Abstract
The aim of the present work is to provide a probabilistic framework for the segmentation/labelling of images, exploiting a priori knowledge concerning the object under consideration. An over-segmented image produced by the classical watershed algorithm is gradually refined by a relaxation labelling process using the original minima of the segments as markers. An important aspect in this relaxation scheme is the particular choice of the compatibility coefficients, which is based on the dynamics of the ascending path between the minima and the watershed line. The proposed method is applied to Cranial-MR images using a statistical digital atlas for the recognition of the ventricular system.
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© 1996 Kluwer Academic Publishers
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Pratikakis, I.E., Sahli, H., Cornelis, J. (1996). Watershed Analysis and Relaxation Labelling: A cooperative approach for the interpretation of Cranial-MR images using a Statistical Digital Atlas . In: Maragos, P., Schafer, R.W., Butt, M.A. (eds) Mathematical Morphology and its Applications to Image and Signal Processing. Computational Imaging and Vision, vol 5. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0469-2_50
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DOI: https://doi.org/10.1007/978-1-4613-0469-2_50
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