A Study of a Ventricular Motion in Cardiac MRI using Deformable Model

We experimented with a novel deformable model that track the right ventricle’s (RV) wall motion through complete cardiac cycle by using a snake-like approach. The model uses a complex Fourier shape descriptor parameterization for efficient calculation of forces that constrains contour deformation. Even though the complexity exists in RV boundary shape, the model tracks the contour correctly and shows the robustness in weak contrast and noisy edge map. We also present a quantitative evaluation of delineation accuracy by comparing manual segmented contours and semi-automatically segmented contour, to check the reliability of our deformable model. The extracted shapes shows that the error between two contours to be an average of two pixels from 256 pixels by 256 pixels of cardiac magnetic resonance images. We used the spatio-temporal characterization of ventricular wall motion, obtained by our model, to help classifying the Intra-ventricular dyssynchrony (IVD) in the LV - i.e. asynchronous activation of LV wall - by adding RV information of ventricular movement to existing data. The classifying method was to use a popular statistical pattern recognition method of the Principal Component Analysis and the Fisher’s Linear Discriminant Analysis. From a database contains 33 patients, our classifier produced correct classification performance of 87.9 % with the RV data, which shows the promising improved IVD classification as contrast to current criteria for selecting therapy, which provided the correct classification of just 84.8 % on the same database with only the LV data

WESD--Weighted Spectral Distance for measuring shape dissimilarity.

This paper presents a new distance for measuring shape dissimilarity between objects. Recent publications introduced the use of eigenvalues of the Laplace operator as compact shape descriptors. Here, we revisit the eigenvalues to define a proper distance, called Weighted Spectral Distance (WESD), for quantifying shape dissimilarity. The definition of WESD is derived through analyzing the heat trace. This analysis provides the proposed distance with an intuitive meaning and mathematically links it to the intrinsic geometry of objects. We analyze the resulting distance definition, present and prove its important theoretical properties. Some of these properties include: 1) WESD is defined over the entire sequence of eigenvalues yet it is guaranteed to converge, 2) it is a pseudometric, 3) it is accurately approximated with a finite number of eigenvalues, and 4) it can be mapped to the ([0,1)) interval. Last, experiments conducted on synthetic and real objects are presented. These experiments highlight the practical benefits of WESD for applications in vision and medical image analysis. © 1979-2012 IEEE