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研究生：	郭文豐 Kuo, Wen-Feng
論文名稱：	以自動高度選取及群集演算法減除分水嶺轉換之過度分割問題 Oversegmentation Reduction for Watershed Transform Using Altitude Selection and Clustering Algorithms
指導教授：	孫永年 Sun, Yung-Nien
學位類別：	博士 Doctor
系所名稱：	電機資訊學院 - 資訊工程學系 Department of Computer Science and Information Engineering
論文出版年：	2009
畢業學年度：	97
語文別：	英文
論文頁數：	100
中文關鍵詞：	懲罰糢糊式霍普非爾神經網路、分水嶺、馬可夫隨機場、競爭式霍普非爾集群網路
外文關鍵詞：	Penalized fuzzy Hopfield neural network, Watershed, Markov random field, Competitive Hopfield clustering network
相關次數：	點閱：88 下載：1
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分水嶺轉換 (Watershed transformation) 已被證實是影像分析的重要工具。然而因影像區域之不規則與雜訊，造成分水嶺影像切割時，無法避免地產生過度分割 (Over-segmentation) 的現象。在傳統的分水嶺轉換方法中，浸沒 (Immersion) 階段的影像梯度高度(Gradient altitude)被預設為一。本篇論文所提出的方法，則可以隨著不同的影像特性，估算出影像適合的梯度高度。此一高度值是依據熵 (Entropy)，從影像切割結果計算而得，並作為分水嶺泛濫 (Flooding) 時的臨界高度。雖然適合的梯度高度能夠順利的降低區域個數，但影像切割的品質仍可進一步改進。所以我們採用馬可夫隨機場 (Markov random field) 模型，將具相近統計特性的相似相鄰區域進行區域合併，藉以提昇切割品質。
除了調整梯度高度，區域合併是消除分水嶺過度分割的另一重要技術。在本論文中，我們結合分水嶺演算法與類神經集群演算法，提出一個新的區域合併技術。而所採用的集群演算法包括競爭式霍普非爾集群網路 (Competitive Hopfield clustering network)、糢糊式C平均值 (Fuzzy c-mean) 與懲罰糢糊式霍普非爾神經網路 (Penalized fuzzy Hopfield neural network) 等演算法。因此，此一方法即是由空間域(分水嶺)對應到特徵空間(集群)，以探討區域間之相似性，並選擇適當的區域合併。
在實作上，使用多張標準影像作為實驗驗証。當與道統方法作比較時，本篇論文所提出的方法，可以增進影像切割品性，並獲得較佳之影像品性指標。所以實驗證明本論文所提出的技術，可以成功減除道統分水嶺轉換方法中，最嚴重的雜訊問題，所導致的過度切割，並帶來更顯著的切割結果。

Watershed transformation has been proven to be an important tool in image analysis. However, due to the presence of noise and region irregularities, the resulting image of watershed transformation is inevitably over-segmented. In conventional watershed transformation, the adopted image gradient altitude in the immersion stage of flooding a watershed image is one. The proposed method assigns a proper gradient altitude which is used as the critical altitude of watershed flooding according to its image characteristics. The optimal altitude is computed based on the entropy estimated from the image segmentation result. Although the proper altitude can successfully reduced the number of regions, the segmentation results can be further refined. Therefore, we adopt the Markov random field (MRF) model to merge neighboring regions that are similar in local statistic properties to improve the quality of image segmentation.
In addition to adjusting the gradient altitude, region merging is the other important technique to remedy the watershed over-segmentation problem. In this dissertation, we propose new region merging techniques by using neural clustering algorithms embedded in the watershed algorithm. The adopted neural clustering algorithms include competitive Hopfield clustering network, fuzzy c-mean and penalized fuzzy Hopfield neural network. By employing the three clustering algorithms in the watershed process, the inter-region similarities are investigated based on image mapping between the spatial domain (watershed) and feature spaces (clustering) in order to determine the optimal region merging.
In the experimental studies, the proposed method has been tested by using several benchmark images. The proposed methods achieve improved image segmentation and better quality indices in comparison with the conventional methods. Experimental results show that the proposed methods successfully eliminate the undesirable over-segmentation that is the most annoying problem in conventional watershed segmentation.

