Accurate segmentation of inflammatory and abnormal regions using medical thermal imagery
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Methodologies reported in the existing literature for identification of a region of interest (ROI) in medical thermograms suffer from over- and under-extraction of the abnormal and/or inflammatory region, thereby causing inaccurate diagnoses of the spread of an abnormality. We overcome this limitation by exploiting the advantages of a logarithmic transformation. Our algorithm extends the conventional region growing segmentation technique with a modified similarity criteria and a stopping rule. In this method, the ROI is generated by taking common information from two independent regions produced by two different versions of a region-growing algorithm that use different parameters. An automatic multi-seed selection procedure prevents missed segmentations in the proposed approach. We validate our technique by experimentation on various thermal images of the inflammation of affected knees and abnormal breasts. The images were obtained from three databases, namely the Knee joint dataset, the DBT-TU-JU dataset, and the DMR-IR dataset. The superiority of the proposed technique is established by comparison to the performance of state-of-the-art competing methodologies. This study performed temperature emitted inflammatory area segmentation on thermal images of knees and breasts. The proposed segmentation method is of potential value in thermal image processing applications that require expediency and automation.
KeywordsHotspot detection Inflammation Region growing Thermal imaging
The research work was supported by the Grant No. 5/7/1516/2016-RCH Dated: 20/06/2017 from the Indian Council of Medical Research (ICMR), Government of India.
Compliance with ethical standards
Conflict of interest
The authors declare there is no potential conflict of interestwith respect to the authorship and/or publication of this article.
Ref. No.4(6-11)-AGMC/Medical Education/Ethics Com/2018/15136, Dated 31st December, 2018.
Informed consent was obtained from all individual participants included in the study.
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