Inhomogeneity correction and hybrid-based segmentation in cardiac MRI
by A.V. Nageswararao; S. Srinivasan; E. Priya
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 28, No. 4, 2018

Abstract: Image segmentation is an important step in medical image analysis and segmentation of ventricles in Cardiac Magnetic Resonance (CMR) images is challenging due to an in-built artefact called intensity-inhomogeneity. The short-axis cine Magnetic Resonance Images (MRI) recorded under a steady-state free precision protocol were corrected for intensity-inhomogeneity using Bias Corrected Fuzzy C-Means (BCFCM) method, Level Set (LS) and Multiplicative Intrinsic Component Optimisation (MICO) methods. The statistical measures show that bias correction by BCFCM has better performance than MICO and LS. In addition, the original and bias corrected images are validated by Multifractal Analysis (MFA). The results show that in bias corrected images, the low frequency components are removed thereby enhancing the sharpness of the ventricular boundaries. Further, ventricular segmentation is performed using the proposed automatic hybrid Sobel edge detector with optimised level set method. The validation parameters of segmented results show that the ventricular detection in bias corrected images matches better with ground truth.

Online publication date: Tue, 06-Nov-2018

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