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Model-based segmentation methods have the advantage of incorporating a priori shape information into the segmentation process but suffer from the drawback that the model must be initialized sufficiently close to the target. We propose a novel approach for initializing an active shape model (ASM) and apply it to 3D lung segmentation in CT scans. Our method(More)
PURPOSE The automated correct segmentation of left and right lungs is a nontrivial problem, because the tissue layer between both lungs can be quite thin. In the case of lung segmentation with left and right lung models, overlapping segmentations can occur. In this paper, the authors address this issue and propose a solution for a model-based lung(More)
In this paper we study the feasibility of an automated ini-tialization system for a robust model-based lung segmentation approach. The lung segmentation method consists of a Robust Active Shape Model (RASM) matching stage followed by an Optimal Surface Finding (OSF) step. The RASM needs to be initialized in rough proximity to the target structure for(More)
Robust lung segmentation is challenging, especially when tens of thousands of lung CT scans need to be processed, as required by large multi-center studies. The goal of this work was to develop and assess a method for the fusion of segmentation results from two different methods to generate lung segmentations that have a lower failure rate than individual(More)
Many state-of-the-art algorithms for object class recognition have recently appeared in the literature. These algorithms recognize one object at a time in that a dedicated classifier needs to be trained for each object class. However, no paper has yet reported a single classifier capable of recognizing the object class of any one of a number of classes.(More)