Browse > Article

Automatic Prostate Segmentation in MR Images based on Active Shape Model Using Intensity Distribution and Gradient Information  

Jang, Yu-Jin (서울여자대학교 컴퓨터학과)
Hong, Helen (서울여자대학교 미디어학부)
Publication Information
Journal of KIISE:Software and Applications / v.37, no.2, 2010 , pp. 110-119 More about this Journal
Abstract
In this paper, we propose an automatic segmentation of the prostate using intensity distribution and gradient information in MR images. First, active shape model using adaptive intensity profile and multi-resolution technique is used to extract the prostate surface. Second, hole elimination using geometric information is performed to prevent the hole from occurring by converging the surface shape to the local optima. Third, the surface shape with large anatomical variation is corrected by using 2D gradient information. In this case, the corrected surface shape is often represented as rugged shape which is generated by the limited number of vertices. Thus, it is reconstructed by using surface modelling and smoothing. To evaluate our method, we performed the visual inspection, accuracy measures and processing time. For accuracy evaluation, the average distance difference and the overlapping volume ratio between automatic segmentation and manual segmentation by two radiologists are calculated. Experimental results show that the average distance difference was 0.3${\pm}$0.21mm and the overlapping volume ratio was 96.31${\pm}$2.71%. The total processing time of twenty patient data was 16 seconds on average.
Keywords
Automatic Segmentation; Active Shape Model; Shape Information; Geometry Information; Gradient Information; Prostate; MRI;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 R. Zwiggelaar, Y. Zhu, S. Williams, "Semiautomatic Segmentation of the Prostate," Lecture Notes in Computer Science 2652, Springer-Verlag, Berlin Heidelberg New York, pp.1108-1116, 2003.
2 A. C. Fan, J. W. Fisher, W. M. Wells, J. J. Levitt, A. S. Willsky, "MCMC Curve Sampling for Image Segmentation," MICCAI, vol.10, no.Pt2, pp.477-485, 2007.
3 N. Betrouni, P. Puech, A. Dewalls, R. Lopes, P. Dubois, M. Vermandel, "3D automatic segmentation and reconstruction of prostate on MR images," Conf. Proc. IEEE EMBS, vol.1, pp.5259-5262, 2007.
4 R. Toth, J. Chappelow, M. Resen, S. Pungavkar, A. Kalyanpur, A. Madabhushi, "Multi-Attribute Non-Initializing Texture Reconstruction Based Active Shape Model(MANTRA)," Lecture Notes in Computer Science, vol.5241, pp.653-661, 2008.
5 Z. Cai, "Restoration of Birnary Images Using Contour Direction Chain Codes Description," Computer Vision Graphics and Image Processing, vol.41, pp.101-106, 1988.   DOI   ScienceOn
6 W. Schroeder, J. Zarge, W. Lorensen, "Decimation of triangle meshes," ACM SIGGRAPH Computer Graphics, vol.26, no.2, pp.65-70, 1992.   DOI
7 S. Vikal, S. Haker, C. Tempany, G. Fichtinger, "Prostate contouring in MRI guided biopsy," MICCAI 2008 Prostate Workshop, 2008.
8 J. Yoo, H. Hong, "Building a Robust 3D Statistical Shape Model of the Mandible," Journal of KIISE : Software and Applications, vol.35, no.2, pp.118-127, 2008.
9 J. Matthews, "An introduction to edge detection: The Sobel Edge Detector," Available at http://www.generation5.org/content/2002/im01.asp
10 S. Klein, UA. Van der Heide, IM. Lips, M. Van Vulpen, M. Staring, JP. Pluim, "Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information," vol.35, no.4, pp.1407-1417, 2008.
11 P. D. Allen, J. Graham, D. C. Williamson, C. E. Hutchinson, "Differential Segmentation of the Prostate in MR Images Using Combined 3D Shape Modelling and Voxel Classification," ISBI IEEE, pp.410-413, 2006.
12 P. J. Besl, N. D. McKay, "A Method for Registration of 3-D Shapes," IEEE, vol.14, no.2, pp.239-256, 1992.
13 B. Lichtenbelt, R Crane, S Naqvi, "Introduction to volume rendering," Prentice-Hall, Inc. Upper Saddle River, NJ, USA.
14 M. Samiee, G. Thomas, R. Fazel-Rezai, "Semi-Automatic Prostate Segmentation of MR Images Based on Flow Orientation," IEEE International Symposium on Signal Processing and Information Technology, pp.203-207, 2006.
15 H. Hong, J. Lee, H. Jo, J. Yoo, "Automatic Generation of the Mandible Bones using Statistical Shape Model in CT Dataset," Proceeding of CARS 2007, vol.2, pp.522-526.
16 Y. Jang, H. Jo, H. Hong, "Automatic Segmentation of the Prostate in MR Images using Image Intensity and Gradient Information," Journal of KIISE: Computing Practices and Letters, vol.15, no.9, pp.695-699, 2009.
17 M. B. Stegmann, D. D. Gomez, "A Brief Introduction to Statistical Shape Analysis," Image Analysis and Computer Graphics, 2002.
18 R. Toth, P. Tiwari, P. Rosen, M. Kalyanpur, A. Pungavkar, S. Madabhushi, A., "A multimodal prostate segmentation scheme by combining spectral clustering and active shape models," Proceedings of the SPIE, vol.6914 pp.69144S-69144S-12, 2008.
19 Y. Zhu, S. Williams, R. Zwiggelaar, "A Hybrid ASM Approach for Sparse Volumetric Data Segmentation," Proc. of Medical Image Inderstanding and Analysis, vol.17, no.2, pp.252-258, 2007.
20 W. Elorensen, H. E. Cline, "Marching cubes: A high resolution 3D surface construction algorithm," Proceedings of SIGGRAPH'87, pp.163-169, 1987.
21 V. Duay, N. Houhou, J. Thiran, "ATLAS-BASED SEGMENTATION OF MEDICAL IMAGES LOCALLY CONSTRAINED BY LEVEL SETS," ICIP, IEEE, vol.2, pp.II: 1286-1289, 2005.
22 S. Klein, UA. Van der Heide, IM. Lips, M. Van Vulpen, M. Staring, JP. Pluim, "Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information," Medical Physics, vol.35, no.4, pp.1407-1417, 2008.   DOI   ScienceOn