No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
A signature dissimilarity measure for trabecular bone texture in knee radiographs
1.E. L. Radin and R. M. Rose, “Role of subchondral bone in the initiation and progression of cartilage damage,” Clin. Orthop. Relat. Res. 213, 34–40 (1986).
2.L. Kamibayashi, U. P. Wyss, T. D. V. Cooke, and B. Zee, “Trabecular microstructure in the medial condyle of the proximal tibia of patients with knee osteoarthritis,” Bone (N.Y.) 17, 27–35 (1995).
3.L. Kamibayashi, U. P. Wyss, T. D. V. Cooke, and B. Zee, “Changes in mean trabecular orientation in the medial condyle of the proximal tibia in osteoarthritis,” Calcif. Tissue Int. 57, 69–73 (1995).
4.E. A. Messent, R. J. Ward, C. J. Tonkin, and C. Buckland-Wright, “Tibial cancellous bone changes in patients with knee osteoarthritis. A short-term longitudinal study using fractal signature analysis,” Osteoarthritis Cartilage 13, 463–470 (2005).
6.P. Podsiadlo, L. Dahl, M. Englund, L. S. Lohmander, and G. W. Stachowiak, “Differences in trabecular bone texture between knees with and without radiographic osteoarthritis detected by fractal methods,” Osteoarthritis Cartilage 16, 323–329 (2008).
7.H. Defossez, R. M. Hall, P. G. Walker, B. M. Wroblewski, P. D. Siney, and B. Purbach, “Determination of the trabecular bone direction from digitised radiographs,” Med. Eng. Phys. 25, 719–729 (2003).
9.P. Dieppe, J. Cushnaghan, P. Young, and J. Kirwan, “Prediction of the progression of joint space narrowing in osteoarthritis of the knee by bone scintigraphy,” Ann. Rheum. Dis. 52, 557–563 (1993).
10.L. Pothuaud, C. L. Benhamou, P. Porion, E. Lespessailles, R. Harba, and P. Levitz, “Fractal dimension of trabecular bone projection texture is related to three-dimensional microarchitecture,” J. Bone Miner. Res. 15, 691–699 (2000).
11.R. Jennane, R. Harba, G. Lemineur, S. Bretteil, A. Estrade, and C. L. Benhamou, “Estimation of the 3D self-similarity parameter of trabecular bone from its 2D projection,” Med. Image Anal. 11, 91–98 (2007).
12.L. Pothuaud, P. Carceller, and D. Hans, “Correlations between grey-level variations in 2D projection images (TBS) and 3D microarchitecture: Applications in the study of human trabecular bone microarchitecture,” Bone (N.Y.) 42, 775–787 (2008).
13.G. Luo, J. H. Kinney, J. J. Kaufman, D. Haupt, A. Chiabrera, and R. S. Siffert, “Relationship between plain radiographic patterns and three-dimensional trabecular architecture in the human calcaneus,” Osteoporosis Int. 9, 339–345 (1999).
14.J. A. Lynch, D. J. Hawkes, and J. C. Buckland-Wright, “Analysis of texture in macroradiographs of osteoarthritic knees using the fractal signature,” Phys. Med. Biol. 36, 709–722 (1991).
15.J. C. Buckland-Wright, J. A. Lynch, and D. G. Macfarlane, “Fractal signature analysis measures cancellous bone organisation in macroradiographs of patients with knee osteoarthritis,” Ann. Rheum. Dis. 55, 749–755 (1996).
16.E. A. Messent, J. C. Buckland-Wright, and G. M. Blake, “Fractal analysis of trabecular bone in knee osteoarthritis (OA) is a more sensitive marker of disease status than bone mineral density (BMD),” Calcif. Tissue Int. 76, 419–425 (2005).
17.P. Podsiadlo and G. W. Stachowiak, “Analysis of trabecular bone texture by modified Hurst orientation transform,” Med. Phys. 29, 460–474 (2002).
20.M. Wolski, P. Podsiadlo, and G. W. Stachowiak, “Directional fractal signature analysis of trabecular bone: Evaluation of different methods to detect early osteoarthritis in knee radiographs,” Proc. Inst. Mech. Eng., Part H: J. Eng. Med. 223, 211–236 (2009).
21.L. Shamir, S. M. Ling, W. W. Scott, Jr., A. Bos, N. Orlov, T. J. Macura, D. M. Eckley, L. Ferrucci, and I. G. Goldberg, “X-ray image analysis method for automated detection of osteoarthritis,” IEEE Trans. Biomed. Eng. 56, 407–415 (2009).
