Abstract
A central task in biomedical image analysis is the segmentation and quantification of 3D image structures. A large variety of segmentation approaches have been proposed including approaches based on different types of deformable models. A main advantage of deformable models is that they allow incorporating a priori information about the considered image structures. In this contribution we give a brief overview of often used deformable models such as active contour models, statistical shape models, and analytic parametric models. Moreover, we present in more detail 3D analytic parametric intensity models, which enable accurate and robust segmentation and quantification of 3D image structures. Such parametric models have been successfully used in different biomedical applications, for example, for the localization of 3D anatomical point landmarks in 3D MR and CT images, for the quantification of vessels in 3D MRA and CTA images, as well as for the segmentation of cells and subcellular structures in 3D microscopy images.
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References
Jain, A., Zhong, Y., Dubuisson-Jolly, M.P.: Deformable Template Models: A review. Signal Processing 71, 109–129 (1998)
McInerney, T., Terzopoulos, D.: Deformable Models in Medical Image Analysis: A Survey. Medical Image Analysis 1(2), 91–108 (1996)
Montagnat, J., Delingette, H., Ayache, N.: A Review of Deformable Surfaces: Topology, Geometry and Deformation. Image and Vision Computing 19, 1023–1040 (2001)
Cremers, D., Rousson, M., Deriche, R.: A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape. Internat. J. of Computer Vision 72(2), 195–215 (2007)
Heimann, T., Meinzer, H.P.: Statistical Shape Models for 3D Medical Image Segmentation: A Review. Medical Image Analysis 13, 543–563 (2009)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active Contour Models. Internat. J. of Computer Vision 1(4), 321–331 (1988)
Osher, S., Sethian, J.: Fronts Propagating with Curvature-Dependent Speed: Algorithms based on Hamilton-Jacobi Formulations. Journal of Computational Physics 79(1), 12–49 (1988)
Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active Shape Models - Their Training and Application. Computer Vision and Image Understanding 61(1), 38–59 (1995)
Cootes, T., Edwards, G., Taylor, C.: Active Appearance Models. IEEE Trans. on Pattern Analysis and Machine Intelligence 23(6), 681–685 (2001)
Solina, F., Bajcsy, R.: Recovery of Parametric Models from Range Images: The Case for Superquadrics with Global Deformations. IEEE Trans. on Pattern Analysis and Machine Intelligence 12(2), 131–147 (1990)
Frantz, S., Rohr, K., Stiehl, H.S.: Localization of 3D anatomical point landmarks in 3d tomographic images using deformable models. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 492–501. Springer, Heidelberg (2000)
Rohr, K.: Landmark-Based Image Analysis: Using Geometric and Intensity Models. Kluwer Academic Publishers, Dordrecht (2001)
Wörz, S.: 3D Parametric Intensity Models for the Localization of 3D Anatomical Point Landmarks and 3D Segmentation of Human Vessels. Volume 299 of DISKI. Aka, Berlin, Germany, Dissertation, KOGS, Department of Informatics, Faculty of Mathematics, Informatics, and Natural Sciences. University of Hamburg, Germany (2006)
Wörz, S., Rohr, K.: Segmentation and Quantification of Human Vessels using a 3-D Cylindrical Intensity Model. IEEE Trans. on Image Processing 16(8), 1994–2004 (2007)
Wörz, S., Sander, P., Pfannmöller, M., Rieker, R., Joos, S., Mechtersheimer, G., Boukamp, P., Lichter, P., Rohr, K.: 3D Geometry-Based Quantification of Colocalizations in Multichannel 3D Microscopy Images of Human Soft Tissue Tumors. IEEE Trans. on Medical Imaging 29(8), 1474–1484 (2010)
Chen, Z., Ning, R.: Three-Dimensional Point Spread Function Measurement of Cone-Beam Computed Tomography System by Iterative Edge-Blurring Algorithm. Physics in Medicine and Biology 49(10), 1865–1880 (2004)
Wörz, S., Rohr, K.: Localization of Anatomical Point Landmarks in 3D Medical Images by Fitting 3D Parametric Intensity Models. Medical Image Analysis 10(1), 41–58 (2006)
Marquardt, D.: An Algorithm for Least-Squares Estimation of Nonlinear Parameters. J. of the Society for Industrial and Applied Mathematics 11(2), 431–441 (1963)
Noordmans, H., Smeulders, A.: High Accuracy Tracking of 2D/3D Curved Line Structures by Consecutive Cross-Section Matching. Pattern Recognition Letters 19(1), 97–111 (1998)
Thomann, D., Rines, D., Sorger, P., Danuser, G.: Automatic Fluorescent Tag Detection in 3D with Super-Resolution: Application to the Analysis of Chromosome Movement. J. of Microscopy 208, 49–64 (2002)
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Wörz, S. (2014). 3D Model-Based Segmentation of 3D Biomedical Images. In: Hliněný, P., et al. Mathematical and Engineering Methods in Computer Science. MEMICS 2014. Lecture Notes in Computer Science(), vol 8934. Springer, Cham. https://doi.org/10.1007/978-3-319-14896-0_4
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DOI: https://doi.org/10.1007/978-3-319-14896-0_4
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