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- Title
Computer-aided diagnosis of rheumatoid arthritis with optical tomography, Part 2: image classification.
- Authors
Montejo, Ludguier D.; Jingfei Jia; Hyun K. Kim; Netz, Uwe J.; Blaschke, Sabine; Müller, Gerhard A.; Hielscher, Andreas H.
- 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.
- Subjects
OPTICAL tomography; RHEUMATOID arthritis diagnosis; COMPUTERS in medicine; JOINTS (Anatomy); CLASSIFICATION algorithms; DISCRIMINANT analysis; SELF-organizing maps; SUPPORT vector machines
- Publication
Journal of Biomedical Optics, 2013, Vol 18, Issue 7, p1
- ISSN
1083-3668
- Publication type
Article
- DOI
10.1117/1.JBO.18.7.076002