Group sparse reconstruction for image segmentation

Neurocomputing

Volumn 136, Issue , 2014, Pages 41-48

  Lu, Xiaoqiang ^a   Li, Xuelong ^a  

^a XI AN INSTITUTE OF OPTICS AND PRECISION MECHANICS   (China)

Author keywords

Active shape model; Group lasso; One class classifier; Prostate segmentation

Indexed keywords

FACE RECOGNITION; MAGNETIC RESONANCE IMAGING; MEDICAL IMAGE PROCESSING; VECTOR SPACES;

ACTIVE SHAPE MODELS; GROUP LASSOS; NON-GAUSSIAN DISTRIBUTION; ONE-CLASS CLASSIFICATION; ONE-CLASS CLASSIFIER; PROSTATE SEGMENTATION; SEGMENTATION OF MEDICAL IMAGES; STATISTICAL INFORMATION;

IMAGE SEGMENTATION;

ARTICLE; CLASSIFIER; HUMAN; IMAGE PROCESSING; IMAGE RECONSTRUCTION; IMAGE SEGMENTATION; INTERMETHOD COMPARISON; MAJOR CLINICAL STUDY; MALE; MEASUREMENT ACCURACY; NUCLEAR MAGNETIC RESONANCE IMAGING; NUCLEAR MAGNETIC RESONANCE SCANNER; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROSTATE;

EID: 84897940097     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.01.034     Document Type: Article

References (31)

1. Wang B., Gao X., Tao D., Li X. A unified tensor level set for image segmentation. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 2010, 40(3):857-867.
2. Gao X., Wang B., Tao D., Li X. A relay level set method for automatic image segmentation. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 2011, 41(2):518-525.
3. J. Winn, N. Jojic, Locus: learning object classes with unsupervised segmentation, in: Proceedings of International Conference on Computer Vision, 2005, pp. 756-763.
4. M. Klodt, D. Cremers, A convex framework for image segmentation with moment constraints, in: Proceedings of International Conference on Computer Vision, 2011, pp. 2236-2243.
5. Li L., Jiang S., Huang Q. Learning hierarchical semantic description via mixed-norm regularization for image understanding. IEEE Trans. Multimedia 2012, 14(5):1401-1403.
6. D. Lowe, Object recognition from local scale-invariant features, in: Proceedings of International Conference on Computer Vision, 1999, pp. 1150-1157.
7. Y. Boykov, M. Jolly, Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images, in: Proceedings of International Conference on Computer Vision, 2001, pp. 105-112.
8. Werghi N. Segmentation and modeling of full human body shape from 3-d scan data. a survey. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 2007, 37(6):1122-1136.
9. Y. Zheng, A. Barbu, B. Georgescu, M. Scheuering, D. Comaniciu, Fast automatic heart chamber segmentation from 3d CT data using marginal space learning and steerable features, in: Proceedings of International Conference on Computer Vision, 2007, pp. 1-8.
10. D. Cobzas, N. Birkbeck, M. Schmidt, M. Jagersand, A. Murtha, 3d variational brain tumor segmentation using a high dimensional feature set, in: Proceedings of International Conference on Computer Vision, 2007, pp. 1-8.
11. Behrens T., Rohr K., Stiehl H. Robust segmentation of tubular structures in 3-d medical images by parametric object detection and tracking. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 2003, 33(4):554-561.
12. Yan P., Xu S., Baris T., Kruecker J. Discrete deformable model guided by partial active shape model for trus image segmentation. IEEE Trans. Biomed.Eng. 2010, 57(5):1158-1166.
13. B. Davis, M. Foskey, J. Rosenman, L. Goyal, S. Chang, S. Joshi, Automatic segmentation of intra-treatment CT images for adaptive radiation therapy of the prostate, in: Proceedings of Medical Image Computing and Computer Assisted Intervention, 2005, pp. 442-450.
14. Li C., Huang R., Ding Z., Gatenby C., Metaxas D., Gore J. A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans. Image Process. 2011, 20(7):2007-2016.
15. F. Shi, L. Wang, G. Wu, Y. Zhang, J.H. Gilmore, W. Lin, D. Shen, Atlas construction via dictionary learning and group sparsity, in: Proceedings of Medical Image Computing and Computer Assisted Intervention, 2012, pp. 247-255.
16. Pham D., Xu C., Prince J. A survey of current methods in medical image segmentation. Annu. Rev. Biomed. Eng. 2000, 2(1):315-337.
17. Shen D., Zhan Y., Davatzikos C. Segmentation of prostate boundaries from ultrasound images using statistical shape model. IEEE Trans. Med. Imaging 2003, 22(4):539-551.
18. Leemput K., maes F., Vandermeulen D., Suetens P. Automated model-based tissue classification of mr images of the brain. IEEE Trans. Med. Imaging 1999, 18(10):897-908.
19. Y. Cao, Y. Yuan, X. Li, B. Turkbey, P. Choyke, P. Yan, Segmenting images by combining selected atlases on manifold, in: Proceedings of Medical Image Computing and Computer Assisted Intervention, 2011, pp. 272-279.
20. Cootes T., Taylor C., Cooper D. Active shape models-their training and application. Comput. Vis. Image Underst. 1995, 61(1):38-59.
21. Heimann T., Meinzer H.-P. Statistical shape models for 3d medical image segmentation. a review. Med. Image Anal. 2009, 13(4):543-563.
22. Ginneken B., Frangi A., Staal J., Romeny B., Viergever M. Active shape model segmentation with optimal features. IEEE Trans. Med. Imaging 2002, 21(8):924-933.
23. J. Zhang, S.K. Zhou, D. Comaniciu, LeonardMcMillan, Discriminative learning for deformable shape segmentation: a comparative study, in: Proceedings of European Conference on Computer Vision, 2008, pp. 711-724.
24. Zheng Y., Barbu A., Georgescu B., Scheuering M., Comaniciu D. Four-chamber heart modeling and automatic segmentation for 3-d cardiac CT volumes using marginal space learning and steerable features. IEEE Trans. Med. Imaging 2008, 27(11):1668-1681.
25. Y. Gao, S. Liao, D. Shen, Prostate segmentation by sparse representation based classification, in: Proceedings of Medical Image Computing and Computer Assisted Intervention, 2012, pp. 451-458.
26. Mishra A., Fieguth P., Clausi D. Decoupled active contour (DAC) for boundary detection. IEEE Trans. Pattern Anal. Mach. Intell. 2011, 33(2):310-324.
27. Peters J., Ecabert O., Meyer C., Kneser R., Weese J. Optimizing boundary detection via simulated search with applications to multi-modal heart segmentation. IEEE Trans. Med. Imaging 2010, 14(1):70-84.
28. Ratsch G., Mika S., Scholkopf B., Muller K. Constructing boosting algorithms from svms. an application to one-class classification. IEEE Trans. Pattern Anal. Mach. Intell. 2002, 24(9):1184-1199.
29. Tibshirani R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. 1996, 58(1):267-288.
30. L. Zini, F. Odone, Efficient pedestrian detection with group lasso, in: Proceedings of International Conference on Computer Vision, 2011, pp. 1777-1784.
31. Toth R., Tiwari P., Rosen M., Reed G., Kurhanewicz J., Kalyanpur A., Pungavkar S., Madabhushi A. A magnetic resonance spectroscopy driven initialization scheme for active shape model based prostate segmentation. Med. Image Anal. 2011, 15(2):214-225.