Noise-Estimation-Dominated Fuzzy Segmentation Strategy for Accurate Implantation of Implantable Cardioverter Defibrillators

Myocardial scar regions appear in cardiac magnetic resonance images of patients with myocardial infarction. Implantable cardioverter defibrillators (ICDs) can be used to effectively prevent arrhythmias and even death caused by myocardial infarction. Whether or not to implant an ICD and deciding the precise location of implantation are huge clinical challenges. This work proposes a noise-estimation-dominated fuzzy segmentation strategy for ICD implantation. It achieves accurate noise estimation in cardiac magnetic resonance image segmentation by weighting mixed noise distributions and adding a spatial information constraint. To be specific, a weighted
$\ell _{2}$
-norm regularization term is proposed to form a universal noise-estimation-based fuzzy
$C$
-means algorithm that can perform accurate segmentation of images subject to mixed or unknown noise. Through region growth and flood fill in order, the region and volume of myocardial scars are precisely obtained. Thus, the ICD implantation is accurately estimated. Finally, a criterion for ICD implantation estimation is reported. Experimental results on different myocardial infarction datasets show that the proposed strategy is more effective and efficient than its competitive peers.