15 July 2013 Computer-aided diagnosis of rheumatoid arthritis with optical tomography, Part 2: image classification
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J. of Biomedical Optics, 18(7), 076002 (2013). doi:10.1117/1.JBO.18.7.076002
This is the second part of a two-part paper on the application of computer-aided diagnosis to diffuse optical tomography (DOT) for diagnosing rheumatoid arthritis (RA). A comprehensive analysis of techniques for the classification of DOT images of proximal interphalangeal joints of subjects with and without RA is presented. A method for extracting heuristic features from DOT images was presented in Part 1. The ability of five classification algorithms to accurately label each DOT image as belonging to a subject with or without RA is analyzed here. The algorithms of interest are the k -nearest-neighbors, linear and quadratic discriminant analysis, self-organizing maps, and support vector machines (SVM). With a polynomial SVM classifier, we achieve 100.0% sensitivity and 97.8% specificity. Lower bounds for these results (at 95.0% confidence level) are 96.4% and 93.8%, respectively. Image features most predictive of RA are from the spatial variation of optical properties and the absolute range in feature values. The optimal classifiers are low-dimensional combinations (<7 features). These results underscore the high potential for DOT to become a clinically useful diagnostic tool and warrant larger prospective clinical trials to conclusively demonstrate the ultimate clinical utility of this approach.
© 2013 Society of Photo-Optical Instrumentation Engineers (SPIE)
Ludguier D. Montejo, Jingfei Jia, Hyun K. Kim, Uwe J. Netz, Sabine Blaschke, Gerhard A. Mueller, Andreas H. Hielscher, "Computer-aided diagnosis of rheumatoid arthritis with optical tomography, Part 2: image classification," Journal of Biomedical Optics 18(7), 076002 (15 July 2013). https://doi.org/10.1117/1.JBO.18.7.076002

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