Pranav Bhagirath

51 Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR images Implementation: Seed selection for region-growing was automatic and repeated for each slice, making it essentially a 2D technique. A minimum of two seeds were selected for each tissue class: scar and healthy. The criteria for selecting seeds for the scar tissue class were the following: I > μ k + 2 σ k where a pixel in the k th slice has intensity I and is subjected to the above test based on mean (μ) and variance (σ 2 ) of myocardium intensity. Individual regions satisfying the above criteria were analyzed for their shape and size. Elongated and thin regions near the epicardium were deleted in an automated manner by computing the eccentricity and width (proportion to myocardial mask) of the region in question, on which a thresholding was performed based on empirical values obtained from the training set. The size of negligible regions was defined in proportion to the pixel size and size of the myocardial mask. The two largest and brightest regions were selected as the seeds. This selected seeds for the scar tissue class. For the healthy tissue class, a similar standard deviation approach was utilized (i.e. I < μ k + 2 σ k ) and the two largest and darkest regions were selected as seeds. Region-growing was initiated from each seed region and these generated segmented regions for healthy or scar tissue classes. The choice of two seeds, per slice, for each tissue class is important as it generates two separate disconnected regions. However, this places a limit on the maximum number of scar or healthy regions possible (i.e. two) in each slice. The region-growing process was followed by a region-labeling step in which pixels that were not labeled as scar or healthy tissue were analyzed; if they contained any adjacent neighbor belonging to either scar and healthy classes, they were labeled as such. This was followed by a post-processing step to fill holes or small gaps in the segmentations. Also, regions that were small islands containing a negligible number of pixels were removed from the segmentation. Finally, dark regions that lacked contrast, but were surrounded by scar pixels were re-labeled as scar. This is characteristic of a microvascular obstruction. Algorithm 3: McGill - conditional random fields (MCG) Background: The previous methods described are geometrical in their nature; a region’s intensity and its geometrical shape are used to determine its classification. The method described in this section is different from the above approaches in that a probabilistic classifier model was used. Based on the training dataset, the classifier can infer the posterior distribution of a pixel’s label to be healthy or scar given the observation. There are two sets of observations made: (1) the pixel’s intensity, and (2) the pixel’s