TY - GEN
T1 - Analysis and classification of optical tomographic images of rheumatoid fingers with ANOVA and discriminate analysis
AU - Montejo, Ludguier D.
AU - Kim, Hyun K.
AU - Häme, Yrjö
AU - Jia, Jingfei
AU - Montejo, Julio D.
AU - Netz, Uwe J.
AU - Blaschke, Sabine
AU - Zwaka, Paul
AU - Müeller, Gerhard A.
AU - Beuthan, Jürgen
AU - Hielscher, Andreas H.
PY - 2011
Y1 - 2011
N2 - We present a study on the effectiveness of computer-aided diagnosis (CAD) of rheumatoid arthritis (RA) from frequency-domain diffuse optical tomographic (FDOT) images. FDOT is used to obtain the distribution of tissue optical properties. Subsequently, the non-parametric Kruskal-Wallis ANOVA test is employed to verify statistically significant differences between the optical parameters of patients affected by RA and healthy volunteers. Furthermore, quadratic discriminate analysis (QDA) of the absorption (μa) and scattering (μa or μ's) distributions is used to classify subjects as affected or not affected by RA. We evaluate the classification efficiency by determining the sensitivity (Se), specificity (Sp), and the Youden index (Y). We find that combining features extracted from μa and μa or μ's images allows for more accurate classification than when μa or μa or μ's features are considered individually on their own. Combining μa and μa or μ's features yields values of up to Y = 0.75 (Se = 0.84 and Sp = 0.91). The best results when μa or μ's features are considered individually are Y = 0.65 (Se = 0.85 and Sp = 0.80) and Y = 0.70 (Se = 0.80 and Sp = 0.90), respectively.
AB - We present a study on the effectiveness of computer-aided diagnosis (CAD) of rheumatoid arthritis (RA) from frequency-domain diffuse optical tomographic (FDOT) images. FDOT is used to obtain the distribution of tissue optical properties. Subsequently, the non-parametric Kruskal-Wallis ANOVA test is employed to verify statistically significant differences between the optical parameters of patients affected by RA and healthy volunteers. Furthermore, quadratic discriminate analysis (QDA) of the absorption (μa) and scattering (μa or μ's) distributions is used to classify subjects as affected or not affected by RA. We evaluate the classification efficiency by determining the sensitivity (Se), specificity (Sp), and the Youden index (Y). We find that combining features extracted from μa and μa or μ's images allows for more accurate classification than when μa or μa or μ's features are considered individually on their own. Combining μa and μa or μ's features yields values of up to Y = 0.75 (Se = 0.84 and Sp = 0.91). The best results when μa or μ's features are considered individually are Y = 0.65 (Se = 0.85 and Sp = 0.80) and Y = 0.70 (Se = 0.80 and Sp = 0.90), respectively.
KW - ANOVA
KW - Diffuse optical tomography
KW - computer aided diagnosis
KW - discriminate analysis
KW - rheumatoid arthritis
UR - http://www.scopus.com/inward/record.url?scp=79955456265&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79955456265&partnerID=8YFLogxK
U2 - 10.1117/12.875977
DO - 10.1117/12.875977
M3 - Conference contribution
AN - SCOPUS:79955456265
SN - 9780819484277
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Advanced Biomedical and Clinical Diagnostic Systems IX
T2 - Advanced Biomedical and Clinical Diagnostic Systems IX
Y2 - 23 January 2011 through 25 January 2011
ER -