3D ultrasound image segmentation using wavelet support vector machines

Medical Physics

Volumn 39, Issue 6, 2012, Pages 2972-2984

  Akbari, Hamed ^a   Fei, Baowei ^a,b,c  

^a EMORY UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b Emory University   (United States)

^c EMORY UNIVERSITY   (United States)

Author keywords

image segmentation; kernel based support vector machine; prostate model; statistical shape model; three dimensional ultrasound imaging; transrectal ultrasound imaging; wavelet based texture extraction

Indexed keywords

BIOPSY; EXTRACTION; SUPPORT VECTOR MACHINES; TEXTURES; TISSUE; ULTRASONIC IMAGING; UROLOGY; VECTORS; WAVELET TRANSFORMS;

3D ULTRASOUND IMAGES; SEGMENTATION METHODS; STATISTICAL SHAPE MODEL; TEXTURE EXTRACTION; THREE-DIMENSIONAL (3D) SEGMENTATION; THREE-DIMENSIONAL ULTRASOUND IMAGING; TRANSRECTAL ULTRASOUND; WAVELET SUPPORT VECTOR MACHINES;

IMAGE SEGMENTATION;

EID: 84863533963     PISSN: 00942405     EISSN: None     Source Type: Journal    
DOI: 10.1118/1.4709607     Document Type: Article

References (41)

