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J. M. Reinhardt and W. E. Higgins. Paradigm for Shape-Based Image Analysis. Optical Engineering, vol. 37, no. 2, pp. 570-581, 1998.

Abstract: Traditional image segmentation techniques typically divide an image into separate regions based on grayscale characteristics. Most real-world image-segmentation problems, however, require some subsequent shape-based processing to yield acceptable results. Unfortunately, choosing an appropriate sequence of image-processing operators (a process) for this purpose can be a time-consuming, tedious procedure that requires considerable image-processing expertise. We describe a semi-automatic paradigm for selecting shape-based operations for an image-analysis process. Desired shape information for image regions is provided by the user in the form of easily-specified cues. The cues are then automatically interpreted to select suitable image-processing operators and operator parameters; the operators can be morphological, topological, and image-manipulation functions. The paradigm, hence, permits easy prototyping of image-analysis processes for different problems. The user is not required to be an image-processing expert to apply this strategy?-he/she need only be able to specify the desired shape properties of the regions in the image. We demonstrate our approach for both 2-D and 3-D image analysis problems.

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Keywords: segmentation shape

Other publications by: J. M. Reinhardt, W. E. Higgins

Related journal papers:

J. M. Reinhardt, A. J. Wang, T. P. Weldon, and W. E. Higgins. Cue-Based Segmentation of 4D Cardiac Image Sequences. Comp. Vision and Image Understanding, vol. 77, no. 2, pp. 251-262, 2000. (details)
S. Ukil and J. M. Reinhardt. Smoothing Lung Segmentation Surfaces in 3D X-ray CT Images using Anatomic Guidance. Acad. Radiol., vol. 12, no. 12, pp. 1502-1511, 2005. (details)
J. M. Reinhardt and W. E. Higgins. Comparison between the Morphological Skeleton and Morphological Shape Decomposition. IEEE Trans. Patt. Anal. Machine Intell., vol. 18, no. 9, pp. 951-957, 1996. (details)
J. M. Reinhardt and W. E. Higgins. Efficient Morphological Shape Representation. IEEE Trans. Image Proc., vol. 5, no. 1, pp. 89-101, 1996. (details)
S. Hu, E. A. Hoffman, and J. M. Reinhardt. Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images. IEEE Trans. Medical Imaging, vol. 20, no. 6, pp. 490-498, 2001. (details)

Related conference papers:

W. E. Higgins, J. M. Reinhardt, and W. L. Sharp. Semi-automatic construction of 3D medical image-segmentation processes. In R. A. Robb, ed., Proc. SPIE Conf. Visual. in Biomed. Comp., vol. 2359, pp. 59-71, Rochester, MN, 1994. (details)
J. M. Reinhardt and W. E. Higgins. Automatic Generation of Image-Segmentation Processes. In Proc. IEEE Int. Conf. Image Processing, vol. III, pp. 791-795, 1994. (details)
J. M. Reinhardt and W. E. Higgins. Strategy for shape-based image analysis. In Proc. IEEE Int. Conf. Image Processing, vol. I, pp. 502-505, 1994. (details)
W. E. Higgins, A. J. Wang, and J. M. Reinhardt. Semi-automatic 4D analysis of cardiac image sequences. In E. A. Hoffman, ed., Proc. SPIE Conf. Medical Imaging, vol. 2709, pp. 359-372, Newport Beach, CA, 1996. (details)
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. (details)

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S. Ukil. Anatomy-guided lung lobe segmentation and fissure analysis in X-ray CT images. PhD thesis, The University of Iowa, Iowa City, IA, 2006. (details)
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