Improved graph cut model with features of superpixels and neighborhood patches for myocardium segmentation from ultrasound image

Ultrasound (US) imaging has the technical advantages for the functional evaluation of myocardium compared with other imaging modalities. However, it is a challenge of extracting the myocardial tissues from the background due to low quality of US imaging. To better extract the myocardial tissues, this study proposes a semi-supervised segmentation method of fast Superpixels and Neighborhood Patches based Continuous Min-Cut (fSP-CMC). The US image is represented by a graph, which is constructed depending on the features of superpixels and neighborhood patches. A novel similarity measure is defined to capture and enhance the features correlation using Pearson correlation coefficient and Pearson distance. Interactive labels provided by user play a subsidiary role in the semi-supervised segmentation. The continuous graph cut model is solved via a fast minimization algorithm based on augmented Lagrangian and operator splitting. Additionally, Non-Uniform Rational B-Spline (NURBS) curve fitting is used as post-processing to solve the low resolution problem caused by the graph-based method. 200 B-mode US images of left ventricle of the rats were collected in this study. The myocardial tissues were segmented using the proposed fSP-CMC method compared with the method of fast Neighborhood Patches based Continuous Min-Cut (fP-CMC). The results show that the fSP-CMC segmented the myocardial tissues with a higher agreement with the ground truth (GT) provided by medical experts. The mean absolute distance (MAD) and Hausdorff distance (HD) were significantly lower than those values of fP-CMC (p < 0.05), while the Dice was significantly higher (p < 0.05). In conclusion, the proposed fSP-CMC method accurately and effectively segments the myocardiumn in US images. This method has potentials to be a reliable segmentation method and useful for the functional evaluation of myocardium in the future study.