Abstract
This paper presents a deformable model for automatically segmenting objects from volumetric MR images and obtaining point correspondences, using geometric and statistical information in a hierarchical scheme. Geometric information is embedded into the model via an affine-invariant attribute vector, which characterizes the geometric structure around each model point from a local to a global level. Accordingly, the model deforms seeking boundary points with similar attribute vectors. This is in contrast to most deformable surface models, which adapt to nearby edges without considering the geometric structure. The proposed model is adaptive in that it initially focuses on the most reliable structures of interest, and subsequently switches focus to other structures as those become closer to their respective targets and therefore more reliable. The proposed techniques have been used to segment boundaries of the ventricles, the caudate nucleus, and the lenticular nucleus from volumetric MR images.
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References
Mclnerney, T., Terzopoulos, D.: Deformable models in medical image analysis: a survey. Medical Image Analysis 1(2), 91–108 (1996)
Staib, L.H., Duncan, J.S.: Boundary finding with parametrically deformable models. IEEE Trans. on PAMI 14(11), 1061–1075 (1992)
Cootes, T.F., Cooper, D., Taylor, C.J., Graham, J.: Active shape models-their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)
Kelemen, A., Szekely, G., Gerig, G.: Elastic model-based segmentation of 3-D neuroradiological data sets. IEEE Trans. on Medical Imaging 18(10), 828–839 (1999)
Wang, Y., Staib, L.H.: Elastic model based non-rigid registration incorporating statistical shape information. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 1162–1173. Springer, Heidelberg (1998)
Lester, H., Arridge, S.R.: A survey of hierarchical non-linear medical image registration. Pattern Recognition 1(32), 129–149 (1999)
Ip, H.H.S., Shen, D.: An affine-invariant active contour model (Al-snake) for model-based segmentation. Image and Vision Computing 16(2), 135–146 (1998)
Shen, D., Davatzikos, C.: A adaptive-focus deformable model using statistical and geometric information. To appear in IEEE Trans. on PAMI (2000)
Davatzikos, C.: Spatial transformation and registration of brain images using elastically deformable models. Comp. Vis. and Image Understanding 66(2), 207–222 (1997)
Pizer, S.M., Fritsch, D.S., Yushkevich, P.A., Johnson, V.E., Chaney, E.L.: Segmentation, registration, and measurement of shape variation via image object shape. IEEE Trans. on Medical Imaging 18(10), 851–865 (1999)
Chen, M., Kanade, T., Pomerleau, D., Schneider, J.: 3-D deformable registration of medical images using a statistical atlas. In: Taylor, C., Colchester, A. (eds.) MICCAI 1999. LNCS, vol. 1679, pp. 621–630. Springer, Heidelberg (1999)
Duta, N., Sonka, M.: Segmentation and interpretation of MR brain images: An improved active shape model. IEEE Trans. on Medical Imaging 17(6), 1049–1062 (1998)
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Shen, D., Davatzikos, C. (2000). Adaptive-Focus Statistical Shape Model for Segmentation of 3D MR Structures. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2000. MICCAI 2000. Lecture Notes in Computer Science, vol 1935. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-40899-4_21
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DOI: https://doi.org/10.1007/978-3-540-40899-4_21
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