Bunyak F, Palaniappan K, Glinskii O, Glinskii V, Glinsky V, Huxley V
Department of Computer Science, University of Missouri-Columbia, Columbia, MO 65211 USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2008;2008:3134-7. doi: 10.1109/IEMBS.2008.4649868.
Accurate vessel segmentation is the first step in analysis of microvascular networks for reliable feature extraction and quantitative characterization. Segmentation of epifluorescent imagery of microvasculature presents a unique set of challenges and opportunities compared to traditional angiogram-based vessel imagery. This paper presents a novel system that combines methods from mathematical morphology, differential geometry, and active contours to reliably detect and segment microvasculature under varying background fluorescence conditions. The system consists of three main modules: vessel enhancement, shape-based initialization, and level-set based segmentation. Vessel enhancement deals with image noise and uneven background fluorescence using anisotropic diffusion and mathematical morphology techniques. Shape-based initialization uses features from the second-order derivatives of the enhanced vessel image and produces a coarse ridge (vessel) mask. Geodesic level-set based active contours refine the coarse ridge map and fix possible discontinuities or leakage of the level set contours that may arise from complex topology or high background fluorescence. The proposed system is tested on epifluorescence-based high resolution images of porcine dura mater microvasculature. Preliminary experiments show promising results.
准确的血管分割是分析微血管网络以进行可靠特征提取和定量表征的第一步。与传统的基于血管造影的血管图像相比，微血管荧光图像的分割呈现出一系列独特的挑战和机遇。本文提出了一种新颖的系统，该系统结合了数学形态学、微分几何和活动轮廓的方法，以在不同背景荧光条件下可靠地检测和分割微血管。该系统由三个主要模块组成：血管增强、基于形状的初始化和基于水平集的分割。血管增强使用各向异性扩散和数学形态学技术处理图像噪声和不均匀的背景荧光。基于形状的初始化利用增强血管图像的二阶导数特征生成一个粗糙的脊（血管）掩码。基于测地线水平集的活动轮廓细化粗糙的脊图，并修复可能由于复杂拓扑或高背景荧光而出现的水平集轮廓的不连续或泄漏。所提出的系统在基于落射荧光的猪硬脑膜微血管高分辨率图像上进行了测试。初步实验显示出了有前景的结果。
