This research demonstrates a completely automated sub-second fast technique for left ventricle (LV) segmentation from clinical cardiac MRI images for the crucial assessment of left ventricular dysfunction as a measure of cardiac diseases. In this work left ventricle segmentation is achieved using the combination of fuzzy c-means which is a pixel based classification method and connected component labeling. This strategic combination obviates user intervention and problem of seed point initialization as it automatically segments the LV accurately on all frames in the complete cardiac cycle in multi-frame MRI. The both methods complement each other such that it achieves sub-second fast computational speed of 0.7 seconds on average per frame. Thus this technique's computational time for left ventricle segmentation is much faster than iteration based methods. The accuracy of the automatic segmentation technique was tested against manual segmentation on the basis of correlation coefficient. The value of correlation coefficient between the automatic and manually traced LV boundaries was 0.932 which can be considered clinically significant.
['Anupama Bhan', 'Ayush Goyal', 'Malay Kishore Dutta', 'Kamil Riha', 'Yara Omran']
Image-based pixel clustering and connected component labeling in left ventricle segmentation of cardiac MR images