Continuous label Bayesian segmentation, applications to medical brain images
Lars Aurdal, Isabelle Bloch, Henri Maı̂tre, Christine Graffigne, C. Adamsbaum · 2002
Continuous label segmentation approaches have recently attracted much interest as they provide a formalism for handling image artifacts due to the partial volume effect which is common in for instance medical images. Here, the authors propose a new approach to this type of segmentation. Their work represents an extension of the now classic Markovian Bayesian discrete label segmentation approaches and provides good results on synthetic images simulating the presence of partial volumes as well as on real patient MR images.