Fast fully automatic multiframe segmentation of left ventricle in cardiac MRI images using local adaptive k-means clustering and connected component labeling

This paper presents a sub-second fast fully automatic method for segmentation of the left ventricle (LV) from cardiac MRI images, which plays a vital role in the diagnosis of left ventricular function for the assessment of cardiac disease in a patient. In this paper the segmentation of the left ventricle using local adaptive k-means clustering and connected components is achieved fully automatically. The segmentation is carried out on multi frame MRI. Adaptive k-means is used to cluster the pixels into groups based on their intensities in order to separate the foreground (ventricle) pixels from the background pixels. Connected component labeling is used to group the pixels into regions based on their connectivity in order to segment the LV pixel region from the other regions of the MRI image. This novel combined method eliminates the problem of initialization and iteration and it segments the LV accurately on multi frame MRI with sub-second fast computational times in the range of 0.01-0.1 seconds per frame. Thus this method achieves left ventricle segmentation for one frame in sub-second duration, much less than the time required for a single iteration in deformable model methods such as level sets and active contours. The automatic segmentation's accuracy was also validated on two frames as the correlation coefficient between the automatic and manually traced LV boundaries (0.992 for frame 1 and 0.993 for frame 2) was found to be higher than the correlation coefficient between two manually traced LV boundaries (0.984 for frame 1 and 0.900 for frame 2) for the same frame.