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B. Li and J. M. Reinhardt. Automatic generation of 3-D shape models and their application to tomographic image segmentation. In M. Sonka and K. M. Hansen, eds., Proc. SPIE Conf. Medical Imaging, vol. 4322, pp. 311-322, San Diego, CA, 2001.

Abstract: We describe a novel method to build 3-D statistical shape models for anatomic objects in tomographic images, and demonstrate the use of the model to guide image segmentation. Our method consists of two main steps. In the first step, a statistical shape model is built for a collection of training images. Boundary similarities between adjacent transverse slices are matched to guide inter-slice interpolation. Slice-by-slice correspondences are established between images in the training sets by matching mean boundary curvatures. A statistical shape model is then obtained by principal components analysis. During the second step, the model is used to guide image segmentation. Segmentation is initialized by placing the mean shape into the image under analysis. The model deforms iteratively by updating its shape and pose parameters using the principles of the active shape model. Following the active shape model, an active contour model (snake) is used to refine the object boundary. The proposed methods have been tested using ten volumetric chest HRCT images. The results show that the new method is able to automatically generate 3-D object shape models without the need for manual landmark identification. The combination of the active shape model with the active contour model yields a fast, accurate object segmentation.

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Keywords: lung segmentation shape

Other publications by: B. Li, J. M. Reinhardt

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