Research Papers: Imaging

Computer-aided diagnosis of rheumatoid arthritis with optical tomography, Part 2: image classification

[+] Author Affiliations
Ludguier D. Montejo, Jingfei Jia

Columbia University, Department of Biomedical Engineering, New York, New York 10025

Hyun K. Kim

Columbia University Medical Center, Department of Radiology, New York, New York 10032

Uwe J. Netz

Laser- und Medizin-Technologie GmbH Berlin, Berlin, Dahlem 14195, Germany

Charité-Universitätsmedizin Berlin, Department of Medical Physics and Laser Medicine, Berlin 10117, Germany

Sabine Blaschke, Gerhard A. Müller

University Medical Center Göttingen, Department of Nephrology and Rheumatology, Göttingen 37075, Germany

Andreas H. Hielscher

Columbia University, Department of Biomedical Engineering, New York, New York 10025

Columbia University Medical Center, Department of Radiology, New York, New York 10032

Columbia University, Department of Electrical Engineering, New York, New York 10025

J. Biomed. Opt. 18(7), 076002 (Jul 15, 2013). doi:10.1117/1.JBO.18.7.076002
History: Received February 7, 2013; Revised May 28, 2013; Accepted May 30, 2013
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Abstract.  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.

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© 2013 Society of Photo-Optical Instrumentation Engineers

Citation

Ludguier D. Montejo ; Jingfei Jia ; Hyun K. Kim ; Uwe J. Netz ; Sabine Blaschke, et al.
"Computer-aided diagnosis of rheumatoid arthritis with optical tomography, Part 2: image classification", J. Biomed. Opt. 18(7), 076002 (Jul 15, 2013). ; http://dx.doi.org/10.1117/1.JBO.18.7.076002


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