Image based sub-second fast fully automatic complete cardiac cycle left ventricle segmentation in multi frame cardiac MRI images using pixel clustering and labelling

Abstract

This research presents a fully automatic sub-second fast method for left ventricle (LV) segmentation from clinical cardiac MRI images based on fast continuous max flow graph cuts and connected component labeling. The motivation for LV segmentation is to measure cardiac disease in a patient based on left ventricular function. This novel classification scheme of graph cuts labeling removes the need for manual segmentation and initialization with a seed point, since it automatically accurately extracts the LV in all slices of the full cardiac cycle in multi-frame MRI. This LV segmentation method achieves a sub-second fast computational time of 0.67 seconds on average per frame. The validity of the graph cuts labeling based automatic segmentation technique was verified by comparison with manual segmentation. Medical parameters like End Systolic Volume (ESV), End Diastolic Volume (EDV) and Ejection Fraction (EF) were calculated both automatically and manually and compared for accuracy.