Abstract
Segmentation of (noisy) images containing a complex ensemble of objects is difficult to achieve on the basis of local image information only. It is advantageous to attack the problem of extraction of object boundaries by a model-based segmentation procedure. Segmentation is achieved by tuning the parameters of the geometrical model in such a way that the boundary template locates and describes the object in the image.
In this contribution we propose a new objective function based on directional gradient information derived from fuzzy derivatives of the image data. The proposed method is designed to accurately locate an object boundary even in the case of a conflicting object positioned close to the object of interest. We further introduce a new smoothness objective to ensure the physical feasibility of the contour.
The method is evaluated on artificial data. Results on real medical images show that the method is very effective in accurately locating object boundaries in very complex medical images (see figure 7, figure 8 and figure 9).
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© 1993 Springer-Verlag Berlin Heidelberg
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Worring, M., Smeulders, A.W.M., Staib, L.H., Duncan, J.S. (1993). Parameterized feasible boundaries in gradient vector fields. In: Barrett, H.H., Gmitro, A.F. (eds) Information Processing in Medical Imaging. IPMI 1993. Lecture Notes in Computer Science, vol 687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0013780
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DOI: https://doi.org/10.1007/BFb0013780
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