TRUS image segmentation with non-parametric kernel density estimation shape prior

Due to noises, speckles, etc., automatic prostate segmentation is rather challenging, and using only low-level information such as intensity gradient is insufficient and unable to tackle the problem. In this paper, we propose an automatic prostate segmentation method combining intrinsic properties of TRUS images with the high-level shape prior information. First, intrinsic properties of TRUS images, such as the intensity transition near the prostate boundary as well as the speckle induced texture features obtained by Gabor filter banks, are integrated to deform the model to the target contour. These properties make our method insensitive to high gradient regions introduced by noises and speckles. Then, the preliminary segmentation is fine-tuned by the non-parametric shape prior, which is optimally distilled by non-parametric kernel density estimation as it can approximate arbitrary distributions. The refinement is along the direction of mean shift vector, and considerably strengthens the robustness of the method. The performance of our method is validated by experimental results. Compared with the state of the art, the accuracy and robustness of the method is quite promising, and the mean absolute distance is only 1.21 ± 0.85 mm.     

Local bandwidth selection for kernel estimation of population densities with line transect sampling

Seber (1986, Biometrics 42, 267-292) suggested an approach to biological population density estimation using kernel estimates of the probability density of detection distances in line transect sampling. Chen (1996a, Applied Statistics 45, 135-150) and others have employed cross validation to choose a global bandwidth for the kernel estimator or have suggested adaptive kernel estimation (Chen, 1996b, Biometrics 52, 1283-1294). Because estimation of the density is required at only a single point, we investigate a local bandwidth selection procedure that is a modification of the method of Schucany (1995, Journal of the American Statistical Association 90, 535-540) for nonparametric regression. We report on simulation results comparing the proposed method and a local normal scale rule with cross validation and adaptive estimation. The local bandwidths and normal scale rule produce estimates with mean squares that are half the size of the others in most cases. Consistency results are also provided.     

基于核密度估算的路网格局与景观破碎化分析

道路网络的发展是导致区域景观破碎化程度加剧的重要因素,如何定量表征道路网络特征及其破碎化效应是道路生态学的一个关键科学问题。本研究以珠江三角洲核心区为案例,采用核密度估算(KDE)结合道路密度指数方法,探讨了区域路网格局及其对景观破碎化的影响。结果表明:KDE法能有效识别和提取高密度路网热点区域;道路密度指数分析显示,道路密度与景观破碎化之间存在较强的相关性;道路密度与KDE法结合能突破传统基于行政边界计算道路密度的局限,为研究路网特征及其景观破碎化程度提供了一个很好的量化工具。