Automatic graph-cut based segmentation of bones from knee magnetic resonance images for osteoarthritis research

Medical Image Analysis

Volumn 15, Issue 4, 2011, Pages 438-448

  Ababneh, Sufyan Y ^a   Prescott, Jeff W ^a   Gurcan, Metin N ^a  

^a OHIO STATE UNIVERSITY   (United States)

Author keywords

Femur; Graph cut algorithm; Osteoarthritis; Segmentation; Tibia

Indexed keywords

ADJACENT STRUCTURES; AGING POPULATION; BONE DETECTION; BONE SEGMENTATION; BONE STRUCTURE; CONTENT-BASED; CONTENT-BASED SYSTEMS; DISJOINT BLOCKS; FAT TISSUE; FEMUR; GRAPH-CUT; IMAGE CONTENT; IMAGE PIXELS; JOINT DISEASE; MAGNETIC RESONANCE IMAGES; MAXIMUM-FLOW ALGORITHM; MORPHOLOGICAL OPERATIONS; MR IMAGES; OSTEOARTHRITIS; POST PROCESSING; SEGMENTATION ACCURACY; SEGMENTATION ALGORITHMS; SEGMENTATION TECHNIQUES; SIMILARITY INDICES; SINGLE IMAGES; SPATIAL CHARACTERISTICS; SYNTHETIC IMAGES; THREE DIMENSIONAL SEGMENTATION; TIBIA; TRAINING DATA; USER INTERACTION;

ALGORITHMS; BONE; GRAPHIC METHODS; MAGNETIC RESONANCE; MAGNETIC RESONANCE IMAGING; MAMMALS; MATHEMATICAL MORPHOLOGY; THREE DIMENSIONAL; TISSUE;

IMAGE SEGMENTATION;

ADIPOSE TISSUE; ALGORITHM; ARTICLE; BONE STRUCTURE; DISEASE SEVERITY; FEMUR; GRAPH CUT BASED SEGMENTATION; HUMAN; IMAGE ANALYSIS; KNEE OSTEOARTHRITIS; MAJOR CLINICAL STUDY; MEDICAL RESEARCH; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; TIBIA;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; FEMUR; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; OSTEOARTHRITIS, KNEE; PATTERN RECOGNITION, AUTOMATED; SENSITIVITY AND SPECIFICITY; TIBIA;

EID: 79958769137     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2011.01.007     Document Type: Article

References (40)

