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19 March 2008 Multivariate mathematical morphology and Bayesian classifier application to colour and medical images
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Proceedings Volume 6812, Image Processing: Algorithms and Systems VI; 681203 (2008)
Event: Electronic Imaging, 2008, San Jose, California, United States
Multivariate images are now commonly produced in many applications. If their process is possible due to computers power and new programming languages, theoretical difficulties have still to be solved. Standard image analysis operators are defined for scalars rather than for vectors and their extension is not immediate. Several solutions exist but their pertinence is hardly linked to context. In the present paper we are going to get interested in segmentation of vector images also including a priori knowledge. The proposed strategy combines a decision procedure (where points are classified) and an automatic segmentation scheme (where regions are properly extracted). The classification is made using a Bayesian classifier. The segmentation is computed via a region growing method: the morphological Watershed transform. A direct computation of the Watershed transform on vector images is not possible since vector sets are not ordered. So, the Bayesian classifier is used for computing a scalar distance map where regions are enhanced or attenuated depending on their similitude to a reference shape: the current distance is the Mahalanobis distance. This combination allows to transfer the decision function from pixels to regions and to preserve the advantages of the original Watershed transform defined for scalar functions. The algorithm is applied for segmenting colour images (with a priori) and medical images, especially dermatology images where skin lesions have to be detected.
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Arnaud Garcia, Corinne Vachier, and Jean-Paul Vallée "Multivariate mathematical morphology and Bayesian classifier application to colour and medical images", Proc. SPIE 6812, Image Processing: Algorithms and Systems VI, 681203 (19 March 2008);

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