Abstract
This paper presents a method for automatic segmentation of the tibia and femur in clinical magnetic resonance images of knees. Texture information is incorporated into an active contours framework through the use of vector-valued geodesic snakes with local variance as a second value at each pixel, in addition to intensity. This additional information enables the system to better handle noise and the non-uniform intensities found within the structures to be segmented. It currently operates independently on 2D images (slices of a volumetric image) where the initial contour must be within the structure but not necessarily near the boundary. These separate segmentations are stacked to display the performance on the entire 3D structure.
This report describes research supported in part by NSF under contract 1R1-9610249 and in part by MERL, A Mitsubishi Electric Research Laboratory.
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Lorigo, L.M., Faugeras, O., Grimson, W.E.L., Keriven, R., Kikinis, R. (1998). Segmentation of bone in clinical knee MRI using texture-based geodesic active contours. In: Wells, W.M., Colchester, A., Delp, S. (eds) Medical Image Computing and Computer-Assisted Intervention — MICCAI’98. MICCAI 1998. Lecture Notes in Computer Science, vol 1496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056309
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