  An important aspect for an improved cardiac functional analysis is the
accurate segmentation of the left ventricle (LV). A novel approach for
fully-automated segmentation of the LV endocardium and epicardium contours is
presented. This is mainly based on the natural physical characteristics of the
LV shape structure. Both sides of the LV boundaries exhibit natural elliptical
curvatures by having details on various scales, i.e. exhibiting fractal-like
characteristics. The fractional Brownian motion (fBm), which is a
non-stationary stochastic process, integrates well with the stochastic nature
of ultrasound echoes. It has the advantage of representing a wide range of
non-stationary signals and can quantify statistical local self-similarity
throughout the time-sequence ultrasound images. The locally characterized
boundaries of the fBm segmented LV were further iteratively refined using
global information by means of second-order moments. The method is benchmarked
using synthetic 3D+time echocardiographic sequences for normal and different
ischemic cardiomyopathy, and results compared with state-of-the-art LV
segmentation. Furthermore, the framework was validated against real data from
canine cases with expert-defined segmentations and demonstrated improved
accuracy. The fBm-based segmentation algorithm is fully automatic and has the
potential to be used clinically together with 3D echocardiography for improved
cardiovascular disease diagnosis.