Abstract in English… … … … … … … …… … … … … I
Abstract in Chinese… … … … … … …… … … … … … III
Acknowledgements… … … … … … … … … … … … … . V
Table of contents… … … … … … ……………… … … … VI
List of tables… … … …  … …… … … … …… …….. VIII
List of figures… … … … … … … … … … ………….. IX
Nomenclature… … … … … … … … … … … … … ….. XII
Chapter 1 Introduction… … … … … … … … … … …. 1
1.1 Watershed transformation… … … … … … … … …. 1
1.2 Oversegmentation reduction based on altitude
    selection … ……………… … … … … … … … …….3
1.3 Oversegmentation reduction by combining the
    watershed segmentation with the clustering
    algorithm ………………………………………………………5
1.4. Dissertation Organization… … … … …  … … …. 7
Chapter 2 Watershed segmentation with automatic altitude
          selection and region merging based on the
          Markov random field model… … … … … ……..8
2.1. Watershed Segmentation… … … … … … …………...8
2.2. Altitude of contours in immersion… … … … … ….10
2.2.1 Evaluation of image segmentation results… … … .10
2.2.2 Optimal altitude of contours in immersion… ………13
2.3. Markov random field merging strategy… … … … … 15
2.3.1 Markov random field model on the region adjacency
       graph… … … … … … ………………………………. 15
2.3.2. Energy computation and region merging… … … … 16
Chapter 3 Image segmentation using the region similarity
          between watershed and competitive Hopfield
          clustering network algorithm……………………. 20
3.1 Competitive Hopfield clustering network… … … … .21
3.2 Merging strategy… … … … … … … … … … … … 23
3.2.1. Computation of the mapping of spatial to
       clustering statistical ratio… ………………… … 24
3.2.2. Merging based on statistical ratio for the
        proposed method… … … … … … ………………… 27
Charter 4 Image segmentation using the region similarity
          between watershed and fuzzy clustering
          algorithms ………………………………………………29
4.1 Fuzzy c-means (FCM) algorithm and its application in
    image segmentation.… ……………………………………….29
4.1.1 Fuzzy c-means clustering representation… … … ….30
4.1.2 Spatial–Clustering fusion method.… ............. 31
4.1.2.1 Markov random field model based on the Gibbs
        energy function… … ……………………………………31
4.1.2.2 Computing mapping spatial to clustering statistic
         probability… … ......... ………………………….32
4.1.2.3 Merging based on statistical probability for the
        proposed method… ……………………………………….35
4.2. Penalized fuzzy Hopfield neural network (PFHNN) algorithm and its application in image segmentation……….36
4.2.1 PFHNN clustering algorithm…...................... .37
4.2.2 Merging based on statistical probability for the Watershed–PFHNN fusion method … … … … … … … … .. 43
4.2.2.1 Computing mapping spatial to clustering statistic probability… … … … . 43
4.4.2.2 Merging based on statistical probability for the proposed method… … .. 47
Chapter 5 Experimental results and discussion … … … .. 50
5.1 Experimental results and discussion in watershed
    immersion altitude… … ……………………………. … … 50
5.2 Experimental results and discussion in clustering
     algorithm… … … … .… .............……………….. 61
5.2.1 Experimental results and discussion in competitive Hopfield clustering network (CHCN) algorithm………………. 64
5.2.2 Experimental results in fuzzy c- means (FCM)
      clustering algorithm… … …………………………… .. 69
5.2.3 Experimental results in experimental results in penalized fuzzy Hopfield neural network (PFHNN) algorithm.77
Chapter 6 Conclusions… … … … … … … … … … … ... 86
References… … … … … …  … … …… … … … … … .. 88
Vita… … … … … … … … …  … … … … … … … … …98
List of Publications… … … … … … … … … … … … . 99
                                    

[1] R. M. Harlick and L.G. Shapiro, “Image segmentation techniques,” Computer Vision, Graphics, and Image Processing, 29 (1985) 100–132.
[2] N. Pal and S. Pal, “A review on image segmentation techniques,” Pattern Recognition, 26 (1993) 1277–1294.
[3] L. Vincent and P. Soille, “Watersheds in digital spaces: An efficient algorithm based on immersion simulations,” IEEE Trans. Pattern Analysis and Machine Intelligence, 13 (6) (1991) 583–593.
