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Antonio Lopez, David Lloret, Joan Serrat, & Juan J. Villanueva. (2000). Multilocal Creaseness Based on the Level-Set Extrinsic Curvarture..
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David Lloret, Antonio Lopez, & Joan Serrat. (1997). Rigid Registration of CT and MR volumes based on Rothes creases.
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Antonio Lopez, Felipe Lumbreras, & Joan Serrat. (1997). Efficient computation of local creaseness. CVC, Bellaterra (Spain).
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Antonio Lopez, David Lloret, & Joan Serrat. (1998). Creaseness measures for CT and MR image registration..
Abstract: Creases are a type of ridge/valley structures that can be characterized by local conditions. Therefore, creaseness refers to local ridgeness and valleyness. The curvature K of the level curves and the mean curvature kM of the level surfaces are good measures of creaseness for 2-d and 3-d images, respectively. However, the way they are computed gives rise to discontinuities, reducing their usefulness in many applications. We propose a new creaseness measure, based on these curvatures, that avoids the discontinuities. We demonstrate its usefulness in the registration of CT and MR brain volumes, from the same patient, by searching the maximum in the correlation of their creaseness responses (ridgeness from the CT and valleyness from the MR). Due to the high dimensionality of the space of transforms, the search is performed by a hierarchical approach combined with an optimization method at each level of the hierarchy
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Antonio Lopez, Felipe Lumbreras, & Joan Serrat. (1998). Creaseness form level set extrinsec curvature..
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David Lloret, Antonio Lopez, & Joan Serrat. (1998). 3-D image Processing and Modeling, workshop on non-linear model-based image analysis..
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David Lloret, Antonio Lopez, & Joan Serrat. (1998). Precise registration of CT and MR volumes based on a new creaseness measure.
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David Lloret, & Joan Serrat. (1999). System for calibration of a stereotatic frame..
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David Lloret, Antonio Lopez, Joan Serrat, & Juan J. Villanueva. (1999). Creaseness-based computer tomography and magnetic resonance registration: Comparison with the mutual information method..
Abstract: This paper describes a method which uses the skull as a landmark for automatic registration of computer tomography to magnetic resonance (MR) images. First, the skull is extracted from both images using a new creaseness operator. Then, the resulting creaseness images are used to build a hierarchic structure which permits a robust and fast search. We have justified experimentally the performance of several choices of our algorithm, and we have thoroughly tested its accuracy and robustness against the well-known mutual information method for five different pairs of images. We have found both comparable, and for certain MR images the proposed method achieves better performance.
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Antonio Lopez, Felipe Lumbreras, Joan Serrat, & Juan J. Villanueva. (1999). Evaluation of Methods for Ridge and Valley Detection.
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Josep Llados, Felipe Lumbreras, & X. Varona. (1999). A multidocument platform for automatic reading of identity cards..
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David Lloret, C. Mariño, Joan Serrat, Antonio Lopez, & Juan J. Villanueva. (2001). Landmark-based registration of full SLO video sequences..
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Antonio Lopez, W. Niessen, Joan Serrat, K. Nicolay, Bart M. Ter Haar Romeny, Juan J. Villanueva, et al. (1999). New improvements in the multiscale analysis of trabecular bone patterns..
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Antonio Lopez, W. Niessen, Joan Serrat, K. Nicolay, Bart M. Ter Haar Romeny, Juan J. Villanueva, et al. (2000). New improvements in the multiscale analysis of trabecular bone patterns..
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Antonio Lopez. (1997). Ridge/Valley-like structures: Creases, separatrices and drainage patterns.
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