1. Ababneh, S.Y., Gurcan, M.N., 2010a. An automated content-based segmentation framework: application to MR images of knee for osteoarthritis research. In: IEEE Int. Conf. on Electro/Information Tech.
2. Ababneh, S.Y., Gurcan, M.N., 2010b. An efficient graph-cut segmentation for knee bone osteoarthritis medical images. In: IEEE Int. Conf. on Electro/Information Tech.
3. Adams R., Bischof L. Seeded region growing. IEEE Trans. Pattern Anal. Mach. Intell. 1994, 16:641-647.
4. Baker K., Xu L., Zhang Y., Nevitt M., Niu J., Aliabadi P., Yu W., Felson D. Quadriceps weakness and its relationship to tibiofemoral and patellafemoral knee osteoarthritis in Chinese. Arthritis Rheum. 2004, 50:1815-1821.
5. Bourgeat P., Fripp J., Stanwell P., Ramadan S., Ourselin S. MR image segmentation of the knee bone using phase information. Med. Image Anal. 2007, 11(4):325-335.
6. Boykov Y. Graph cuts and efficient N-D image segmentation. Int. J Comput Vision 2006, 70(2):109-131.
7. Boykov Y., Kolmogorov V. An experiemental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell. 2004, 26(9):1124-1137.
8. Boykov, Y., Jolly, M., 2001. Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images, computer vision, 2001. ICCV 2001. In: Proceedings Eighth IEEE Int. Conf. in Comp. Vision.
9. Brant W.E., Holms C.A. Fundamentals of Diagnostic Radiology 2006, Lippincott Williams & Wilkins, Philadelphia, PA. third ed.
10. Brem M.H., Schlechtweg P.M., Bhagwat J., Genovese M., Dillingham M.F., Yoshioka H., Lang P. Longitudinal evaluation of the occurrence of MRI-detectable bone marrow edema in osteoarthritis of the knee. Acta Radiol. 2008, 49(9):1031-1037.
11. Buckwalter J.A., Einhorn T.A., Simon S.R., American Academy of Orthopaedic Surgeons Orthopaedic Basic Science: Biology and Biomechanics of the Musculoskeletal System 2000, American Academy of Orthopaedic Surgeons, Rosemont, IL.
12. Carballido-Gamio J., Belongie S., Majumdar S. Normalized cuts in 3-D for spinal MRI segmentation. IEEE Trans. Med. Img. 2004, 23(1):36-44.
13. Cover T.M., Thomas J.A. Elements of Information Theory 1991, John Wiley and Sons, Inc., New York, NY.
14. Dice L. Measure of the amount of ecologic association between species. Ecology 1945, 26:297-302.
15. Ding C., Cicuttini F., Jones G. Tibial subchondral bone size and knee cartilage defects: relevance to knee osteoarthritis. Osteoarthr. Cartilage 2007, 15(5):479-486.
16. Felson D., Lawrence R., et al. Osteoarthritis: new insights. Part 1: the disease and its risk factors. Ann. Intern. Med. 2000, 133:635-646.
17. Fritscher K., Grunerbl A., Schubert R. 3D image segmentation using combined shape-intensity prior models. Int. J. Comput Assist Radiol Surg. 2007, 1(6):341-350.
18. Fripp J., Crozier S., Warfield S.K., Ourselin S. Automatic segmentation of the bone and extraction of the bone-cartilage interface from magnetic resonance images of the knee. Phys. Med. Biol. 2007, 52:1617-1631.
19. Gonzalez R.C., Woods R.E. Digital Image Processing using Matlab 2002, Prentice Hall, Upper Saddle River, NJ.
20. Haralick R.M., Shanmugam K., Dinstein I. Textural features of image classification. IEEE Trans. Syst. Man Cybern., SMC 1973, 3(6):610-621.
21. Kellgren J., Lawrence J. Radiological assessment of osteoarthritis. Ann Rheum Dis. 1957, 16:494-501.
22. Leventon, M.E, 2000, Statistical Models in Medical Image Analysis, PhD Dessertation, MIT.
23. Lorigo L.M., Faugeras O., Grimson W.E.L., Keriven R., Kikinis R. Segmentation of bone in clinical knee MRI using texture-based geodesic active contours. MICCAI 1998, 1496:1195-1204.
24. Li K., Millington S., Wu X., Chen D.Z., Sonka M. Simultaneous segmentation of multiple closed surfaces using optimal graph searching. IPMI 2005, 3565:406-417.
25. Liu, L., Raber, D., Nopachai, D., Commean, P., Sinacore, P., Prior, F., Pless, R., Ju, T., 2008. Interactive separation of segmented bones in ct volumes using graph cut. In: Metaxas, D.N., Axel, L., Fichtinger, G., Székely, G. (Eds.), MICCAI, ser. LNCS, vol. 5241. pp. 296-304.
26. Matsuda S., Miura H., Nagamine R., Mawatari T., Tokunaga M., Nabeyama R., Iwamoto Y. Anatomical analysis of the femoral condyle in normal and osteoarthritic knees. J. Orthop. Res. 2004, 22(1):104-109.
27. Neogi T., Felson D., Niu J., Lynch J., Nevitt M., Guermazi A., Roemer F., Lewis C.E., Wallace B., Zhang Y. Cartilage loss occurs in the same subregions as subchondral bone attrition: a within-knee subregion-matched approach from the Multicenter Osteoarthritis Study. Arthritis Rheum. 2009, 61(11):1539-1544.
28. Nevitt, M.C., Felson, D.T., Lester, G., 2006. The Osteoarthritis Initiative: Protocol for the Cohort Study, v. 1.1. < http://www.oai.ucsf.edu/datarelease/.
29. Peterfy C.G., Schneider E., Nevitt M. The osteoarthritis initiative: report on the design rationale for the magnetic resonance imaging protocol for the knee. Osteoarthritis Cartilage 2008, 16(12):1433-1441.
30. Pouders C., De Maeseneer M., Van Roy P., Gielen J., Goossens A., Shahabpour M. Prevalence and MRI-anatomic correlation of bone cysts in osteoarthritic knees. AJR Am. J. Roentgenol. 2008, 190(1):17-21.
31. Prescott, J., Pennell, M., Best, T.M., Swanson, M.S., Haq, F., Jackson, R., Gurcan, M.N., 2009. An automated method to segment the femur for osteoarthritis research, IEEE Eng. Med. Biol. Soc (IEMBS).
32. Prescott, J., Best, T.M., Swanson, M.S., Haq, F., Jackson, R.D., Gurcan, M.N., 2010. Anatomically-anchored template-based level set segmentation: application to quadriceps muscles in MR images from the Osteoarthritis Initiative. J. Digit. Imag., e-published.
33. Schmid J., Magnenat-Thalmann N. MRI bone segmentation using deformable models and shape priors. Med. Image Comput. Comput-assisted Intervent. 2008, 119-126.
34. Smith J.J., Sorensen A.G., Thrall J.H. Biomarkers in imaging: realizing radiology's future. Radiology 2003, 227(3):633-638.
35. Swanson M.S., Prescott J., Best T.M., Powell K., Jackson R.D., Haq F., Gurcan M.N. Semi-automated segmentation to assess the lateral meniscus in normal and osteoarthritis knees. Osteoarthr.Cartilage 2010, 18(3):344-353.
36. Unlu Z., Tarhan S., Goktan C., Tuzun C. The correlation between magnetic resonance detected cartilage defects and spiking of tibial tubercles in osteoarthritis of the knee joint. Acta Med. Okayama 2006, 60(4):207-214.
37. Vezhnevets, V., Konouchine, V., 2005. GrowCut - interactive multi-label N-D image segmentation by cellular automata. Proc. Graphicon 150-156.
38. Wang, C., Guo, X., Yao, L., Li, K., Jin, Z., 2007. A practical method for muscles extraction and automatic segmentation of leg magnetic resonance images. In: Int. Conf. on Complex Medical Eng.
39. Yin, Y., Zhang, X., Sonka, M., 2008. Optimal multi-object multi-surface graph search segmentation: Full-joint cartilage delineation in 3d. In: Proc. of the 12th Annual Conf. on Medical Image Understanding and Analysis.
40. Zijdenbos A., Dawant B., Margolin R., Palmer A. Morphometric analysis of white matter lesions in MR images: method and validation. IEEE Trans. Med. Imag. 1994, 13:716-724.