Pranav Bhagirath

53 Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR images potentials p(y i | x i ) computed the inference on the healthy or scar labels (y i ) from the MRI intensity observed at pixel i. This potential was modeled from labeled training data provided within the challenge using: φ(y i | X) = log p(y i | x i ) where y i is the label and x i is the observed intensity at voxel i. A binary classifier was employed for the purpose of distinguishing between healthy and scar. The decision boundary was learned from training data using a variant of support vector machines (SVM) known as relevance vector machines (RVM) (Tipping, 2001). The final classification of the first-level CRF was performed using a graph-cut optimization framework (Boykov et al., 2001). In the second-level CRF, using infarction candidates fromthefirst level, a twodimensional histogram encoding the distribution of image brightness values in the neighborhood of a particular reference point was constructed. This is the spin image which encoded local information around infarct candidates. Besides voxel intensity, these spin image features were also used for CRF. Similar to the first-level CRF, the final inference was performed using a graph-cut optimization framework. Algorithm 4: Mevis Fraunhofer - EM-algorithm and watershed transformation (MV) Background: The method presented in this work assumes that the voxel intensity distribution in MR images can be modeled using statistical distribution models. Depending on acquisition parameters and the reconstruction algorithm, it can either be modeled using a Gaussian, Rayleigh or non-central χ-distribution (Dietrich et al., 2007). These distributions are also closely related to the Rician distribution, making it suitable for modeling healthy myocardium intensities. For diseased myocardium the Rician– Gaussian mixture was found to be appropriate, and for necrotic tissues, the non-central χ-distribution was shown to be suitable (Hennemuth et al., 2008). The watershed segmentation approach was used in this method (Hennemuth et al., 2008). Watershed is a classical image segmentation technique where the gradient image is considered as a topographic surface. Structures such as scar can be assumed to have high intensity gradients at edges and low gradients in the interior. This high- low-high intensity gradient profile creates basins in the image. Once points are located inside each basin they can be segmented by following paths of decreasing altitudes on the topography of the gradient image.