Heart Motion Abnormality Detection via an Information Measure and Bayesian Filtering

Authors:
Kumaradevan Punithakumar;Shuo Li;Ismail Ayed;Ian Ross;Ali Islam;Jaron Chong
Affiliations:
GE Healthcare, London, Canada;GE Healthcare, London, Canada;GE Healthcare, London, Canada;London Health Science Center, London, Canada;St. Joseph's Health Care, London, Canada;University of Western Ontario, London, Canada
Venue:
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Year:
2009

Citing 7
Cited 3

Elements of information theory

Elements of information theory
Estimation with Applications to Tracking and Navigation

Estimation with Applications to Tracking and Navigation
Kernel-Based Object Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
Identifying Regional Cardiac Abnormalities from Myocardial Strains Using Spatio-temporal Tensor Analysis

MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Left Ventricle Tracking Using Overlap Priors

MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Fading channels: information-theoretic and communications aspects

IEEE Transactions on Information Theory
A nonparametric statistical method for image segmentation using information theory and curve evolution

IEEE Transactions on Image Processing

A parameterization of deformation fields for diffeomorphic image registration and its application to myocardial delineation

MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Regional heart motion abnormality detection via information measures and unscented kalman filtering

MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Assessment of regional myocardial function via statistical features in MR images

MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III

Quantified Score

Hi-index	0.01

Visualization

Abstract

This study investigates heart wall motion abnormality detection with an information theoretic measure of heart motion based on the Shannon's differential entropy (SDE) and recursive Bayesian filtering. Heart wall motion is generally analyzed using functional images which are subject to noise and segmentation inaccuracies, and incorporation of prior knowledge is crucial in improving the accuracy. The Kalman filter, a well known recursive Bayesian filter, is used in this study to estimate the left ventricular (LV) cavity points given incomplete and noisy data, and given a dynamic model. However, due to similarities between the statistical information of normal and abnormal heart motions, detecting and classifying abnormality is a challenging problem which we proposed to 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 method analyzes wall motion quantitatively by constructing distributions of the normalized radial distance estimates of the LV cavity. Using 269×20 segmented LV cavities of short-axis magnetic resonance images obtained from 30 subjects, the experimental analysis demonstrates that the proposed SDE criterion can lead to significant improvement over other features that are prevalent in the literature related to the LV cavity, namely, mean radial displacement and mean radial velocity.