22.L. Shamir, S. M. Ling, W. Scott, M. Hochberg, L. Ferrucci, and I. G. Goldberg, “Early detection of radiographic knee osteoarthritis using computer-aided analysis,” Osteoarthritis Cartilage 17, 1307–1312 (2009).
27.H. Jegou, C. Schmid, H. Harzallah, and J. Verbeek, “Accurate image search using the contextual dissimilarity measure,” IEEE Trans. Pattern Anal. Mach. Intell. 32, 2–11 (2010).
28.N. Sudha and Y. H. K. Wong, “Hausdorff distance for iris recognition,” in Proceedings of the 22nd IEEE International Symposium on Intelligent Control, Singapore, Singapore, 1–3 October 2007 (unpublished).
29.W. Zhu, T. Jiang, and X. Li, “Local region based medical image segmentation using J-divergence measure,” in Proceedings of the IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China, 1–4 September 2005 (unpublished).
32.J. Babaud, A. P. Witkin, M. Baudin, and R. O. Duda, “Uniqueness of the Gaussian kernel for scale-space filtering,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8, 26–33 (1986).
34.M. Wei-Ying and Z. Hong Jiang, “Benchmarking of image features for content-based retrieval,” in Conference Record of the 32nd Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, 1–4 November 1998 (unpublished).
35.V. Castelli and L. D. Bergman, Image Databases: Search and Retrieval of Digital Imagery (Wiley, New York, 2002).
36.P. Brodatz, Textures: A Photographic Album for Artists and Designers (Dover, New York, 1966).
37.T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 971–987 (2002).
38.T. Randen and T. J. H. Husøy, “Filtering for texture classification: A comparative study,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 291–310 (1999).
39.J. Zhang, M. Marszałek, S. Lazebnik, and C. Schmid, “Local features and kernels for classification of texture and object categories: A comprehensive study,” Int. J. Comput. Vis. 73, 213–238 (2007).
40.J. C. Russ, Fractal Surfaces (Plenum, New York, 1994).
41.A. G. Haus and S. M. Jaskulski, The Basics of Film Processing in Medical Imaging (Medical Physics Publishing, Madison, 1997).
42.J. A. Bencomo and B. G. Fallone, “A logit model for the modulation transfer function of screen-film systems,” Med. Phys. 13, 857–860 (1986).
43.H. B. Mann and D. R. Whitney, “On a test of whether one of two random variables is stochastically larger than the other,” Ann. Math. Stat. 18, 50–60 (1947).
45.R. D. Altman, M. Hochberg, W. A. Murphy, Jr., F. Wolfe, and M. Lequesne, “Atlas of individual radiographic features in osteoarthritis,” Osteoarthritis Cartilage 3, A3–A70 (1995).
46.P. Podsiadlo, M. Wolski, and G. W. Stachowiak, “Automated selection of trabecular bone regions in knee radiographs,” Med. Phys. 35, 1870–1883 (2008).
47.N. Orlov, L. Shamir, T. Macura, J. Johnston, M. D. Eckley, and I. G. Goldberg, “WND-CHARM: Multi-purpose image classification using compound image transforms,” Pattern Recogn. Lett. 29, 1684–1693 (2008).
49.B. Liu, H. D. Cheng, J. Huang, J. Tian, X. Tang, and J. Liu, “Fully automatic and segmentation-robust classification of breast tumors based on local texture analysis of ultrasound images,” Pattern Recogn. 43, 280–298 (2010).
51.S. G. Armato III, A. S. Roy, H. MacMahon, F. Li, K. Doi, S. Sone, and M. B. Altman, “Evaluation of automated lung nodule detection on low-dose computed tomography scans from a lung cancer screening program,” Acad. Radiol. 12, 337–346 (2005).
53.P. Georgiadis, D. Cavouras, I. Kalatzis, D. Glotsos, E. Athanasiadis, S. Kostopoulos, K. Sifaki, M. Malamas, G. Nikiforidis, and E. Solomou, “Enhancing the discrimination accuracy between metastases, gliomas and meningiomas on brain MRI by volumetric textural features and ensemble pattern recognition methods,” Magn. Reson. Imaging 27, 120–130 (2009).
Article metrics loading...
Full text loading...
Most read this month