[4] A. Bieniek and A. Moga, “An efficient watershed algorithm based on connected components,” Pattern Recognition, 33 (2000) 907–916.
[5] YC Lin, YP Tsai, YP Hung and ZC Shih, “Comparison between immersion-based and toboggan-based watershed image segmentation,” IEEE Trans. on Image Processing, 15 (3) (2006) 632–640.
[6] F. Meyer, “Topographic distance and watershed lines,” Signal Processing, 38 (1) (1994) 113–125.
[7] Maria Frucci, “Oversegmentation reduction by flooding regions and digging watershed lines,” International Journal of Pattern Recognition and Artificial Intelligence, 20 (2006) 15–38.
[8] S. Beucher and F. Meyer, “The morphological approach to segmentation: the watershed transformation,” Mathematical Morphology in Image Processing, New York, 1993, pp. 433–481.
[9] J. B. T. M. Roerdink and A. Meijster, “The watershed transform: Definitions, algorithms and parallelization strategies,” Fundamenta Informaticae, 41 (2000) 187–228.
[10] S.R. Sternberg, “Grayscale morphology,” Computer Vision Graphics Image Process, 35 (1986) 333–355.
[11] A. Blean, J. Guise, A.R. LeBlanc, “A new set fast algorithms for mathematical morphology: Idempotent geodesic transforms,” CVGIP Image Understanding, 56 (2) (1992) 178–209.
[12] L. Najman, M. Schmitt, “Watershed of a continuous function,” Signal Processing, 38 (1) (1994) 99–112.
[13] F. Meyer and S. Beucher, “Morphological segmentation,” Visual Communication and Image Representation, 1 (1990) 21–46.
[14] A.N. Moga, B. Cramariuc, and M. Gabbouj, “A parallel watershed algorithm based on rainfalling simulation,” Proceedings 12th European Conference on Circuit Theory and Design, 1 (1995) 339–342.
[15] Richard Beare, “A locally constrained watershed transform,” Member, IEEE Trans. Pattern Analysis and Machine Intelligence., 28 (7) (2006) 1063–1074.
[16] P.S. Umesh Adiga, B.B. Chaudhuri, “Region based techniques for segmentation of volumetric histo-pathological images,” Computer Methods and Programs in Biomedicine, 68 (2002) 185–193.
[17] P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Trans. Pattern Analysis and Machine Intelligence, 12 (7) (1990) 629–639.
[18] Y. You, W. A. Tannenbaum, and M. Kaveh, “Behavioral analysis of anisotropic diffusion in image process,” IEEE Trans. Image Processing, 5 (11) (1996) 1539–1553.
[19] J.Weickert, B.M. ter Haar Romeny, and M.A. Viergever, “Efficient and reliable schemes for nonlinear diffusion filtering,” IEEE Trans. Image Processing, 7 (3) (1998) 398–410.
[20] J. Weickert, “Anisotropic Diffusion in Image Processing,” Stuttgart, Germany: Teubner-Verlag, 1998.
[21] K. Haris, S.N. Efstratiadis, N. Maglaveras, A.K. Katsaggelos, ”Hybrid image segmentation using watershed and fast region merging,” IEEE Trans. Image Processing, 7 (12) (1988) 1684–1699.
[22] L. Vincent, “Morphological Grayscale Reconstruction in Image Analysis: Applications and Efficient Algorithms”, IEEE Trans. Image Processing, 2 (2) (1993) 176–201.
[23] M. Grimaud, “A new measure of contrast: the dynamics,” Proceeding SPIE Image Algebra and Morphological Processing III, San Diego (1992) 292–305.
[24] C. Vauchier and F. Meyer, “Extinction Values: A new measurement of Persistence,” Proceeding of IEEE Workshop on Non-Linear Signal/Image Processing, (1995) 254–257.
[25] I.E. Pratikakis, H. Sahli, J. Cornelis, “Low level image partitioning guided by the gradient watershed hierarchy,” Signal Processing, 75 (2) (1999) 173–195.
[26] S. Peng and L. Gu, “A Novel Implementation of Watershed Transform Using Multi-Degree Immersion Simulation,” Proceedings IEEE 27th Annual Conference Engineering in Medicine and Biology (2005) 1–4.
