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Sparse MRF Appearance Models for Fast Anatomical Structure Localisation

Image segmentation methods like active shape models, active appearance models or snakes require an initialisation that guarantees a considerable over- lap with the object to be segmented. In this paper we present an approach that localises anatomical structures in a global manner by means of Markov Ran- dom Fields (MRF). It does not need initialisation, but finds the most plausible match of the query structure in the image. It provides for precise, reliable and fast detection of the structure and can serve as initialisation for more detailed segmentation steps. Sparse MRF Appearance Models (SAMs) encode a priori information about the geometric configurations of interest points, local features at these points and local features along the edges of adjacent points. This information is used to formulate a Markov Random Field and the mapping of the modeled object (e.g. a sequence of vertebrae) to the query image interest points is performed by the MAX- S UM algorithm. The local image information is captured by novel symmetry-based in- terest points and local descriptors derived from Gradient Vector Flow. Ex- perimental results are reported for two data-sets showing the applicability to complex medical data.

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When Oct 28, 2007
from 05:42 pm to 05:42 pm
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