Paper
1 April 2005 Classification of optical tomographic images of rheumatoid finger joints with support vector machines
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Abstract
Over the last years we have developed a sagittal laser optical tomographic (SLOT) imaging system for the diagnosis and monitoring of inflammatory processes in proximal interphalangeal (PIP) joint of patients with rheumatoid arthritis (RA). While cross sectional images of the distribution of optical properties can now be generated easily, clinical interpretation of these images remains a challenge. In first clinical studies involving 78 finger joints, we compared optical tomographs to ultrasound images and clinical analyses. Receiver-operator curves (ROC) were generated using various image parameters, such as minimum and maximum scattering or absorption coefficients. These studies resulted in specificities and sensitivities in the range of 0.7 to 0.76. Recently, we have trained support vector machines (SVMs) to classify images of healthy and diseased joints. By eliminating redundancy using feature selection, we are achieving sensitivities of 0.72 and specificities up to 1.0. Studies with larger patient groups are necessary to validate these findings; but these initial results support the expectation that SVMs and other machine learning techniques can considerably improve image interpretation analysis in optical tomography.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Vivek Balasubramanyam and Andreas H. Hielscher "Classification of optical tomographic images of rheumatoid finger joints with support vector machines", Proc. SPIE 5692, Advanced Biomedical and Clinical Diagnostic Systems III, (1 April 2005); https://doi.org/10.1117/12.591096
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Cited by 5 scholarly publications.
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KEYWORDS
Ultrasonography

Tomography

Image analysis

Image classification

Machine learning

Scattering

Surface plasmons

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