The segmentation of left ventricle in ultrasound imaging of human heart would provide an important clinical parameter for the evaluation of cardiac functions including volume stroke or ejection fraction and wall motion tracking. We propose a real-time segmentation method to reduce laborious manual efforts and conveniently provide robust and stable cardiac quantification to users. The proposed m...

White matter (WM) segmentation from T1-weighted MRI is complicated by intensity non-uniformities, noise, and WM’s high surface area to volume ratio. Accurate algorithms are often computationally intensive and time consuming, precluding interactivity and routine clinical use. To address this we developed a workand step-efficient parallel narrow-band level set algorithm and mapped this onto commo...

Conditional entropies as over-segmentation and under-segmentation metrics for multi-part image segmentation" (2011). Abstract In this paper, we define two conditional entropy measures for performance evaluation of general image segmentation. Given a segmentation label map and a ground truth label map, our measures describe their compatibility in two ways. The first one is the conditional entrop...

⎯This paper presents an improved multi-object segmentation algorithm based on probabilistic labeling. First, a critical look is focused on utilizing vector calculus operator and combinational operator to rewrite Dirichlet integral into a matrix form, and boundary condition is defined to obtain the needed harmonic function. The only unique parameter β that dominantly affects the segmentation per...

A color image segmentation method is proposed on the basis of the maximum likelihood (ML) estimation. The observed color image is considered as a mixture of multi-variate normal densities and the number of densities is assumed to be known. The EM algorithm is introduced in order to estimate and improve the parameters of the mixture of densities recursively. The initial parameters for the EM alg...

In this paper we propose an algorithm for combining multiple image segmentations to achieve a final improved segmentation. In contrast to previous works we consider the most general class of segmentation combination, i.e. each input segmentation can have an arbitrary number of regions. Our approach is based on a random walker segmentation algorithm which is able to provide high-quality segmenta...

In many signal processing applications, such as EEG analysis, the non-stationary signal is often required to be segmented into small epochs. This is accomplished by drawing the boundaries of signal at time instances where its statistical characteristics, such as amplitude and/or frequency, change. In the proposed method, the original signal is initially decomposed into signals with different fr...