3D long bone reconstruction based on level sets.
In medical imaging a three-dimensional (3D) object must often be reconstructed from serial cross-sections to aid in the comprehension of the object's structure as well as to facilitate its automatic manipulation and analysis. The most popular interpolation scheme for a sequence of image slices is the shape-based method, where object information extracted from a given 3D volume image is used in guiding the interpolation process. The paper presents a level set reformulation of the well-known shape-based method as well as a new automatic level set method, which offers better performance. In particular, we focus on X-ray examinations of long bones, which also requires us to deal with the problem of an optimal slice positioning. To this aim, a 2D version of the proposed algorithm will be used to localize a subset of slices from the entire volume image. A number of experiments were performed on computed tomographic real images to evaluate the proposed approach. The experimental results show a substantial improvement of visual effects (qualitative evaluation) using the proposed method in comparison to both the conventional gray-level interpolation scheme and the shape-based method. Compared with the shape-based interpolation scheme the proposed method has much lower computational cost.
Department of Mathematics, University of Bologna, P.zza di Porta San Donato 5, 40127 Bologna, Italy. morigi@dm.unibo.it
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D000465:Algorithms; D001842:Bone and Bones / Q000033:anatomy & histology / Q000000981:diagnostic imaging*; D006801:Humans; D007558:Italy; D008279:Magnetic Resonance Imaging; D014057:Tomography, X-Ray Computed