<title>On the integration of image segmentation and shape analysis with its application to left-ventricle motion analysis</title>

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This paper describes an integrated approach to image segmentation and shape analysis and its application to left ventricle motion and deformation analysis based on CT volumetric data. The proposed approach is different from traditional image analysis scenario in which the image segmentation and shape analysis are usually considered separately. The advantage of integrating the image segmentation with the shape analysis lies in the fact that the shape characteristics of the object can be used as effective constraints in the process of segmentation while original image data can be made useful along with the segmentation results in the process of shape analysis. In the case of left ventricle motion estimation through shape analysis based on CT volumetric data, such an integration can be applied to obtain the estimation results that are consistent with both given image data and a prior shape knowledge. The initial segmentation of the images is obtained through adaptive K-mean classification and the boundary of the given objects is computed based on such segmentation. The shape analysis is accomplished through fitting the boundary points to the surface modeling primitives. These two processes are integrated through the feedforward and feedback channels so that the surface fitting is weighted by the confidence measures of the boundary points and segmentation refinement is controlled by the result of surface modeling. Its application to left ventricle motion analysis is implemented through identifying the correspondences between the parameters of surface modeling primitives and the parameters of motion and deformation modeling. The preliminary results of the application show the promising improvement of such integrated approach over the traditional approaches.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.