Renal cortex segmentation using optimal surface search with novel graph construction

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

Volumn 6893 LNCS, Issue PART 3, 2011, Pages 387-394

  Li, Xiuli ^a   Chen, Xinjian ^b   Yao, Jianhua ^b   Zhang, Xing ^a   Tian, Jie ^a  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b NATIONAL INSTITUTES OF HEALTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA SETS; FALSE POSITIVE; GRAPH CONSTRUCTION; MULTIPLE SURFACES; RENAL CORTEX; SUBGRAPHS; SURFACE SEARCH; TRUE POSITIVE;

COMPUTERIZED TOMOGRAPHY; MEDICAL COMPUTING; MEDICAL IMAGING; OPTIMIZATION; TITRATION;

IMAGE SEGMENTATION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUDIOVISUAL EQUIPMENT; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; DIAGNOSTIC IMAGING; DIFFUSION; HUMAN; KIDNEY CORTEX; METHODOLOGY; PATHOLOGY; REPRODUCIBILITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DIAGNOSTIC IMAGING; DIFFUSION; HUMANS; KIDNEY CORTEX; MODELS, ANATOMIC; MODELS, BIOLOGICAL; PATTERN RECOGNITION, AUTOMATED; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; TOMOGRAPHY, X-RAY COMPUTED;

EID: 80053550320     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-23626-6_48     Document Type: Conference Paper

References (14)

1. Lin, D., Lei, C., Hung, S.: Computer-aided kidney segmentation on abdominal CT images. IEEE T. Inf. Technol. Biomed. 10(1), 59-65 (2006)
2. Cai, W., et al.: Dynamic-Threshold Level Set Method for Volumetry of Porcine Kidney in CT Images: In Vivo and Ex Vivo Assessment of the Accuracy of Volume Measurement. Acad. Radiol. 14(7), 890-896 (2007)
3. Spiegel, M., et al.: Segmentation of kidneys using a new active shape model generation technique based on non-rigid image registration. Comput. Med. Imaging Graph. 33(1), 29-39 (2009)
4. Li, K., et al.: Optimal surface segmentation in volumetric images - A graph-theoretic approach. IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 119-134 (2006)
5. Yin, Y., et al.: LOGISMOS-Layered Optimal Graph Image Segmentation of Multiple Objects and Surfaces: Cartilage Segmentation in the Knee Joint. IEEE Trans. Med. Imaging 29(12), 2023-2037 (2010)
6. Heimann, T., Meinzer, H.: Statistical shape models for 3D medical image segmentation: A review. Med. Image Anal. 13, 543-563 (2009)
7. Zhang, X., et al.: Automatic Liver Segmentation Using a Statistical Shape Model with Optimal Surface Detection. IEEE Trans. Biomed. Eng. 57(10), 2622-2626 (2010)
8. Weickert, J., Romeny, B., Viergever, M.: Efficient and reliable schemes for nonlinear diffusion filtering. IEEE Trans. Image Process. 7(3), 398-410 (1998)
9. Lorensen, W.E., Cline, H.E.: Marching cubes: A high resolution 3D surface construction algorithm. In: SIGGRAPH 1987 Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques, pp. 163-170 (1987)
10. Heimann, T., et al.: Implementing the automatic generation of 3d statistical shape models with itk. In. MICCAI 2006 Open Source Workshop, Copenhagen, Denmark (2006)
11. Stalling, D., Westerhoff, M., Hege, H.: Amira: A highly interactive system for visual data analysis. In: The Visualization Handbook. Elsevier, Amsterdam (2005)
12. Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1124-1137 (2004)
13. Sato, Y., et al.: Tissue classification based on 3D local intensity structures for volume rendering. IEEE Trans. Vis. Comput. Graph. 6(2), 160-180 (2002)
14. Tian, J., Xue, J., Dai, Y., et al.: A novel software platform for medical image processing and analyzing. IEEE T. Inf. Technol. Biomed. 12, 800-812 (2008)