基于灰度和非局部空间灰度特征的二维Otsu曲线阈值分割法

为了克服图像噪声对图像分割结果的影响,利用图像中与像素具有相似邻域结构的像素提取当前像素的非局部空间信息,构造了基于像素的灰度信息和非局部空间灰度信息的二维直方图,并将此二维直方图引入到Otsu曲线阈值分割法中,提出了基于灰度和非局部空间灰度特征的二维Otsu曲线阈值分割法。实验结果表明,该方法能进一步提高原始二维Otsu曲线阈值分割法对于图像噪声的鲁棒性,获得了更加理想的分割结果。     

灰度图像的二维Otsu自动阈值分割法

Otsu法是最常用的利用图象一维灰度直方图的阈值化方法之一,本文的目的是将它推广到二维直方图,二维otsu法除了考虑象素点的灰度信息外还考虑了象素点与其邻域的空间相关信息,通过与一维的0tsu法比较表明,在有噪声的图象中,本文提出的方法性能好得多.     

基于二维Otsu阈值修正的图像分割新方法

首先采用对二维直方图斜分割和查表的方法,解决了传统二维Otsu方法分割图像计算耗时,难以实时实现的缺点。其次基于灰度统计的思想,针对二维Otsu法处理小目标图像难以实现正确分割的缺点,提出了一种在实现过程中采用迭代的阈值修正新方法。最后设计了一种新型滤波器对分割后的图像进行滤波降噪处理。实验结果表明,阈值修正后的二维Otsu改进算法对小目标图像分割效果明显,而且新型滤波器对滤除散布在目标与背景中的噪声非常有效。将阈值修正法和新型滤波器结合使用,不仅快速,而且准确,取得了良好的分割效果。     

二维Otsu自适应阈值分割算法的改进

在二维OTSU自适应阈值分割算法的基础上提出了一种改进的自适应阈值分割算法，这种改进算法由于充分考虑了图像二维直方图中象素灰度值及其领域平均灰度值比较接近的区域而获得了比传统算法具有更强抗噪声能力的分割算法，通过将该算法用于显微细胞图像的分割证明了它不仅分割效果得到改善，同时还大大降低了算法的复杂性。     

狼群优化的二维Otsu快速图像分割算法

传统二维Otsu算法的阈值选取大都采用穷尽搜索方式,造成算法分割时间较长、实时性差等缺点,影响图像分割效果。为提高算法的运行效率,采用狼群算法来搜索最优阈值,每匹人工狼代表一个可行的二维阈值向量,狼群通过游走、召唤、围攻这三种智能行为的不断迭代以及狼群间的信息交互来获取最佳阈值。仿真结果表明,与标准粒子群优化二维Otsu算法和传统二维Otsu算法相比,狼群优化算法降低了分割时间并提高了图像分割精度。     

灰度图像最小误差阈值分割法的二维推广

一维最小误差阈值法假设了目标和背景的灰度分布服从混合正态分布. 考虑到噪声等因素对图像质量的影响, 本文在二维灰度直方图上, 基于二维混合正态分布假设, 给出一维最小误差阈值法的二维推广表达式. 为了提高算法的运行速度, 也给出了快速递推算法. 实验表明, 二维最小误差阈值法是一个有效的图像分割算法, 能够更好地适应目标和背景方差相差较大的图像及噪声图像的分割问题.     

基于CPSO的二维Otsu图像分割法

二维Otsu方法同时考虑了图像的灰度信息和像素问的空间邻域信息,图像分割效果好但算法计算量较大。针对上述情况,提出一种基于混沌粒子群优化算法（CPSO）的策略,将其用于二维Otsu方法中,并与标准粒子群优化算法（SPSO）进行仿真实验对比。实验结果表明,该方法可以提高分割速度,克服SPSO的缺点,图像分割结果较理想。     

一种改进的二维Otsu图像分割算法

二维Otsu图像分割算法将类间离散度矩阵的迹作为阈值识别函数,计算复杂度高且易导致分割错误,为此对二维Otsu算法进行改进,设计一种新的阈值识别函数.通过对比试验验证改进算法的有效性.     

基于自适应加权中值滤波的二维Otsu图像分割算法

为解决传统二维Otsu算法在含噪声较多的图像应用中分割效果较差这一问题,提出一种基于自适应加权中值滤波的二维Otsu图像分割算法.该算法首先利用一种新的自适应加权中值滤波对噪声图像中值滤波;然后将中值图像的二维直方图区域划分由四分法改为二分法;最后利用改进的二维Otsu算法对图像作精确分割.实验结果表明,该算法对灰度噪声图像具有更强的抗噪性且分割效果也更为理想.     

基于二维直方图重建的Otsu图像分割算法

针对二维Otsu算法因区域误分而产生的抗噪性差和计算量较大这一问题,提出了一种基于二维直方图重建的Otsu图像分割算法。该算法首先分析了原始算法中二维直方图所存在的误分和不足;然后重建二维直方图,以此来减弱噪声的干扰;最后将二维直方图区域划分由原来的四分法改为二分法,从而提高了计算速度。实验结果表明,本算法具有更强的抗噪性,分割效果也更为理想。     

Water-body segmentation from satellite images using Kapur's entropy-based thresholding method

Water body segmentation helps in extracting water bodies like lake, pond, river, and reservoir from high resolution satellite images. This also helps in discovering new water bodies. But, extraction of water bodies from satellite images is much complicated, mainly due to the severe disparity in size, shape, and appearance of the water bodies. In this article, Kapur's entropy-based thresholding method is proposed for the segmentation of water bodies from Very High Resolution (VHR) satellite images. The dataset used in this article is AIRS (Aerial Imagery for Roof Segmentation) dataset, with VHR satellite images, from which only the images with water bodies are considered. Experimental results show that the proposed method yields better segmentation performance with an overall accuracy of 98.43% and Structural Similarity Index rate of 0.9712.     

