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Cardiac Image Segmentation Using Memory Persistence Methodology

This paper presents a novel computer-aided framework for cardiac image segmentation using a methodology based on memory persistence. The primary concept is to mimic the process of human cognition in the segmentation of time-varying images (i.e., 2D + time or 3D + time), by remembering and exploiting results of previously segmented frames, to aid in segmentation of the region of interest with poor or ambiguous boundaries. The framework involves an intelligent image segmentation process which incorporates an automatic contour initialization mechanism, and a segmentation refinement mechanism that iteratively improves the segmentation results. The proposed framework is general and can integrate most existing image segmentation algorithms in the literature. The experimental results show the benefits of the proposed framework achieving insensitivity to contour initialization, high automation and better segmentation accuracy as compared to the original algorithm and its standard temporal constraint version.