Facilitating 3D spectroscopic imaging through automatic prostate localization in MR images using random walker segmentation initialized via boosted classifiers

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6963 LNCS, Issue , 2011, Pages 47-56

  Khurd, Parmeshwar ^a   Grady, Leo ^a   Gajera, Kalpitkumar ^a   Diallo, Mamadou ^a   Gall, Peter ^b   Requardt, Martin ^b   Kiefer, Berthold ^b   Weiss, Clifford ^c   Kamen, Ali ^a  

^a SIEMENS CORPORATE RESEARCH   (United States)

^b SIEMENS AG   (Germany)

^c DEPARTMENT OF PATHOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADVANCED ANALYSIS; AUTOMATIC SEGMENTATIONS; BOOSTED CLASSIFIERS; CENTER DETECTION; EXPECTATION MAXIMIZATION; GAUSSIAN MIXTURE MODEL; INHOMOGENEITIES; INTENSITY-BASED; KEY COMPONENT; LARGE DATABASE; MR IMAGES; MULTI-LABEL; MULTI-LEVEL; NEAR-FIELD; PROSTATE ANATOMY; PROSTATE CANCERS; PROSTATE GLANDS; PROSTATE LOCALIZATION; PROSTATE SEGMENTATION; RANDOM WALKERS; SEEDING PROCESS; SHAPE MODEL; SPECTROSCOPIC IMAGING; TREATMENT MONITORING; TURBO SPIN ECHOS;

DIAGNOSIS; DISEASES; IMAGE ANALYSIS; IMAGE SEGMENTATION; MEDICAL IMAGING; RANDOM PROCESSES; SPECTROSCOPIC ANALYSIS; THREE DIMENSIONAL;

MAGNETIC RESONANCE IMAGING;

EID: 80053480142     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-23944-1_5     Document Type: Conference Paper

References (20)

1. Betrouni, N., Puech, P., Dewalle, A., Lopes, R., Dubois, P., Vermandel, M.: 3D automatic segmentation and reconstruction of prostate on MR images. In: IEEE EMBS Conf. (2007)
2. Bishop, C.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)
3. Dobkin, D., Kirkpatrick, D.: Determining the separation of preprocessed polyhedra - a unified approach. Automata, Languages and Programming 443, 400-413 (1990)
4. Flores-Tapia, D., Thomas, G., Venugopal, N., McCurdy, B., Pistorius, S.: Semi-automatic MRI prostate segmentation based on wavelet multiscale products. In: IEEE EMBS Conf. (2008)
5. Franiel, T., Ludemann, L., Rudolph, B., Rehbein, H., Stephan, C., Taupitz, M., Beyersdorff, D.: Prostate MR imaging: Tissue characterization with pharmacokinetic volume and blood flow parameters and correlation with histologic parameters. Radiology 252(1), 101-108 (2009)
6. van Ginneken, B., Heimann, T., Styner, M.: 3D segmentation in the clinic: A grand challenge. In: MICCAI Wshp. 3D Segmentation in the Clinic (2007)
7. Gong, L., Pathak, S., Haynor, D., Cho, P., Kim, Y.: Parametric shape modeling using deformable superellipses for prostate segmentation. TMI 23(3) (2004)
8. Grady, L.: Random walks for image segmentation. IEEE Pattern Analysis and Machine Intelligence 28(11), 1768-1783 (2006)
9. Klein, S., van der Heide, U.A., Lips, I., van Vulpen, M., Maes, F., Staring, M., Pluim, J.: Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. Medical Physics 35(4), 1407-1417 (2008)
10. Leemput, K.V., Maes, F., Vandermeulen, D., Suetens, P.: Automated model-based bias field correction of MR images of the brain. TMI 18(10), 885-896 (2003)
11. Liu, X., Langer, D.L., Haider, M.A., Der Kwast, T.H.V., Evans, A.J., Wernick, M.N., Yetik, I.S.: Unsupervised segmentation of the prostate using MR images based on level set with a shape prior. In: IEEE EMBS Conf. (2009)
12. Rousson, M., Khamene, A., Diallo, M.H., Celi, J.C., Sauer, F.: Constrained surface evolutions for prostate and bladder segmentation in CT images. In: Liu, Y., Jiang, T.-Z., Zhang, C. (eds.) CVBIA 2005. LNCS, vol. 3765, pp. 251-260. Springer, Heidelberg (2005) (Pubitemid 43741532)
13. Samiee, M., Thomas, G., Fazel-Rezai, R.: Semi-automatic prostate segmentation of MR images based on flow orientation. In: IEEE International Symposium on Signal Processing and Information Technology (2006)
14. Scheenen, T., Heijmink, S., Roell, S., De Kaa, C.H., Knipscheer, B., Witjes, J., Barentsz, J., Heerschap, A.: Three-dimensional proton MR spectroscopy of human prostate at 3 T without endorectal coil. Radiology 245(2), 507-516 (2007)
15. Toth, R., Chappelow, J., Rosen, M.A., Pungavkar, S., Kalyanpur, A., Madabhushi, A.: Multi-attribute non-initializing texture reconstruction based active shape model (MANTRA). In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 653-661. Springer, Heidelberg (2008)
16. 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. Medical Image Analysis 15, 214-225 (2011)
17. Tsai, A., Yezzi, A., Wells, W., Tempany, C., Tucker, D., Fan, A., Grimson, W., Willsky, A.: A shape-based approach to the segmentation of medical imagery using level sets. TMI 22(2), 137-154 (2003)
18. Tu, Z., Zhou, X., Barbu, A., Bogoni, L., Comaniciu, D.: Probabilistic 3D polyp detection in CT images: The role of sample alignment. In: CVPR (2006)
19. Turkbey, B., Pinto, P., Choyke, P.L.: Imaging techniques for prostate cancer: implications for focal therapy. Nature Reviews: Urology 6, 191-203 (2009)
20. Viola, P., Jones, M.: Robust real-time face detection. Int. J. Comp. Vision 57(2), 137-154 (2004)