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http://dx.doi.org/10.5391/IJFIS.2015.15.3.180

Automatic Intelligent Asymmetry Detection Using Digital Infrared Imaging with K-Means Clustering  

Kim, Kwang Baek (Department of Computer Engineering, Silla University)
Song, Doo Hoen (Department of Computer Games, Songdam College, Yongin)
Publication Information
International Journal of Fuzzy Logic and Intelligent Systems / v.15, no.3, 2015 , pp. 180-185 More about this Journal
Abstract
Digital infrared thermal imaging is a non-invasive adjunctive diagnostic technique that allows an examiner to visualize and quantify changes in skin surface temperature. The asymmetry of temperature differences between the diseased and the contralateral healthy body parts can be automatically analyzed and has been studied in many areas of medical science. In this paper, we propose a method for intelligent automatic asymmetry detection based on a K-means analysis and a YCbCr color model. The implemented software successfully visualizes an asymmetric distribution of colors with respect to the patients’ health status.
Keywords
Digital infrared thermal imaging; YCbCr color model; K-means clustering; Asymmetry analysis; Object labeling;
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