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Segmentation of Short Axis CMR Images Using Hybrid Method

[ Vol. 14 , Issue. 3 ]

Author(s):

A.V. Nageswararao*, S. Peter Babu and S. Srinivasan   Pages 461 - 467 ( 7 )

Abstract:


Background: Highly advanced and sophisticated imaging modality, Cardiac Magnetic Resonance (CMR) images are referred to examine the cardiac morphology and its function.

Methods: In this work, the main aim is to develop a hybrid segmentation method for automatic segmentation of both left, right ventricles from short axis CMR images. In the proposed hybrid segmentation method, Fast Adaptive K-Means (FAKM) clustering method is used to locate the ventricles which are further segmented by Distance Regularized Level Set Evolution (DRLSE) method.

Results: The validation parameters show that the segmentation by proposed hybrid method is better than hybrid methods like Gaussian mixture model with dynamic programming and semi-automatic method.

Discussions: Further, FAKM hybrid method is evaluated based on End Systolic Volume (ESV), End Diastolic Volume (EDV) and Ejection Fraction (EF).

Conclusion: The analytical result shows that the hybrid method of FAKM with DRLSE gives faster and better results.

Keywords:

Clustering, level set, DRLSE, EDV, Ejection Fraction (EF), CMR images.

Affiliation:

Department of Instrumentation Engineering, Madras Institute of Technology Campus, AnnaUniversity, Chennai, Barnard Institute of Radiology, Madras Medical College and Rajiv Gandhi Government General Hospital, Chennai, Department of Instrumentation Engineering, Madras Institute of Technology Campus, AnnaUniversity, Chennai

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