Knee X-Ray Image Analysis Method for Automated Detection of Osteoarthritis

We describe a method for automated detection of radiographic osteoarthritis (OA) in knee X-ray images. The detection is based on the Kellgren-Lawrence (KL) classification grades, which correspond to the different stages of OA severity. The classifier was built using manually classified X-rays, representing the first four KL grades ( normal, doubtful, minimal, and moderate). Image analysis is performed by first identifying a set of image content descriptors and image transforms that are informative for the detection of OA in the X-rays and assigning weights to these image features using Fisher scores. Then, a simple weighted nearest neighbor rule is used in order to predict the KL grade to which a given test X-ray sample belongs. The dataset used in the experiment contained 350 X-ray images classified manually by their KL grades. Experimental results show that moderate OA (KL grade 3) and minimal OA (KL grade 2) can be differentiated from normal cases with accuracy of 91.5% and 80.4%, respectively. Doubtful OA (KL grade 1) was detected automatically with a much lower accuracy of 57%. The source code developed and used in this study is available for free download at www.openmicroscopy.org.

[1]  I. Gurevich,et al.  Comparative analysis and classification of features for image models , 2006, Pattern Recognition and Image Analysis.

[2]  I. Boniatis,et al.  Osteoarthritis severity of the hip by computer-aided grading of radiographic images , 2006, Medical and Biological Engineering and Computing.

[3]  Jorge Herbert de Lira,et al.  Two-Dimensional Signal and Image Processing , 1989 .

[4]  Raveendran Paramesran,et al.  Efficient computation of radial moment functions using symmetrical property , 2006, Pattern Recognit..

[5]  Sergios Theodoridis,et al.  Pattern Recognition , 1998, IEEE Trans. Neural Networks.

[6]  T. Aigner,et al.  Molecular pathology and pathobiology of osteoarthritic cartilage , 2002, Cellular and Molecular Life Sciences CMLS.

[7]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[8]  R. Rose,et al.  Role of Subchondral Bone in the Initiation and Progression of Cartilage Damage , 1986, Clinical orthopaedics and related research.

[9]  Carlos Higuera,et al.  Hip and knee arthroplasty in the geriatric population. , 2006, Clinics in geriatric medicine.

[10]  Rodney Long,et al.  Anterior osteophyte discrimination in lumbar vertebrae using size-invariant features. , 2004, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[11]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[12]  J. Kellgren,et al.  Radiological Assessment of Osteo-Arthrosis , 1957, Annals of the rheumatic diseases.

[13]  Hideyuki Tamura,et al.  Textural Features Corresponding to Visual Perception , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[14]  G. Gold,et al.  Atlas of individual radiographic features in osteoarthritis, revised. , 2007, Osteoarthritis and cartilage.

[15]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[16]  M. Teague.  Image analysis via the general theory of moments , 1980 .

[17]  Douglas A. Creager,et al.  The Open Microscopy Environment (OME) Data Model and XML file: open tools for informatics and quantitative analysis in biological imaging , 2005, Genome Biology.

[18]  Erik Brauner,et al.  Informatics and Quantitative Analysis in Biological Imaging , 2003, Science.

[19]  Lior Shamir,et al.  Computer Vision for Microscopy Applications , 2007 .

[20]  P. Croft,et al.  An introduction to the Atlas of Standard Radiographs of Arthritis. , 2005, Rheumatology.

[21]  Lior Shamir,et al.  WND-CHARM: Multi-purpose image classification using compound image transforms , 2008, Pattern Recognit. Lett..

[22]  H. Cowell,et al.  Instructional Course Lectures, The American Academy of Orthopaedic Surgeons. , 1988 .

[23]  Lena Costaridou,et al.  Computer-aided grading and quantification of hip osteoarthritis severity employing shape descriptors of radiographic hip joint space , 2007, Comput. Biol. Medicine.

[24]  S. Ahlback,et al.  Osteoarthrosis of the knee. A radiographic investigation. , 1968 .

[25]  Ewert Bengtsson,et al.  A Feature Set for Cytometry on Digitized Microscopic Images , 2003, Analytical cellular pathology : the journal of the European Society for Analytical Cellular Pathology.

[26]  Jean-Pierre Pelletier,et al.  Osteoarthritis: recent developments , 2003 .

[27]  Joseph Lipka,et al.  A Table of Integrals , 2010 .

[28]  I. S. Gradshteyn,et al.  Table of Integrals, Series, and Products , 1976 .

[29]  Dennis Gabor,et al.  Theory of communication , 1946 .

[30]  Aminudin Che Ahmad,et al.  Atlas of standard radiographs of arthritis addendum: the ankle , 2009 .

[31]  I. Rossman,et al.  Normal Human Aging: The Baltimore Longitudinal Study of Aging , 1986 .

[32]  A.B. Suksmono,et al.  Automated detection of unimpaired joint space for knee osteoarthritis assessment , 2005, Proceedings of 7th International Workshop on Enterprise networking and Computing in Healthcare Industry, 2005. HEALTHCOM 2005..

[33]  H. J. Mankin,et al.  Instructional Course Lectures, The American Academy of Orthopaedic Surgeons - Articular Cartilage. Part I: Tissue Design and Chondrocyte-Matrix Interactions*† , 1997 .

[34]  R. Stockwell.  Cartilage failure in osteoarthritis: Relevance of normal structure and function. A review , 1991 .

[35]  P A Gaydecki,et al.  Radiographic image analysis in the study of bone morphology. , 1987, Clinical physics and physiological measurement : an official journal of the Hospital Physicists' Association, Deutsche Gesellschaft fur Medizinische Physik and the European Federation of Organisations for Medical Physics.

[36]  Ioannis Andreadis,et al.  Real-time computation of Zernike moments , 2005, IEEE Transactions on Circuits and Systems for Video Technology.

[37]  Hermann Brenner,et al.  Reliability of radiographic grading of osteoarthritis of the hip and knee. , 1997, Scandinavian journal of rheumatology.

[38]  Jie Yao,et al.  Searching online journals for fluorescence microscope images depicting protein subcellular location patterns , 2001, Proceedings 2nd Annual IEEE International Symposium on Bioinformatics and Bioengineering (BIBE 2001).

[39]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[40]  M Lequesne,et al.  Atlas of individual radiographic features in osteoarthritis. , 1995, Osteoarthritis and cartilage.

[41]  David Arenberg,et al.  Normal Human Aging: The Baltimore Longitudinal Study on Aging , 1984 .

[42]  Shree K. Nayar,et al.  Spatial information in multiresolution histograms , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[43]  Ilya G. Goldberg,et al.  Open microscopy environment , 2005, 2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05).