Hand Radiograph Analysis and Joint Space Location Improvement for Image Interpretation

• Autorzy: Zieliński B.
• Języki publikacji: EN

Abstrakty

EN

Hand radiograph analysis is extremely exhausting and time consuming for radiologists, not because of the complexity, but because of the precision required during diagnosis. Due to this fact, automatic analysis of the joint space width of all joints in a hand would be indispensable. In the following paper, an improvement of a method used to quantify the minimal JSW in hand radiographs, proposed by the author in his previous works, is considered. The proposed changes have an impact on initial pre-processing and joint space location. As a result of experiments conducted on more than 1100 joint spaces, the overall error of pre-processing and joint location decreases from 10.8% to 2.59% after changes.

Słowa kluczowe

EN

image processing; medical imaging; radiology; rheumatoid arthritis; X-ray photo; thresholding; joint space location; joint space width

• Wydawca: Wydawnictwo Uniwersytetu Jagiellońskiego
• Czasopismo: Schedae Informaticae
• Rocznik: 2009
• Tom: Vol. 17/18
• Strony: 45--61
• Opis fizyczny: Bibliogr. 23 poz., rys.

Twórcy

autor

Zieliński B.

• Faculty of Physics, Astronomy and Applied Computer Science Jagiellonian University, Reymonta 4, 30-059 Kraków, Poland,

Bibliografia

• [1] Bachmann D., Resnick D.; Radiological Atlas of Rheumatoid Diseases, Editiones Roche, Switzerland, 1994.
• [2] Bielecki A., Korkosz M., Zieliński B.; Hand radiographs preprocessing, image representation in the finger regions and joint space width measurements for image interpretation, Pattern Recognition, 41(12), 2008, pp. 3786−3798.
• [3] Beier D., Lehmann T.M., Seidl T., Thies C.; Segmentation of medical images combining local, regional, global, and hierarchical distances into a bottom-up region merging scheme, Proceedings of the SPIE, 5747, 2005, pp. 546−555.
• [4] Botcher J. et al.; Computerized Digital Imaging Techniques Provided by Digital X-ray Radiogrammetry as New Diagnostic Tool in Rheumatoid Arthritis, Journal of Digital Imaging, 19(3), 2006, pp. 279−288.
• [5] Bublinski Z., Mikrut Z., Popiela-Mizera A.; A method of linear star section applied for object separation in ERCP images, International Conference of Image Processing, 1, 1996, pp. 363−366.
• [6] Bublinski Z., Mikrut Z., Popiela-Mizera A., Zajac A.; A dedicated algorithm for object identification in medical images, Image Processing Communication, 1, 1995, pp. 43−58.
• [7] Cooper C., Klippel J., Dieppe P.; Osteoarthritis and Related Disorders in Rheumatology, Mosby International, London, 1998.
• [8] Freeman H.; On the encoding of arbitrary geometric configurations, IRE Transaction Electronical Computations, 10, 1961, pp. 260−268.
• [9] Freeman H.; On the digital-computer classification of geometric line patterns, in Proceedings of the 18th National Electronics Conference, 1962, pp. 312−324.
• [10] van Kloster R. et al.; Automatic quantification of osteoarthritis in hand radiographs: validation of a new method to measure joint space width, Osteoarthritis and Cartilage, 16(1), 2008, pp. 18−25.
• [11] Liang M., Esdaile J., Klippel J., Dieppe P.; Impact and Cost Effectiveness of Rheumatologic Care in Rheumatology, Mosby International, London 1998.
• [12] MacGregor A., Silman A., Klippel J., Dieppe P.; Rheumatoid Arthritis in Rheumatology, Mosby International, London 1998.
• [13] Otsu N.; A threshold selection method from gray-level histograms, IEEE Transaction Systems, Man, and Cybernetics, 9, 1979, pp. 62−66.
• [14] Pavlidis T.; Algorithms for Graphics and Image Processing, Springer and Rockville Computer Science Press, Berlin 1982.
• [15] Peterfy C., Klippel J., Dieppe P.; Imaging Techniques in Rheumatology, Mosby International, London 1998.
• [16] Pfeil A. et al.; Normative Reference Values of Joint Space Width Estimated by Computer-aided Joint Space Analysis (CAJSA): The Distal Interphalangeal Joint, Journal of Digital Imaging, Suppl. 1, 2008, pp. 104−112.
• [17] Pfeil A. et al.; Computer-aided joint space analysis of the metacarpalphalangeal and proximal-interphalangeal finger joint: normative age-related and gender-specific data, Skeletal Radiology, 36(9), 2007, pp. 853−864.
• [18] Pratt W.K.; Digital Image Processing, John Wiley & Sons, New York, 1991.
• [19] Sharp J., Gardner J., Bennett E.; Computer-based methods for measuring joint space and estimating erosion volume in the finger and wrist joints of patients with rheumatoid arthritis, Arthritis & Rheumatism, 43(6), 2000, pp.1378−1386.
• [20] Szczeklik A., Zimmermann-Górska I.; Internal Medicine (Chorobywewnętrzne), Medycyna Praktyczna, Warszawa 2006 (in Polish).
• [21] Tadeusiewicz R., Ogiela M.; Picture languages in automatic radiological palm interpretation, International Journal of Applied Mathematics and Computer Science, 15(2), 2005, pp. 305−312.
• [22] Tadeusiewicz R., Ogiela M., Ogiela L.; Image languages in intelligent radiological palm diagnostics, Pattern Recognition, 39, 2006, pp. 2157−2165.
• [23] Zieliński B.; Fully-automated algorithm dedicated to computing metacarpophalangeal and interphalangeal joint cavity widths, Schedae Informaticae, 16, 2007, pp. 47−67.