A Combined Spatial Fuzzy C-Means and Level Set Approach for Endocardium Segmentation in MRI Image Series

Background: Obtaining accurate left ventricular (LV) endocardium segmentation requires the exclusion of the papillary muscles fromthecardiacwall,whichisasignificant,albeitchallenging,stepincardiacimageanalysis. Mostmedicalimagingsystemssuffer from noise in that it affects the processing procedure. We herein introduce a segmentation algorithm, which improves segmentation accuracy by excluding the papillary muscles and suppressing noise effects. Methods: We proposed a hybrid fuzzy-based level set method (LSM) to segment the cardiac wall in magnetic resonance imaging imageseries. Inthisapproach,weappliedimprovedspatialfuzzyc-means(FCM)onthegivenimageandthenutilizedtheobtained result as the initial contour for the LSM method to obtain more accurate segmentation results. Results: We compared the obtained results with those obtained via manual segmentation as the gold standard vis-à-vis accuracy, Jaccard coefficient, dice coefficient, and false positive ratio. Also, the robustness of the proposed method to the noise was tested by addingtheGaussiannoisewithdifferentvariancestotheoriginalimage. Theobtainedresultsshowed96 ± 1.3% accuracyinnormal images and 89 ± 4% accuracy in noisy images with a signal-to-noise ratio of -2.2 to -1. Conclusions: Ourresultsdemonstratedthatourproposedmethodwasabletoexcludethepapillarymusclesfromthecardiacwall. Moreover, our hybrid method showed better accuracy than the 2 methods of FCM and LSM alone.