Elements of information theory
Elements of information theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
A Statistical Overlap Prior for Variational Image Segmentation
International Journal of Computer Vision
Localized shape variations for classifying wall motion in echocardiograms
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Fading channels: information-theoretic and communications aspects
IEEE Transactions on Information Theory
Multiframe temporal estimation of cardiac nonrigid motion
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A machine learning approach to tongue motion analysis in 2D ultrasound image sequences
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
An atlas for cardiac MRI regional wall motion and infarct scoring
STACOM'12 Proceedings of the third international conference on Statistical Atlases and Computational Models of the Heart: imaging and modelling challenges
Segmentation of the left ventricle in cardiac cine MRI using a shape-constrained snake model
Computer Vision and Image Understanding
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We present an original information theoretic measure of heart motion based on the Shannon's differential entropy (SDE), which allows heart wall motion abnormality detection. Based on functional images, which are subject to noise and segmentation inaccuracies, heart wall motion analysis is acknowledged as a difficult problem, and as such, incorporation of prior knowledge is crucial for improving accuracy. Given incomplete, noisy data and a dynamic model, the Kalman filter, a well-known recursive Bayesian filter, is devised in this study to the estimation of the left ventricular (LV) cavity points. However, due to similarity between the statistical information of normal and abnormal heart motions, detecting and classifying abnormality is a challenging problem, which we investigate with a global measure based on the SDE. We further derive two other possible information theoretic abnormality detection criteria, one is based on Rényi entropy and the other on Fisher information. The proposed methods analyze wall motion quantitatively by constructing distributions of the normalized radial distance estimates of the LV cavity. Using 269 x 20 segmented LV cavities of short-axis MRI obtained from 30 subjects, the experimental analysis demonstrates that the proposed SDE criterion can lead to a significant improvement over other features that are prevalent in the literature related to the LV cavity, namely, mean radial displacement and mean radial velocity.