Research Papers: Imaging

Computer-aided diagnosis of rheumatoid arthritis with optical tomography, Part 1: feature extraction

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

Columbia University, Department of Biomedical Engineering, New York, New York 10027

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 10027

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), 076001 (Jul 15, 2013). doi:10.1117/1.JBO.18.7.076001
History: Received September 28, 2012; Revised May 28, 2013; Accepted May 30, 2013
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Abstract.  This is the first part of a two-part paper on the application of computer-aided diagnosis to diffuse optical tomography (DOT). An approach for extracting heuristic features from DOT images and a method for using these features to diagnose rheumatoid arthritis (RA) are presented. Feature extraction is the focus of Part 1, while the utility of five classification algorithms is evaluated in Part 2. The framework is validated on a set of 219 DOT images of proximal interphalangeal (PIP) joints. Overall, 594 features are extracted from the absorption and scattering images of each joint. Three major findings are deduced. First, DOT images of subjects with RA are statistically different (p<0.05) from images of subjects without RA for over 90% of the features investigated. Second, DOT images of subjects with RA that do not have detectable effusion, erosion, or synovitis (as determined by MRI and ultrasound) are statistically indistinguishable from DOT images of subjects with RA that do exhibit effusion, erosion, or synovitis. Thus, this subset of subjects may be diagnosed with RA from DOT images while they would go undetected by reviews of MRI or ultrasound images. Third, scattering coefficient images yield better one-dimensional classifiers. A total of three features yield a Youden index greater than 0.8. These findings suggest that DOT may be capable of distinguishing between PIP joints that are healthy and those affected by RA with or without effusion, erosion, or synovitis.

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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 1: feature extraction", J. Biomed. Opt. 18(7), 076001 (Jul 15, 2013). ; http://dx.doi.org/10.1117/1.JBO.18.7.076001


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