Journal of Zhejiang University SCIENCE
(ISSN 1009-3095, Monthly)

2003   Vol. 4   No. 3   p.294-299


A new algorithm of brain volume contours segmentation

WU Jian-ming(Î⽨Ã÷)(Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai 200030, China)
SHI Peng-fei(Ê©Åô·É)(Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai 200030, China)

Abstract:This paper explores brain CT slices segmentation technique and some related problems, including contours segmentation algorithms, edge detector, algorithm evaluation and experimental results. This article describes a method for contour-based segmentation of anatomical structures in 3D medical data sets. With this method, the user manually traces one or more 2D contours of an anatomical structure of interest on parallel planes arbitrarily cutting the data set. The experimental results showes the segmentation based on 3D brain volume and 2D CT slices. The main creative contributions in this paper are: (1) contours segmentation algorithm; (2) edge detector; (3) algorithm evaluation.
Keywords:CT slices, Contours segmentation, Edge detector

CLC Number:TP242.6 Document ID:A

Foundation Item:Project(No.69931010) supported by the National Natural Science Foundation of China
Author Resume:WU Jian-ming,E-mail: wjm010@sjtu.edu.cn

References:

[1]Bao, X.D. and Xiao, S.J., 1998. Three-dimensional segmentation of CT images using neural network. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 20 (2).
[2]Feng, T., 2002. An unified approach to missile guidancelaws: a 3D extension. American Control Conference, 2: 1711-1716.
[3]Flyun, P. J., 1990. CAD-Based Computer Vision Modeling and Recognition Strategies. PhD thesis, Michigan State University.
[4]Ge, Z.Y., Venkatesan, V. and Mitra, S., 2001. A statistical 3-D segmentation algorithm for classifying brain tissues in multiple sclerosis. In: Computer-Based Medical Systems. CBMS 2001. Proceedings of 14th IEEE Symposium, p. 455-460.
[5]Luca Foresti, G. and Pieroni, G., 1998. Exploiting neural in range image understanding. Pattern Recognition Letters, 19 (2): 869-878.
[6]Marcondes, R., Cesar, C. Jr. and Luciano da Fontoura, C., 1999. Computr-vision-based extraction of neural dendrograms. Journal of Neurosience Methods, 93 (5): 121-131.
[7]Masato, Y. and Hasegawa, M., 1996. Extraction of Brain Tissues by Non-parametric Region Growing Method. In: MR CT Brain Image Analysis.18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Amsterdam.
[8]Poli, R., Cagnoni, S. and Valli, G., 1992. Genetic and learning automata algorithms for adaptive digital filters. Proc. ICASSP-92, 4 (1): 41-44.
[9]Shen, D., Herskovits, E. H. and Davatzikos, C., 2001. An adaptive-focus statistical shape model for segmentation and shape modeling of 3-D brain structures. Medical Imaging, IEEE Transactions on, 20 (4): 257-270.
[10]Winterer, J. T., Schaefer, O. and Uhrmeister, P., 2002. Contrast enhanced MR angiography in the assessment of relevant stenoses in occlusive disease of the pekvic and lower limb arteries: diagnostic value of a two-step examination protocol in comparision. European Journal of Radiology, 41(2): 153-160.
[11]Wyatt, C. L., Ge, Y. and Vining, D. J., 2000. Automatic segmentation of the colon for virtual colonoscopy. Computerized Medical Imaging and Graphics, 24 (3): 1-9.


Manuscript Received:2002 July 6

Manuscript Revised:2002 Sept. 29

Published:2003 June 1