Computer-aided classification of optical images for diagnosis of osteoarthritis in the finger joints.

This study presents a computer-aided classification method to distinguish osteoarthritis finger joints from healthy ones based on the functional images captured by x-ray guided diffuse optical tomography. Three imaging features, joint space width, optical absorption, and scattering coefficients, are employed to train a Least Squares Support Vector Machine (LS-SVM) classifier for osteoarthritis classification. The 10-fold validation results show that all osteoarthritis joints are clearly identified and all healthy joints are ruled out by the LS-SVM classifier. The best sensitivity, specificity, and overall accuracy of the classification by experienced technicians based on manual calculation of optical properties and visual examination of optical images are only 85%, 93%, and 90%, respectively. Therefore, our LS-SVM based computer-aided classification is a considerably improved method for osteoarthritis diagnosis.

[1]  Huabei Jiang,et al.  Image-guided optical spectroscopy in diagnosis of osteoarthritis: a clinical study , 2010, Biomedical optics express.

[2]  Alessandro Torricelli,et al.  Optical biopsy of bone tissue: a step toward the diagnosis of bone pathologies. , 2004, Journal of biomedical optics.

[3]  Jürgen Beuthan,et al.  Sagittal laser optical tomography for imaging of rheumatoid finger joints. , 2004, Physics in medicine and biology.

[4]  Zhen Yuan,et al.  Tomographic x-ray-guided three-dimensional diffuse optical tomography of osteoarthritis in the finger joints. , 2008, Journal of biomedical optics.

[5]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[6]  Bernhard Schölkopf,et al.  Improving the accuracy and speed of support vector learning machines , 1997, NIPS 1997.

[7]  Ian Witten,et al.  Data Mining , 2000 .

[8]  Chih-Jen Lin,et al.  Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel , 2003, Neural Computation.

[9]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[10]  Philip G Conaghan,et al.  Noninvasive techniques for assessing skeletal changes in inflammatory arthritis: imaging technique , 2004, Current opinion in rheumatology.

[11]  K. Han,et al.  Breast lesions on sonograms: computer-aided diagnosis with nearly setting-independent features and artificial neural networks. , 2003, Radiology.

[12]  C. Floyd,et al.  Case-based reasoning computer algorithm that uses mammographic findings for breast biopsy decisions. , 2000, AJR. American journal of roentgenology.

[13]  T Nishii,et al.  Disorders of acetabular labrum and articular cartilage in hip dysplasia: evaluation using isotropic high-resolutional CT arthrography with sequential radial reformation. , 2007, Osteoarthritis and cartilage.

[14]  Kristof Coussement,et al.  Faculteit Economie En Bedrijfskunde Hoveniersberg 24 B-9000 Gent Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparing Two Parameter-selection Techniques Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparin , 2022 .

[15]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[16]  Franz Kainberger,et al.  Musculoskeletal ultrasound including definitions for ultrasonographic pathology. , 2005, The Journal of rheumatology.

[17]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[18]  Bogdan Filipic,et al.  Exploiting structural information for semi-structured document categorization , 2006, Inf. Process. Manag..

[19]  Qizhi Zhang,et al.  Automated breast cancer classification using near-infrared optical tomographic images. , 2008, Journal of biomedical optics.

[20]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[21]  Nurettin Acir.  A support vector machine classifier algorithm based on a perturbation method and its application to ECG beat recognition systems , 2006, Expert Syst. Appl..

[22]  C. Floyd,et al.  Differences between computer-aided diagnosis of breast masses and that of calcifications. , 2002, Radiology.

[23]  Enrico Grosso,et al.  Face Authentication Using One-Class Support Vector Machines , 2005, IWBRS.

[24]  Bernhard Schölkopf,et al.  Improving the Accuracy and Speed of Support Vector Machines , 1996, NIPS.

[25]  Huabei Jiang,et al.  High-resolution x-ray guided three-dimensional diffuse optical tomography of joint tissues in hand osteoarthritis: morphological and functional assessments. , 2010, Medical physics.

[26]  Hsuan-Tien Lin.  A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods , 2005 .

[27]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .