Abdominal Organ Segmentation Using Sparse Representation and Further Combining Graph Cuts an Oriented Active Appearance Models
Das S.S.1, Bhanuse V.R.2, Dombale A.B3
1Das S.S., KJ, COE, Pune, India.
2Bhanuse V.R., VIT, Pune, India.
3Dombale A.B., Trinity, COE, Pune, India.
Manuscript received on March 17, 2013. | Revised Manuscript received on April 07, 2013. | Manuscript published on April 30, 2013. | PP: 391-393 | Volume-2, Issue-4, April 2013. | Retrieval Number: D1453042413/2013©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Segmentation of abdominal 3-D organ segmentation from volumetric images forms the basis for surgical planning required for living donor transplantations and tumor resections surgeries. This paper introduces a novel idea of using sparse representations of organ shapes in a learned structured dictionary to produce an accurate preliminary segmentation, which is further evolved based on a strategic combination of the active appearance model ,live wire, and graph cuts for abdominal 3-D organ segmentation. The increased accuracy of the preliminary segmentation translates into faster convergence of the evolution step and highly accurate final segmentations.
Keywords: Segmentation, sparse representation, AAM, Graph cut.