in existing literature for segmentation of hotspot or inflamed knee region in medical thermal images suffer from over and under extraction.In the present scope, we try to overcome this limitation by extending the conventional region growing segmentation technique with stronger similarity criteria and stopping rule. In this method, hotspot or inflamed region is generated by taking the intersection of two independent regions produced by two different version of Region growing algorithm using a separate set of parameters. An automatic multiseed selection procedure ensures prevention of missed segmentation. We validate our technique by experimentation on various thermal image datasets like a newly created inflammatory thermal knee-joint-Database of 50 images, DBT-TU-JU Dataset, and DMR-IR Dataset. The effectiveness of the proposed technique is established compared to the performance of state-of-the-art competing methodologies.