1. J. A. Noble and D. Boukerroui, Ultrasound image segmentation: A survey., IEEE Trans. Med. Imaging 25, 987-1010 (2006). 10.1109/TMI.2006.877092
2. R. Siegel, E. Ward, O. Brawley, and A. Jemal, Cancer statistics, 2011: The impact of eliminating socioeconomic and racial disparities on premature cancer deaths., CA Cancer J. Clin. 61, 212-236 (2011). 10.3322/caac.20121
3. B. Fei, V. Master, P. Nieh, H. Akbari, X. Yang, A. Fenster, and D. Schuster, A PET/CT directed, 3D ultrasound-guided biopsy system for prostate cancer., Prostate Cancer Imaging. Image Analysis and Image-Guided Interventions, Lecture Notes in Computer Science (Springer-Verlag, Berlin, Heidelberg, 2011), Vol. 6963, pp. 100-108. 10.1007/978-3-642-23944-1-11
4. J. Bax, J. Williams, D. Cool, L. Gardi, J. Montreuil, V. Karnik, S. Sherebrin, C. Romagnoli, and A. Fenster, Mechanically assisted 3D prostate ultrasound imaging and biopsy needle-guidance system., in Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling, Vol. 7625, edited by, K. H. Wong, and, M. I. Miga, (SPIE, San Diego, CA, 2010).
5. H. Akbari, X. Yang, L. Halig, and B. Fei, 3D segmentation of prostate ultrasound images using wavelet transform., Medical Imaging-Image Processing, edited by Benoit M. Dawant and David R. Haynor, Proc. SPIE 7962, 79622K-1-79622K-8 (SPIE, San Diego, CA, 2011). 10.1117/12.878072
6. N. Betrouni, M. Vermandel, D. Pasquier, S. Maouche, and J. Rousseau, Segmentation of abdominal ultrasound images of the prostate using a priori information and an adapted noise filter., Comput. Med. Imaging Graph. 29, 43-51 (2005). 10.1016/j.compmedimag.2004.07.007
7. B. Chiu, G. H. Freeman, M. M. A. Salama, and A. Fenster, Prostate segmentation algorithm using dyadic wavelet transform and discrete dynamic contour., Phys. Med. Biol. 49, 4943-4960 (2004). 10.1088/0031-9155/49/21/007
8. M. Y. Ding, B. Chiu, I. Gyacskov, X. P. Yuan, M. Drangova, D. B. Downey, and A. Fenster, Fast prostate segmentation in 3D TRUS images based on continuity constraint using an autoregressive model., Med. Phys. 34, 4109-4125 (2007). 10.1118/1.2777005
9. A. Ghanei, H. Soltanian-Zadeh, A. Ratkewicz, and F. F. Yin, A three-dimensional deformable model for segmentation of human prostate from ultrasound images., Med. Phys. 28, 2147-2153 (2001). 10.1118/1.1388221
10. L. X. Gong, S. D. Pathak, D. R. Haynor, P. S. Cho, and Y. Kim, Parametric shape modeling using deformable superellipses for prostate segmentation., IEEE Trans. Med. Imaging 23, 340-349 (2004). 10.1109/TMI.2004.824237
11. K. Bogie, X. Wang, B. Fei, and J. Sun, New technique for real-time interface pressure analysis: getting more out of large image data sets., J. Rehabil. Res. Dev. 45, 523-535 (2008). 10.1682/JRRD.2007.03.0046
12. X. Liu, D. L. Langer, M. A. Haider, Y. Yang, M. N. Wernick, and I. S. Yetik, Prostate cancer segmentation with simultaneous estimation of Markov random field parameters and class., IEEE Trans. Med. Imaging 28, 906-915 (2009). 10.1109/TMI.2009.2012888
13. G. Lixin, S. D. Pathak, D. R. Haynor, P. S. Cho, and K. Yongmin, Parametric shape modeling using deformable superellipses for prostate segmentation., IEEE Trans. Med. Imaging 23, 340-349 (2004). 10.1109/TMI.2004. 824237
14. B. Fei, H. Wang, J. D. Meyers, D. K. Feyes, N. L. Oleinick, and J. L. Duerk, High-field magnetic resonance imaging of the response of human prostate cancer to Pc 4-based photodynamic therapy in an animal model., Lasers Surg. Med. 39, 723-730 (2007). 10.1002/lsm.20576
15. S. D. Pathak, D. R. Haynor, and Y. Kim, Edge-guided boundary delineation in prostate ultrasound images., IEEE Trans. Med. Imaging 19, 1211-1219 (2000). 10.1109/42.897813
16. X. Yang and B. Fei, 3D prostate segmentation of ultrasound images combining longitudinal image registration and machine learning., Medical Imaging: Image-Guided Procedures, Robotic Interventions, and Modeling, edited by, David R. Holmes III, and, Kenneth H. Wong, Proc. SPIE 8316, 831620 (SPIE, San Diego, CA, 2012). 10.1117/12.877888
17. I. B. Tutar, S. D. Pathak, L. X. Gong, P. S. Cho, K. Wallner, and Y. M. Kim, Semiautomatic 3-D prostate segmentation from TRUS images using spherical harmonics., IEEE Trans. Med. Imaging 25, 1645-1654 (2006). 10.1109/TMI.2006. 884630
18. R. S. Xu, O. Michailovich, and M. Salama, Information tracking approach to segmentation of ultrasound imagery of the prostate., IEEE Trans. Ultrason. Ferroelectr. Freq. Control 57, 1748-1761 (2010). 10.1109/TUFFC.2010.1613
19. P. Yan, S. Xu, B. Turkbey, and J. Kruecker, Adaptively learning local shape statistics for prostate segmentation in ultrasound., IEEE Trans. Biomed. Eng. 58, 633-641 (2010).
