TY - JOUR
T1 - Multiparameter classifications of optical tomographic images
AU - Klose, Christian D.
AU - Klose, Alexander D.
AU - Netz, Uwe
AU - Beuthan, Juergen
AU - Hielscher, Andreas H.
N1 - Funding Information:
The authors thank Scheel, University of Göttingen, Germany, for providing experimental data used in this analysis. This work was supported in part by a grant number 2R01-AR46255 from the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), which is part of the National Institutes of Health.
PY - 2008
Y1 - 2008
N2 - This research study explores the combined use of more than one parameter derived from optical tomographic images to increase diagnostic accuracy which is measured in terms of sensitivity and specificity. Parameters considered include, for example, smallest or largest absorption or scattering coefficients or the ratios thereof in an image region of interest. These parameters have been used individually in a previous study to determine if a finger joint is affected or not affected by rheumatoid arthritis. To combine these parameters in the analysis we employ here a vector quantization based classification method called Self-Organizing Mapping (SOM). This method allows producing multivariate ROC-curves from which sensitivity and specificities can be determined. We found that some parameter combinations can lead to higher sensitivities whereas others to higher specificities when compared to singleparameter classifications employed in previous studies. The best diagnostic accuracy, in terms of highest Youden index, was achieved by combining three absorption parameters [maximum(μ a), minimum(μ a), and the ratio of minimum(μ a) and maximum(μ a)], which result in a sensitivity of 0.78, a specificity of 0.76, a Youden index of 0.54, and an area under the curve (AUC) of 0.72. These values are higher than for previously reported single parameter classifications with a best sensitivity and specificity of 0.71, a Youden index of 0.41, and an AUC of 0.66.
AB - This research study explores the combined use of more than one parameter derived from optical tomographic images to increase diagnostic accuracy which is measured in terms of sensitivity and specificity. Parameters considered include, for example, smallest or largest absorption or scattering coefficients or the ratios thereof in an image region of interest. These parameters have been used individually in a previous study to determine if a finger joint is affected or not affected by rheumatoid arthritis. To combine these parameters in the analysis we employ here a vector quantization based classification method called Self-Organizing Mapping (SOM). This method allows producing multivariate ROC-curves from which sensitivity and specificities can be determined. We found that some parameter combinations can lead to higher sensitivities whereas others to higher specificities when compared to singleparameter classifications employed in previous studies. The best diagnostic accuracy, in terms of highest Youden index, was achieved by combining three absorption parameters [maximum(μ a), minimum(μ a), and the ratio of minimum(μ a) and maximum(μ a)], which result in a sensitivity of 0.78, a specificity of 0.76, a Youden index of 0.54, and an area under the curve (AUC) of 0.72. These values are higher than for previously reported single parameter classifications with a best sensitivity and specificity of 0.71, a Youden index of 0.41, and an AUC of 0.66.
KW - computer aided diagnostics
KW - image interpretation
KW - neural networks
KW - optical tomography
KW - self-organizing maps
KW - vector quantization
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U2 - 10.1117/1.2981806
DO - 10.1117/1.2981806
M3 - Article
C2 - 19021375
AN - SCOPUS:60849123955
SN - 1083-3668
VL - 13
JO - Journal of biomedical optics
JF - Journal of biomedical optics
IS - 5
M1 - 050503
ER -