2 research outputs found
Multiscale Nakagami parametric imaging for improved liver tumor localization
Effective ultrasound tissue characterization is usually hindered by complex
tissue structures. The interlacing of speckle patterns complicates the correct
estimation of backscatter distribution parameters. Nakagami parametric imaging
based on localized shape parameter mapping can model different backscattering
conditions. However, performance of the constructed Nakagami image depends on
the sensitivity of the estimation method to the backscattered statistics and
scale of analysis. Using a fixed focal region of interest in estimating the
Nakagami parametric image would increase estimation variance. In this work,
localized Nakagami parameters are estimated adaptively by means of maximum
likelihood estimation on a multiscale basis. The varying size kernel integrates
the goodness-of-fit of the backscattering distribution parameters at multiple
scales for more stable parameter estimation. Results show improved quantitative
visualization of changes in tissue specular reflections, suggesting a potential
approach for improving tumor localization in low contrast ultrasound images.Comment: IEEE International Conference on Image Processing (ICIP), USA, pp.
3384-3388, 201
Spatio-Temporal Segmentation in 3D Echocardiographic Sequences using Fractional Brownian Motion
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.Comment: 11 pages, 10 figures, 2 tables, journal articl