Abstract
Image segmentation is a fundamental problem in Image Processing, Computer Vision and Medical Imaging with numerous applications. In this paper, we address the atlas-based image segmentation problem which involves registration of the atlas to the subject or target image in order to achieve the segmentation of the target image. Thus, the target image is segmented with the assistance of a registration process. We present a novel variational formulation of this registration assisted image segmentation problem which leads to solving a coupled set of nonlinear PDEs that are solved using efficient numerical schemes. Our work is a departure from earlier methods in that we have a unified variational principle wherein registration and segmentation are simultaneously achieved. We present several 2D examples on synthetic and real data sets along with quantitative accuracy estimates of the registration.
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References
L. Zollei A. Yezzi and T. Kapur. A variational framework for joint segmentation and registration. In IEEE Workshop on Biomedical Image Analysis, pages 44–51, Kauai, Hawaii, 2001.
Y. Chen B. Vemuri, J. Ye and C. Leonard. A level-set based approach to image registration. In IEEE Workshop on Biomedical Image Analysis, pages 86–93, Hilton Head Island, SC, 2000.
M. Chen, T. Kanade, H. A. Rowley, and D. Pomerleau. Quantitative study of brain anatomy. In IEEE Workshop on Biomedical Image Analysis, pages 84–92, Santa Barbara, CA, 1998.
Sarang C. Joshi, Ayananshu Banerjee, Gary E. Christensen, John G. Csernansky, John W. Haller, Michael I. Miller, and Lei Wang. Gaussian random fields on sub-manifolds for characterizing brain surfaces. In 15th International Conference, IPMI’97, Poultney, Vermont, June 1997.
A. Collignon, F. Maes, D. Vandermeulen, P. Suetens, and G. Marchal. Automated multimodality image registration using information theory. In Info. Processing in Medical Images, pages 263–274, Brest, France, 1995.
B. M. Dawant, S. L. Hartmann, J. P. Thirion, F. Maes, D. Vandermeulen, and P. Demaerel. Automatic 3-d segmentation of internal structures of the head in mr images using a combination of similarity and free-form transformations: Part i, methodology and validation on normal subjects. IEEE Trans. on Medical Imaging, 18(10):909–916, 1999.
D. Rueckert, A. F. Frangi, and J. A. Schnabel. Automatic construction of 3d statistical deformation models using non-rigid registration. In Fourth International Conference, MICCAI 2001, Utrecht, The Netherlands, October 2001.
G. E. Christensen, M. I. Miller, and M. Vannier. Individualizing neuroanatomical atlases using a massively parallel computer. IEEE Computer, 1(29):32–38, 1996.
C. Meyer et. al. Demonstration of accuracy and clinical versatility of mutual information for automatic multimodality image fusion using affine and thin-plate spline-warped geometric deformations. Medical Image Analysis, 1:195–206, 1997.
Guido Gerig, Martin Styner, Martha E. Shenton, and Jeffrey A. Lieberman. Shape versus size: Improved understanding of the morphology of brain structures. In Fourth International Conference, MICCAI 2001, Utrecht, The Netherlands, October 2001.
Nikos Paragios and Rachid Deriche. Corrections to’ geodesic active contours and level sets for the detection and tracking of moving objects’. IEEE Trans. on Pattern Analysis and Machine Intelligence, 22(4):415, Apr. 2000.
W. Murray P.E. Gill and M.H. Wright. Practical Optimization. Academic Press, London, New York, 1981.
D. Rueckert, L. I. Sonda, D.L. G. Hill C. Hayes, M. O. Leach, and D. J. Hawkes. Nonrigid registration using free-form deformations: Application to breast mr images. IEEE Trans. on Medical Imaging, 18(8):712–721, 1999.
Yousef Sadd. Iterative Methods for Sparse Linear Systems. PWS Publishing Company, Boston, MA, 1995.
T. Schormann, S. Henn, and K. Zilles. A new approach to fast elastic alignment with application to human brains. In Visualization in Biomedical Computing, pages 338–342, Hamburg, 1996.
J.R. Shewchuk. An introduction to the conjugate gradient method without the agonising pain. Technical Report CMU-CS-94-125, Carnegie Mellon University, School of Computer Science, Mar. 1994.
R. Szeliski and J. Coughlan. Hierarchical spline-based image registration. In IEEE Conf. Comput. Vision Patt. Recog, pages 194–201, Seattle, 1994.
P. Thevenaz and M. Unser. Optimization of mutual information for multiresolution image registration. IEEE Trans. on Image Processing, 9(12):2083–2099, Dec. 2000.
J. P. Thirion. Image matching as a diffusion process: an analogy with maxwell’s demons. Medical Image Analysis, 2(3):243–260, 1998.
A. Trouve. Diffeomorphisms groups and pattern matching in image analysis. International Journal of Computer Vision, 28(3):213–221, 1998.
Andy Tsai, Jr. A. Yezzi, and Alan S. Willsky. Curve evolution implementation of the mumford-shah functional for image segmentation, denoising, interpolation, and magnification. IEEE Trans. on Image Processing, 10(8):1169–1186, Aug. 2001.
P. Viola and W. M. Wells III. Alignment by maximization of mutual information. In Intl. Conf. on Compu. Vision, pages 16–23, Cambridge, MA, 1995.
Y. Wang and L. H. Staib. Elastic model based non-rigid registration incorporating statistical shape information. In Medical Image Computing and Computer-Assisted Intervention, pages 1162–1173, Boston, MA, Oct. 1998.
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© 2002 Springer-Verlag Berlin Heidelberg
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Vemuri, B.C., Chen, Y., Wang, Z. (2002). Registration Assisted Image Smoothing and Segmentation. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds) Computer Vision — ECCV 2002. ECCV 2002. Lecture Notes in Computer Science, vol 2353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47979-1_37
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DOI: https://doi.org/10.1007/3-540-47979-1_37
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