Image-based fuzzy c-means clustering and connected component labeling subsecond fast fully automatic complete cardiac cycle left ventricle segmentation in multi frame cardiac MRI images

Abstract

A rapid method for left ventricle extraction from MRI images of cardiac patients is presented in this research. This facilitates cardiologists to critically assess the cardiac function or dysfunction in a patient in terms of their left ventricle's performance, measured as its ejection fraction. Fuzzy c-means based pixel clustering is used for automatic segmentation. The left ventricle in all frames in the complete cardiac heartbeat cycle are extracted after being automatically loaded and segmented. In each image, pixels are grouped into two clusters - foreground and background. After the clustering, connected component analysis labels the pixels into connected regions. The left ventricle region is heuristically selected based on the distance from the image center and eccentricity. This novel original pixel clustering with labeling approach avoids manual initialization or user intervention. This method fully automatically extracts the left ventricle with more accuracy than manual tracing on all slices in the MRI images of the complete cardiac heartbeat cycle. The average computational processing speed per frame is 0.6 seconds, making it much more efficient than level sets, active contours, or other deformable methods, which need many iterations for the evolution of the snake or contour. Accuracy of the automated method presented herein was validated against manual tracing-based extraction. After performing the comparison on four MRI frames, it was found that an average correlation coefficient of 0.95 between the automatic and manual left ventricle segmented boundaries was higher than an average correlation coefficient of 0.85 between two manual tracing-based segmentations of the same.