Multivariate mathematical morphology and Bayesian classifier application to colour and medical images

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 scal...

• Type: Conference
• Year: 2008
• Where: IPAS
• Authors: Arnaud Garcia, Corinne Vachier, Jean-Paul Vallée
• Keywords: Bayesian Classifier, Image Processing, Images, IPAS 2008, Watershed Transform