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Published in:
2016 | OriginalPaper | Chapter
Automatic Left Ventricle Segmentation in Cardiac MRI Images Using a Membership Clustering and Heuristic Region-Based Pixel Classification Approach
Authors : Vinayak Ray, Ayush Goyal
Published in: Advances in Signal Processing and Intelligent Recognition Systems
Publisher: Springer International Publishing
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This study demonstrates an automated fast technique for left ventricle boundary detection in cardiac MRI for assessment of cardiac disease in heart patients. In this work, fully automatic left ventricle extraction is achieved with a membership clustering and heuristic region-based pixel classification approach. This novel heuristic region-based and membership-clustering technique obviates human intervention, which is necessary in active contours and level sets deformable techniques. Automatic extraction of the left ventricle is performed on every frame of the MRI data in the end-diastole to end-systole to end-diastole cardiac cycle in an average of 0.7 seconds per slice. Manual tracing of the left ventricle wall in the multiple slice MRI images by radiologists was employed for validation. Ejection Fraction (EF), End Diastolic Volume (EDV), and End Systolic Volume (ESV) were the clinical parameters estimated using the left ventricle area and volume measured from the automatic and manual segmentation.