Clinician-friendly methods for cardiac image segmentation in clinical practice remain a tough challenge. Larger standard deviation in segmentation accuracy may be expected for automatic methods when the input dataset is varied; also at some instances the radiologists find them hard in case any correction is desired. In this context, this study presents a semi-automatic algorithm that uses anisotropic diffusion for smoothing the image and enhancing the edges followed by a new graph-cut method, ‘AnnularCut’, for three-dimensional left ventricle (LV) segmentation from some selected slices. Unlike the conventional cellular automata, where the performance depends solely on the image features, this method simultaneously considers the minimal energy between two adjacent regions thus mitigating the convergence problem. The two main contributions in this study can be summarised as (i) a dynamic cellular automation approach to integrate the minimal energy between two distinct labels, and (ii) generation of missing contours of the subject from the selected slices using a level set method to construct the volumetric LV. Both qualitative and quantitative evaluation performed on publicly available databases reflect the potential of the proposed method.

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AnnularCut: a graph-cut design for left ventricle segmentation from magnetic resonance images

References

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