[27] T. Geraud, J. F. Mangin, I. Bloch, and H. Maitre, “Segmenting the internal structures in 3D MR images of the brain by Markovian relaxation on a watershed based adjacency graph,” Proceeding IEEE International Conference Image Processing, 3 (1995) 548–551.
[28] B. Ogor, V. Haese-Coat, and K. Kpalma, “Cooperation of mathematical morphology and region growing for remote sensing region growing,” Image Signal Process. Remote Sensing II. 2579 (1995) 375–386.
[29] Andrè Bleau and L. Joshua Leon, “Watershed-Based Segmentation and Region Merging,” Computer Vision and Image Understanding, 77 (2000) 317–370.
[30] S.E. Hernandez, K.E. Barner and Y. Yuan, “Region merging using homogeneity and edge integrity for watershed-based image segmentation,” Optical Engineering, 44 (1) (2005) 25–32.
[31] S. Makrogiannis, G. Economou, S. Fotopoulos and N. G. Bourbakis, “Segmentation of Color Images Using Multiscale Clustering and Graph Theoretic Region Synthesis,” IEEE Trans. on Systems, Man, and Cybernetics-Part A: systems and Humans, 35 (2) (2005) 224–238.
[32] L. Patino, “Fuzzy relations applied to minimize over segmentation in watershed algorithms,” Pattern Recognition Letters, 26 (6) (2005) 819–828.
[33] S. Makrogiannis, G. Economou, S. Fotopoulos and N. G. Bourbakis, “A Region Dissimilarity Relation That Combines Feature-Space and Spatial Information for Color Image Segmentation,” IEEE Trans. On Systems, Man, and Cybernetics-Part B: Cybernetics, 35 (1) (2005) 44–53.
[34] Y.L. Huang, and D.R. Chen, “Watershed segmentation for breast tumor in 2 D sonography,” Ultrasound in Medical and Biology,30 (3) (2004) 625–632.
[35] W.F. Kuo, C.Y Lin and Y.N. Sun, “Region similarity relationship between watershed and penalized fuzzy Hopfield neural network algorithms for brain image segmentation,” International Journal of Pattern Recognition and Artificial Intelligence, 22 (7), (2008) 1403–1425.
[36] X. Wu., “Adaptive split-merge segmentation based on piecewise least-square approximation,” IEEE Trans. Pattern Analysis and Machine Intelligence, 15 (8) (1993) 808–815.
[37] M. Suk and T.H. Cho, “An Object-Detection Algorithm Based on the Region-Adjacency Graph,” Proceedings of the IEEE, 72 (7) (1984) 985–986
[38] J.C. Dunn, “A fuzzy Relative of the ISODATA process and its use in detecting compact well-separated clusters,” Journal of Cybernetics, 3 (3) (1973) 32–57.
[39] J.C. Bezdek, “Fuzzy mathematics in Pattern Classification,” Ph.D. Dissertation, Applied Mathematics, Cornell University, Ithaca, New York, 1973.
[40] M.A. Ismail, S.Z. Selim, “Fuzzy c-mean: Optimality of solutions and effective termination of the algorithm,” Pattern Recognition, 19 (6) (1986) 481–485.
[41] M.S. Yang, “On a class of fuzzy classification maximum likelihood procedures,” Fuzzy Sets and Systems, 57 (3) (1993) 365–375.
[42] J. Besag, “Spatial interaction and the statistical analysis of lattice systems,” Royal Statistical Society, Series B 36, (1974) 192–236.
[43] Stan Z Li. Markov Random Field Modeling in Image Analysis. 1-42, New York: Spring Verlag; 2001.
[44] P.C. Chung , C.T. Tsai, E.L. Chen, and Y.N. Sun, “Polygonal approximation using a competitive Hopfield neural network,” Pattern Recognition, 27 (1994) 1505–1512.
[45] K.S. Cheng, J.S. Lin, and C.W. Mao, “The application of competitive Hopfield neural network to medical image segmentation,” IEEE Trans. Medical Imaging, 15 (4) (1996) 560–567.
[46] J.S. Lin, and C.Y. Lin, “A penalized fuzzy Hopfield neural network and its application in image compression,” Proceedings of the 8th Int. Fuzzy Systems Association World Congress, 2 (1999) 843–848.
