Dynamic autonomous image segmentation based on Grow Cut

The main incentive of this paper is to provide an enhanced approach for 2D medical image segmentation based on the Unsupervised Grow Cut algorithm, a method that requires no prior training. This paper assumes that the reader is, to some extent, familiar with cellular automata and their function as they make up the core of this technique. The benchmarks were performed on 2D MRI images of the heart and chest cavity. We obtained a significant increase in the output quality as compared to classical Unsupervised Grow Cut by using standard measures, based on the existence of accurate ground truth. This increase was obtained by dynamically altering the local threshold parameter. In conclusion, our approach provides the opportunity to become a building block of a computer aided diagnostic system.

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