Abstract
This paper proposes a probabilistic prior-based active contour model for segmenting human brain MR images. Our model is formulated with the maximum a posterior (MAP) principle and implemented under the level set framework. Probabilistic atlas for the structure of interest, e.g., cortical gray matter or caudate nucleus, can be seamlessly integrate into the level set evolution procedure to provide crucial guidance in accurately capturing the target. Unlike other region-based active contour models, our solution uses locally varying Gaussians to account for intensity inhomogeneity and local variations existing in many MR images are better handled. Experiments conducted on whole brain as well as caudate segmentation demonstrate the improvement made by our model.
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References
Leemput, K.V., et al.: Automated model-based tissue classification of MR images of the brain. IEEE Trans. on Medical Imaging 18, 897–908 (1999)
Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. on Image Processing 10(2), 266–277 (2001)
Chan, T.F., Vese, L.A.: A level set algorithm for minimizing the Mumford-Shah functional in image processing. In: 1st IEEE Workshop on Variational and Level Set Methods in Computer Vision, pp. 161–168 (2001)
Cocosco, C.A., et al.: BrainWeb: Online interface to a 3D MRI simulated brain database. Neuroimage 5(4) part 2/4, S245 (1997)
Cremers, D., Rousson, M., Deriche, R.: A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. In: IJCV
Yang, J., Tagare, H., Staib, L.H., Duncan, J.S.: Segmentation of 3D Deformable Objects with Level Set Based Prior Models. In: ISBI, pp. 85–88 (2004)
Evans, A.C., Collins, D.L., Milner, B.: An MRI-based stereotactic atlas from 250 young normal subjects. Society of Neuroscience Abstrasts 18, 408 (1992)
Gao, S., Bui, T.D.: Image Segmentation and Selective Smoothing by Using Mumford-Shah Model. IEEE Transactions on Image Processing 14(10), 1537–1549 (2005)
Li, C., Liu, J., Fox, M.D.: Segmentation of Edge Preserving Gradient Vector Flow: An Approach Toward Automatically Initializing and Splitting of Snakes. In: CVPR, vol. 1, pp. 162–167 (2008)
Liu, J., Chelberg, D., Smith, C., Chebrolu, H.: Distribution-based Level Set Model for Medical Image Segmentation. In: BMVC 2007. British Machine Vision Conference, Warwick, 10-13 September 2007, UK (2007)
Paragios, N., Deriche, R.: Coupled Geodesic Active Regions for Image Segmentation: A Level Set Approach. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 224–240. Springer, Heidelberg (2000)
Rousson, M., Deriche, R.: A Variational Framework for Active and Adaptative Segmentation of Vector Valued Images, INRIA Technical Report (2002)
Mechelli, A., Price, C.J., Friston, K.J., Ashburner, J.: Voxel-Based Morphometry of the Human Brain: Methods and Applications. Current Medical Imaging Reviews, 105–113 (2005)
Tsai, A., Yezzi, A., Wells, W., Tempany, C.: Approach to Curve: Evolution for Segmentation of Medical Imagery. IEEE TMI 22(2), 137–154 (2003)
Xu, C., Prince, J.L.: Snakes, Shapes, and Gradient Vector Flow. IEEE Transactions on Image Processing 7(3), 359–369 (1998)
Zhou, J., Rajapakse, J.C.: Segmentation of subcortical brain structures using fuzzy templates. NeuroImage 28, 915–924 (2005)
Zhu, S., Yuille, A.: Region competition: Unifying snakes, region growing, and bayes/MDL for multiband image segmentation. PAMI 18(9), 884–900 (1996)
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Liu, J., Smith, C., Chebrolu, H. (2007). A Local Probabilistic Prior-Based Active Contour Model for Brain MR Image Segmentation. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76386-4_91
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DOI: https://doi.org/10.1007/978-3-540-76386-4_91
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