[47] F. Meyer, “An Overview of Morphological Segmentation,” International Journal of Pattern Recognition and Artificial Intelligence, 15 (7) (2001) 1089–1118.
[48] B. Fischl, and EL Schwartz, “Adaptive Nonlocal Filtering: a Fast Alternative to Anisotropic Diffusion for Image Enhancement,” IEEE Trans. Pattern Analysis and Machine Intelligence, 21 (1) (1999) 42–48.
[49] G. Gilboa, N. Sochen, Y. Y. Zeevi, “Complex Diffusion Processes for Image Filtering”, IEEE Trans. Pattern Analysis and Machine Intelligence, 26 (8) (2004) 1020–1036.
[50] A. Jain, Fundamentals of Digital Image Processing, Englewood Cliffs, New York; Pentice-Hall, 1989.
[51] J. Liu and Y.-H Yang, “Multiresolution Color Image Segmentation,” IEEE Trans. Pattern Analysis and Machine Intelligence, 16 (7) (1994) 689–700.
[52] M. Borsotti, P. Campadelli and R. Schettini, “Quantitative Evaluation of Color Image Segmentation Results,” Pattern Recognition Letter, 19 (1998) 741–747.
[53] H. Zhang, J.E. Fritts, and S.A. Goldman, “An entropy-based objective segmentation evaluation method for image segmentation,” SPIE Electronic Imaging - Storage and Retrieval Methods and Applications for Multimedia (2004) 38–49.
[54] H. Derin, W.S. Cole, “Segmentation of textured images using using Gibbs random fields,” Computer Vision, Graphics and Image Processing, 35 (1) (1986) 72–98.
[55] J.S. Lin, “Annealed Hopfield neural network with moment and entropy constraints for magnetic resonance image classification,” IEICE Trans. on Inf. & Syst., E83-D(1) (2000) 100–108.
[56] R.M. Chen, Y.M. Huang, and C.Y. Lin, “Competitive neural network to solve real-time scheduling,” International Computer Symposium, (2002) 1–8.
[57] S.H. Liu and J.S. Lin, “Vector quantization in DCT domain using fuzzy possibilistic C-Mean based on penalized and compensated constraints,” Pattern Recognition, 35 (2002) 2201–2211.
[58] J.S. Lin and S.H. Liu, “Classification of multispectral images based on a fuzzy-possibilistic neural network,” IEEE Trans. on Systems, Man, and Cybernetics, 32 (4) (2002) 499–506.
[59] C.Y. Lin and C.H. Chen, “Image compression using grey-based Hopfield neural network and block truncation Coding,” Proceedings of the 20th Workshop on Combinatorial Mathematics and Computation Theory, (2003) 193–198.
[60] Y. Wang, T. Adah, S.Y. Kung, and Z. Szabo, “Quantification and segmentation of brain tissues from MR images: a probabilistic neural network approach,” IEEE Trans. Image Processing, 7 (8) (1998) 11651181.
[61] R. Deklerck, J. Guo, etc., “A Method to Construct Flat Maps of the Brain’s Surface and its Application,” International Journal of Pattern Recognition and Artificial Intelligence, 20 (2006) 679-709.
[62] J. L. Marroquin, B.C. Vemuri, S. Botello, F. Calderon, and A. Fernandez-Bouzas, “An accurate and efficient Bayesian method for automatic segmentation of brain MRI,” IEEE Trans. Medical Imaging, 21 (8) (2002) 934945.
[63] M.A. González Ballester, A. Zisserman, and M. Brady, “Segmentation and measurement of brain structures in MRI including confidence bounds,” Medical Image Analysis Journal, 4 (3) (2000) 189-200.
[64] K. Yuan, L.W. Wu and Q.S. Cheng, “A Novel Fuzzy C-Means Algorithm and Its Application,” International Journal of Pattern Recognition and Artificial Intelligence, 19 (2005) 1059–1066.
[65] M. Omran. and A. P. Engelbrecht, “Particle Swarm Optimization Method for Image Clustering,” International Journal of Pattern Recognition and Artificial Intelligence, 19 (2005) 297–321.
[66] H. Elliott, H. Derin, R. Cristi, D. Geman, “Application of the Gibbs distribution to image segmentation,” Proc. of the International Conference on Acoustic, Speech and Signal Processing, (1984) 32.5.1–32.5.4.
