CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 1997 vol.19 Issue No.09 - September
Issue No.09 - September (1997 vol.19)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.615445
<p><b>Abstract</b>—We propose two methods for supervised image segmentation: supervised relaxation labeling and watershed-driven relaxation labeling. The methods are particularly well suited to problems in 3D medical image analysis, where the images are large, the regions are topologically complex, and the tolerance of errors is low. Each method uses predefined cues for supervision. The cues can be defined interactively or automatically, depending on the application. The cues provide statistical region information and region topological constraints. Supervised relaxation labeling exhibits strong noise resilience. Watershed-driven relaxation labeling combines the strengths of watershed analysis and supervised relaxation labeling to give a computationally efficient noise-resistant method. Extensive results for 2D and 3D images illustrate the effectiveness of the methods.</p>
Image segmentation, watershed analysis, relaxation labeling, 3D image analysis, 3D medical imaging, 3D cardiac image analysis.
Michael W. Hansen, William E. Higgins, "Relaxation Methods for Supervised Image Segmentation", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.19, no. 9, pp. 949-962, September 1997, doi:10.1109/34.615445