Encephalic NMR image analysis by textural interpretation
Proceedings of the 2008 ACM symposium on Applied computing
A hierarchical evolutionary algorithm for automatic medical image segmentation
Expert Systems with Applications: An International Journal
Effect of Number of Coupled Structures on the Segmentation of Brain Structures
Journal of Signal Processing Systems
Centroid Neural Network with Spatial Constraints
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Encephalic NMR Tumor Diversification by Textural Interpretation
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
A survey of graph theoretical approaches to image segmentation
Pattern Recognition
Pattern Recognition and Image Analysis
Extraction of left ventricle borders with local and global priors from echocardiograms
Machine Vision and Applications
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This paper presents a new 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