[67] H. Derin, H. Elliott, “Modeling and segmentation of noisy and textured images using Gibbs random fields,” IEEE Trans. Pattern Analysis and Machine Intelligence, 9 (1) (1987) 39–55.
[68] A.K. Jain, M.N. Murty and P.J. Flynn, “Data clustering: a review,” ACM Computing Surveys, 31 (3) (1999) 264–323.
[69] Z. Wu and R. Leahy, “An optimal graph theoretic approach to data clustering: Theory and its application to image segmentation,” IEEE Trans. Pattern Analysis and Machine Intelligence, 15 (11) (1993) 1101–13.
[70] M.N. Ahmed, S.M. Yamany, N. Mohamed, A.A. Farag, and T. Moriarty, “A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data,” IEEE Trans. Med. Imaging, 21 (3) (2002) 193–199.
[71] K.H. Chuang, M.J. Chiu, C.C. Lin, and J.H. Chen, “Model-free functional MRI analysis using kohonen clustering neural network and fuzzy c-means,” IEEE Trans. Med. Imaging, 18 (12) (1999) 1117–1128.
[72] B. Verma and J. Zakos, “A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques,” IEEE Trans. Information Technology Biomedicine, 5 (1) (2001) 46–54.
[73] S. Shen, W. Sandham, M. ranat, and A. Sterr, “MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization,” IEEE Trans. on Information Technology in Biomedicine 9 (3) (2005) 459–467.
[74] J.S. Lin, K.S. Cheng and C.W. Mao, “A fuzzy hopfield neural network for medical image segmentation,” IEEE Trans. Nuclear Science, 43 (4) (1996) 2389–2398.
[75] M.S. Yang, and C.F. Su, “On parameter estimation for normal mixtures based on fuzzy clustering algorithms,” Fuzzy sets and Systems, 68 (1) (1994) 13–28.
[76] D.L. Collins, A.P., Zijdenbos, V. Kollokian, J.G. Sled, N.J. Kabani, C.J. Holmes and A.C. Evans, “Design and construction of a realistic digital brain phantom,” IEEE Trans. Medical Imaging, 27(3) (1998) 463–468, http://www.bic.mni.mcgill.ca/brainweb/.
[77] R.K.-S. Kwan, A.C. Evans, G.B. Pike, “MRI simulation-based evaluation of image-processing and classification methods,” IEEE Trans. Medical Imaging. 18 (11) (1999) 1085–1097.
[78] D.L Collins, A.P. Zijdenbos, V. Kollokian , J.G. Sled, N.J. Kabani, C.J. Holmes, and A.C. Evans, “Design and construction of a realistic digital brain phantom,” IEEE Trans. Medical Imaging, 17 (3) (1998) 463–468.
[79] V. Grau, A.U.J. Mewes, M. Alcañiz, R. Kikinis, and S.K. Warfield, “Improved watershed transform for medical image segmentation using prior information,” IEEE Trans. Medical Imaging, 23(4) (2004) 447–58.
[80] KVan Leemput, F. Maes, D. Vandermeulen and P. Suetens, “Automated model-based tissue classification of MR images of the brain,” IEEE Trans. Medical Imaging, 18 (10) (1999) 897-908.
[81] X. Zeng, L.H. Staib, R.T. Schultz and J.S. Duncan, “Segmentation and measurement of the cortex from 3-D MR images using coupled-surfaces propagation,” IEEE Trans. Medical Imaging, 18 (10) (1999) 927–937.
[82] J. Mykkänena, J. Tohkab, J. Luomac, and U. Ruotsalainenb, “Automatic extraction of brain surface and mid-sagittal plane from PET images applying deformable models,” Computer Methods and Programs in Biomedicine, 79 (2005) 1–17.
[83] S. Smith, “Fast robust automated brain extraction,” Human Brain Mapping, 17 (3) (2002) 143–155.
[84] F. Ségonne and A.M. Dale, et al, “A hybrid approach to the skull stripping problem in MRI,” NeuroImage, 22 (2004) 1060–1075.
[85] AH. Zhuang, DJ. Valentino and AW. Toga, “Skull-stripping magnetic resonance brain images using a model-based level set,” NeuroImage, 32 (2006) 79–92.

校內：2010-07-29公開
校外：2010-07-29公開

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