Automated interventricular septum thickness measurement from B-mode echocardiograms
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
Localizing Region-Based Active Contours
IEEE Transactions on Image Processing
Automated septum thickness measurement-A Kalman filter approach
Computer Methods and Programs in Biomedicine
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Automated robust segmentation of intra-ventricular septum (IVS) from B-mode echocardiographic images is an enabler for early quantification of cardiac disease. Segmentation of septum from ultrasound images is very challenging due to variations in intensity/contrast in and around the septum, speckle noise and non-rigid shape variations of the septum boundary. In this work, we effectively address these challenges using an approach that merges novel computer vision ideas with physiological markers present in cardiac scans. Specifically, we contribute towards the following: 1) A novel 1-D active contour segmentation approach that utilizes non-local (NL) temporal cues, 2) Robust initialization of the active contour framework, based on NL-means de-noising, and MRF based clustering that incorporates physiological cues. We validate our claims using cardiac measurement results on ~30 cardiac scan videos (~2000 ultrasound frames in total). Our method is fully automatic and near real time ( 0.1sec/frame) implementation.