TY - GEN
T1 - Multi-parameter optical image interpretations based on self-organizing mapping
AU - Klose, Christian D.
AU - Klose, A. K.
AU - Netz, U.
AU - Scheel, A.
AU - Beuthan, J.
AU - Hielscher, Andreas H.
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2008
Y1 - 2008
N2 - We found that using more than one parameter derived from optical tomographic images can lead to better image classification results compared to cases when only one parameter is used.. In particular we present a multi-parameter classification approach, called self-organizing mapping (SOM), for detecting synovitis in arthritic finger joints based on sagittal laser optical tomography (SLOT). This imaging modality can be used to determine various physical parameters such as minimal absorption and scattering coefficients in an image of the proximal interphalengeal joint. Results were compared to different gold standards: magnet resonance imaging, ultra-sonography and clinical evaluation. When compared to classifications based on single-parameters, e.g., absorption minimum only, the study reveals that multi-parameter classifications lead to higher classification sensitivities and specificities and statistical significances with p-values <5 per cent. Finally, the data suggest that image analyses are more reliable and avoid ambiguous interpretations when using more than one parameter.
AB - We found that using more than one parameter derived from optical tomographic images can lead to better image classification results compared to cases when only one parameter is used.. In particular we present a multi-parameter classification approach, called self-organizing mapping (SOM), for detecting synovitis in arthritic finger joints based on sagittal laser optical tomography (SLOT). This imaging modality can be used to determine various physical parameters such as minimal absorption and scattering coefficients in an image of the proximal interphalengeal joint. Results were compared to different gold standards: magnet resonance imaging, ultra-sonography and clinical evaluation. When compared to classifications based on single-parameters, e.g., absorption minimum only, the study reveals that multi-parameter classifications lead to higher classification sensitivities and specificities and statistical significances with p-values <5 per cent. Finally, the data suggest that image analyses are more reliable and avoid ambiguous interpretations when using more than one parameter.
KW - Artificial Intelligence
KW - Classification
KW - Optical Tomographic Imaging
KW - Self-Organizing Maps
UR - http://www.scopus.com/inward/record.url?scp=42149109172&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=42149109172&partnerID=8YFLogxK
U2 - 10.1117/12.763680
DO - 10.1117/12.763680
M3 - Conference contribution
AN - SCOPUS:42149109172
SN - 9780819470256
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Multimodal Biomedical Imaging III
T2 - Multimodal Biomedical Imaging III
Y2 - 19 January 2008 through 21 January 2008
ER -