20. B. Fei, D. Schuster, V. Master, H. Akbari, A. Fenster, and P. Nieh, A molecular image-directed, 3D ultrasound-guided biopsy system for the prostate., Medical Imaging: Image-Guided Procedures, Robotic Interventions, and Modeling, edited by, David R. Holmes III, and, Kenneth H. Wong, Proc. SPIE 8316, 831613 (SPIE, San Diego, CA, 2012). 10.1117/12.912182
21. X. Yang, D. Schuster, V. Master, P. Nieh, A. Fenster, and B. Fei, Automatic 3D segmentation of ultrasound images using atlas registration and statistical texture prior., Medical Imaging: Visualization, Image-Guided Procedures, and Modeling, Proc. SPIE 7964, 796432 (SPIE, San Diego, CA, 2011). 10.1117/12.877888
22. Y. Yu, J. A. Molloy, and S. T. Acton, Segmentation of the prostate from suprapubic ultrasound images., Med. Phys. 31, 3474-3484 (2004). 10.1118/1.1809791
23. Y. Zhan and D. Shen, Deformable segmentation of 3-D ultrasound prostate images using statistical texture matching method., IEEE Trans. Med. Imaging 25, 256-272 (2006). 10.1109/TMI.2005.862744
24. Y. Zhang, R. Sankar, and W. Qian, Boundary delineation in transrectal ultrasound image for prostate cancer., Comput. Biol. Med. 37, 1591-1599 (2007). 10.1016/j.compbiomed.2007.02.008
25. N. Hu, D. B. Downey, A. Fenster, and H. M. Ladak, Prostate boundary segmentation from 3D ultrasound images., Med. Phys. 30, 1648-1659 (2003). 10.1118/1.1586267
26. A. C. Hodge, A. Fenster, D. B. Downey, and H. M. Ladak, Prostate boundary segmentation from ultrasound images using 2D active shape models: Optimisation and extension to 3D., Comput. Methods Programs Biomed. 84, 99-113 (2006). 10.1016/j.cmpb.2006.07.001
27. C. Knoll, M. Alcaniz, V. Grau, C. Monserrat, and M. Carmen Juan, Outlining of the prostate using snakes with shape restrictions based on the wavelet transform (Doctoral Thesis: Dissertation)., Pattern Recogn. 32, 1767-1781 (1999). 10.1016/S0031-3203(98)00177-0
28. T. C. Wang and N. B. Karayiannis, Detection of microcalcifications in digital mammograms using wavelets., IEEE Trans. Med. Imaging 17, 498-509 (1998). 10.1109/42.730395
29. S. G. Mallat, A theory for multiresolution signal decomposition-The wavelet representation., IEEE Trans. Pattern Anal. Mach. Intell. 11, 674-693 (1989). 10.1109/34.192463
30. Y. L. Qiao, J. S. Pan, and S. H. Sun, An experimental comparison on gabor wavelet and wavelet frame based features for image retrieval., Knowledge-Based Intelligent Information and Engineering Systems, Vol. 3683, edited by, R. Khosla, R. J. Howlett, and, L. C. Jain, (Springer, BerlinHeidelberg, 2005), pp. 353-358.
31. M. Unser, Texture classification and segmentation using wavelet frames., IEEE Trans. Image Process. 4, 1549-1560 (1995). 10.1109/83.469936
32. A. Mojsilovic, M. Popovic, S. Markovic, and M. Krstic, Characterization of visually similar diffuse diseases from B-scan liver images using nonseparable wavelet transform., IEEE Trans. Med. Imaging 17, 541-549 (1998). 10.1109/42.730399
33. V. N. Vapnik, The Nature of Statistical Learning Theory (Springer-Verlag, Berlin, 1995).
34. M. Brown, H. G. Lewis, and S. R. Gunn, Linear spectral mixture models and support vector machines for remote sensing., IEEE Trans. Geosci. Remote Sens. 38, 2346-2360 (2000). 10.1504/IJFIPM.2009.027592
35. H. Akbari, Y. Kosugi, K. Kojima, and N. Tanaka, Hyperspectral image segmentation and its application in abdominal surgery., International Journal of Functional Informatics and Personalised Medicine 2, 201-216 (2009). 10.1504/IJFIPM.2009.027592
36. T. L. Faber and E. M. Stokely, Orientation of 3-D structures in medical images., IEEE Trans. Pattern Anal. Mach. Intell. 10, 626-633 (1988). 10.1109/34.6771
37. N. M. Alpert, J. F. Bradshaw, D. Kennedy, and J. A. Correia, The principal axes transformation-a method for image registration., J Nucl. Med. 31, 1717-1722 (1990).
38. D. Cool, D. Downey, J. Izawa, J. Chin, and A. Fenster, 3D prostate model formation from non-parallel 2D ultrasound biopsy images., Med. Image Anal. 10, 875-887 (2006). 10.1016/j.media.2006.09.001
39. I. B. Tutar, S. D. Pathak, L. Gong, P. S. Cho, K. Wallner, and Y. Kim, Semiautomatic 3-D prostate segmentation from TRUS images using spherical harmonics., IEEE Trans. Med. Imaging 25, 1645-1654 (2006). 10.1109/TMI.2006. 884630
40. H. Bustince, M. Pagola, E. Barrenechea, J. Fernandez, P. Melo-Pinto, P. Couto, H. R. Tizhoosh, and J. Montero, Ignorance functions. An application to the calculation of the threshold in prostate ultrasound images., Fuzzy Sets Syst. 161, 20-36 (2010). 10.1016/j.fss.2009.03.005
41. Y. Zhang, X. J. He, and J. H. Han, Texture feature-based image classification using wavelet package transform., Adv. Intell. Comput., Pt 1, 3644, 165-173 (2005). 10.1007/11538059