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Cellular automata-based left ventricle reconstruction from magnetic resonance images

Despite advanced medical imaging technologies being available, image segmentation still continues to represent a major bottleneck in practical applications due to high noise level and lack of contrast. This paper presents a semi-automatic algorithm that uses stochastic resonance as a pre-processing step for enhancing the contrast of input magnetic resonance (MR) images followed by a cellular automata (CA)-based mechanism for surface reconstruction of the left ventricle (LV) from a few selected MR slices. The main contribution, in this work, is a new formulation for preventing the CA method to leak into surrounding areas of similar intensity. Another contribution is the use of level sets for segmenting the rest of the slices of a subject automatically between the selected slices segmented by the CA to build the LV surface. Both qualitative and quantitative evaluations performed on York and MICCAI Grand Challenge workshop database of MR images show promising segmentation accuracy when compared with the ground truth, reflecting the potential of the proposed method.