In vivo automated quantification of thermally damaged human tissue using polarization sensitive optical coherence tomography. (March 2018)

• Record Type: Journal Article
• Authors: Dubey, Kavita; Srivastava, Vishal; Dalal, Krishna
• Is Part Of: Computerized medical imaging and graphics. Volume 64(2018)
• Page Start: 22
• Page End: 28
• Publication Date: 2018-03
• Subjects: Optical coherence tomography -- Machine learning -- Thermal damaged tissue (human skin); Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2018.01.002
• Languages: English
• ISSNs: 0895-6111

Abstract

Abstract: Over the decades numerous technologies have been performed for the quantification of skin injuries, but their poor sensitivity, specificity and accuracy limits their applications. Optical coherence tomography (OCT) can be potential tool for the identification but the modern high-speed OCT system acquires huge amount of data, which will be very time-consuming and tedious process for human interpretation. Our proposed method opens the possibility of fully automated quantitative analysis based on morphological features of thermally damaged tissue, which will become biomarker for the removal of non-viable skin. The proposed method is based on multi-level ensemble classifier by dissociating morphological features (A-line, B-scan, phase images) extracted from Polarization Sensitive Optical Coherence Tomography (PS-OCT) images. Our proposed classifier attained the average sensitivity, specificity and accuracy is 92.22%, 87.2% and 92.5%, respectively, in detecting the thermally damaged human skin. Moreover, we show that our classifier is one of the best possible classifier based on features extracted from PS-OCT images, which demonstrates the significance of PS-OCT data in detecting abnormality in human skin.