局部区域拟合的快速水平集图像分割方法  

Fast level-set method based on local region information for image segmentation

作　　者：黄国鹏 姬红兵[1] 张文博[1] Huang Guopeng;Ji Hongbing;Zhang Wenbo(School of Electronic Engineering,Xidian University,Xi'an 710071,China)

机构地区：[1]西安电子科技大学电子工程学院

出　　处：《中国图象图形学报》2019年第12期2210-2221,共12页Journal of Image and Graphics

基　　金：国家自然科学基金项目(61501357);陕西省基础科学研究项目(2016JQ6080)~~

摘　　要：目的由于灰度不均匀图像在不同目标区域的灰度分布存在严重的重叠,对其进行分割仍然是一个难题;同时,图像中的噪声严重降低了图像分割的准确性。因此,传统水平集方法无法鲁棒、精确、快速地对具有灰度不均匀性和噪声的图像进行分割。针对这一问题,提出一种基于局部区域信息的快速水平集图像分割方法。方法灰度不均匀图像通常被描述为一个分段常数图像乘以一个缓慢变化的偏移场。首先,通过一个经过微调的多尺度均值滤波器来估计图像的偏移场,并对图像进行预处理以减轻图像的不均匀性;然后,利用基于偏移场校正的方法和基于局部区域信息拟合的方法分别构建能量项,并利用演化曲线轮廓内外图像灰度分布的重叠程度,构建权重函数自适应调整两个能量项之间的权重;最后,引入全方差规则项对水平集进行约束,增强了数值计算的稳定性和对噪声的鲁棒性,并通过加性算子分裂策略实现水平集快速演化。结果在具有不同灰度不均匀性和噪声图像上的分割结果表明,所提方法不但对初始轮廓的位置、灰度不均匀性和各种噪声具有较强的鲁棒性,而且具有高达94.5%的分割精度和较高的分割效率,与传统水平集方法相比分割精度至少提高了20.6%,分割效率是LIC(local intensity clustering)模型的9倍;结论本文提出一种基于局部区域信息的快速水平集图像分割方法。实验结果表明,与传统水平集方法相比具有较高的分割精度和分割效率,可以很好地应用于具有灰度不均匀和噪声的医学、红外和自然图像等的分割。Objective Image segmentation is important in computer vision and image processing. The level-set method has been widely used for image segmentation because it can handle complex topological changes. Intensity inhomogeneity, which is usually caused by a defect in the imaging device or illumination variation, is a common phenomenon in real-world images. Images with intensity inhomogeneity are difficult to segment due to the overlap of the intensity distributions between different object regions. Meanwhile, noise severely reduces the segmentation accuracy. Therefore, the traditional level-set method cannot robustly, accurately, and quickly segment images with intensity inhomogeneity and noise. To address this problem, a fast level-set method based on local region information is proposed for segmenting images in the presence of intensity inhomogeneity and noise. Method An intensity inhomogeneous image is usually described as a piecewise constant image multiplied by a slowly varying bias field. The bias field can be estimated by a multi-scale mean filter because it varies slowly over the entire image domain. However, the traditional multi-scale mean filter utilizes a fixed number of scales to estimate the bias field;hence, it may not correctly estimate the bias field for a small-sized image with severe intensity inhomogeneity. Therefore, a fine-tuned multi-scale mean filter is utilized to roughly estimate the bias field and preprocess the image to mitigate image intensity inhomogeneity. Then, the processed image is used to construct a bias correction-based pressure function, with which the image with weak intensity inhomogeneity can be quickly segmented and the bias field can be estimated simultaneously. The original image is also utilized to design a local region-based pressure function that can provide accurate segmentation for the region near the object boundaries. In addition, image entropy is integrated into the local region-based pressure function to extract additional local intensity information from the bounda

关 键 词：图像分割 快速水平集方法 灰度不均匀 多尺度均值滤波 局部区域信息 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]