Medical image segmentation using minimal path deformable models with implicit shape priors
Abbreviated Journal Title
IEEE T. Inf. Technol. Biomed.
deformable models; energy minimization; medical image segmentation; minimal path; shape prior modeling; ACTIVE CONTOUR MODELS; ALGORITHMS; Computer Science, Information Systems; Computer Science, ; Interdisciplinary Applications; Mathematical & Computational Biology; Medical Informatics
This paper presents anew method for segmentation of medical images by extracting organ contours, using minimal path deformable models incorporated with statistical shape priors. In our approach, boundaries of structures are considered as minimal paths, i.e., paths associated with the minimal energy, on weighted graphs. Starting from the theory of minimal path deformable models, an intelligent "worm" algorithm is proposed for segmentation, which is used to evaluate the paths and finally find the minimal path. Prior shape knowledge is incorporated into the segmentation process to achieve more robust segmentation. The shape priors are implicitly represented and the estimated shapes of the structures can be conveniently obtained. The worm evolves under the joint influence of the image features, its internal energy, and the shape priors. The contour of the structure is then extracted as the worm trail. The proposed segmentation framework overcomes the shortcomings of existing deformable models and has been successfully applied to segmenting various medical images.
Ieee Transactions on Information Technology in Biomedicine
"Medical image segmentation using minimal path deformable models with implicit shape priors" (2006). Faculty Bibliography 2000s. 6726.