Biomedical Research

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Automatic hybrid ventricular segmentation of short-axis cardiac MRI images

Ventricular segmentation is an important task to quantitatively evaluate the function of the cardiovascular system in Cardiac Magnetic Resonance (CMR) images. In this work, an automatic hybrid segmentation method is proposed in which Kirsch edge detection method is combined with modified Local Region based Chan-Vese (LRCV) method. The edge transformed image obtained from the Kirsch operator is provided to the modified LRCV method with distance regularized level set for segmenting the ventricles. Segmentation results shows that the hybrid method performs better than conventional Local Chan-Vese (LCV), Local Binary Fitting (LBF), Local Global Active Contour (LGAC), Locally Statistical Active Contour (LSAC), LRCV and semi-automatic methods both qualitatively and quantitatively by dice metric coefficient, Jaccard Index, modified Hausdroff distance, sensitivity and specificity. Further, the proposed hybrid method is evaluated based on End systolic volume, End diastolic volume and ejection fraction using Bland-Altman plot. The analytical results show that the automatic Kirsch hybrid method provides better segmentation to evaluate the diagnostic parameters.

Author(s): Nageswararao AV, Srinivasan S, Babu Peter S