A multi-scale 3D Otsu thresholding algorithm for medical image segmentation

Thresholding technique is one of the most imperative practices to accomplish image segmentation. In this paper, a novel thresholding algorithm based on 3D Otsu and multi-scale image representation is proposed for medical image segmentation. Considering the high time complexity of 3D Otsu algorithm, an acceleration variant is invented using dimension decomposition rule. In order to reduce the effects of noises and weak edges, multi-scale image representation is brought into the segmentation algorithm. The whole segmentation algorithm is designed as an iteration procedure. In each iteration, the image is segmented by the efficient 3D Otsu, and then it is filtered by a fast local Laplacian filtering to get a smoothed image which will be input into the next iteration. Finally, the segmentation results are pooled to get a final segmentation using majority voting rules. The attractive features of the algorithm are that its segmentation results are stable, it is robust to noises and it holds for both bi-level and multi-level thresholding cases. Experiments on medical MR brain images are conducted to demonstrate the effectiveness of the proposed method. The experimental results indicate that the proposed algorithm is superior to the other multilevel thresholding algorithms consistently.     

Connectivity-based local adaptive thresholding for carotid artery segmentation using MRA images

The computer algorithms for the delineation of anatomical structures and other regions of interest on the medical imagery are important component in assisting and automating specific radiological tasks. In addition, the segmentation of region is an important first step for variety image related application and visualization tasks. In this paper, we propose a fast and automated connectivity-based local adaptive thresholding (CLAT) algorithm to segment the carotid artery in sequence medical imagery. This algorithm provides the new feature that is the circumscribed quadrangle on the segmented carotid artery for region-of-interest (ROI) determination. By using the preserved connectivity between consecutive slice images, the size of the ROI is adjusted like a moving window according to the segmentation result of previous slice image. The histogram is prepared for each ROI and then smoothed by local averaging for the threshold selection. The threshold value for carotid artery segmentation is locally selected on each slice image and is adaptively determined through the sequence image. In terms of automated features and computing time, this algorithm is more effective than region growing and deformable model approaches. This algorithm is also applicable to segment the cylinder shape structures and tree-like blood vessels such as renal artery and coronary artery in the medical imagery. Experiments have been conducted on synthesized images, phantom and clinical data sets with various Gaussian noise.     

基于边缘信息的图像阈值化分割方法

针对现有几种利用边缘信息来进行图像闽值分割的方法存在的对噪声高度敏感,梯度阈值难以选取,且不具有自适应性的特点,提出一种抗噪声影响的形态学梯度算子和一种基于梯度直方图统计特征的梯度闽值自适应选取算法,得出了一套完整的基于边缘信息的图像闽值化分割算法。     

Canny算子中Otsu阈值分割法的运用

Canny算子只要能适当地选择其参数就能提取物体清晰的轮廓.利用类间方差最大化阈值分割算法(Otsu)能够计算出对Canny算子性能具有决定意义的高门限值,然后将这门限值运用于Canny算子来检测物体边缘.从实验结果看,Otsu算法应用于Canny算子中门限选择,改善了Canny算子的边缘提取效果,取得了预计的成果.     

Otsu多阈值快速求解算法

最大类间方差(Otsu)方法计算简单,分割效果良好,广泛应用于图像的单阈值分割。为了使Otsu方法能够适应于更加复杂的图像,很多学者对其进行了多阈值的推广,但存在计算量大、效率低的问题。针对此不足,对Otsu方法也进行了多阈值的推广,首先划分直方图区间,然后采用快速二分法求取区间中的阈值以实现Otsu方法的多阈值扩展,使其在保持良好分割效果的基础上大大节省了时间。实验说明了该算法的有效性。     

基于梯度熵的Otsu图像分割算法

当目标和背景的类内方差差距较大时,传统图像分割算法Otsu会将类内方差较大的类中部分像素划分到类内方差较小的类,造成错误分割,针对这种情况,提出一种基于梯度熵的O tsu算法。利用梯度值分析求出目标和背景的分界点;针对目标和背景分别进行有选择性的线性拉伸,使目标和背景满足类内方差差距小的条件;对处理后的图像采用Otsu算法进行分割。实验结果表明,该算法能有效避免传统Otsu阈值偏向方差大的一类的情况发生,从客观和主观角度进行图像分割质量评价,效果良好。     

Human mental search-based multilevel thresholding for image segmentation

Multilevel thresholding is one of the principal methods of image segmentation. These methods enjoy image histogram for segmentation. The quality of segmentation depends on the value of the selected thresholds. Since an exhaustive search is made for finding the optimum value of the objective function, the conventional methods of multilevel thresholding are time-consuming computationally, especially when the number of thresholds increases. Use of evolutionary algorithms has attracted a lot of attention under such circumstances. Human mental search algorithm is a population-based evolutionary algorithm inspired by the manner of human mental search in online auctions. This algorithm has three interesting operators: (1) clustering for finding the promising areas, (2) mental search for exploring the surrounding of every solution using Levy distribution, and (3) moving the solutions toward the promising area. In the present study, multilevel thresholding is proposed for image segmentation using human mental search algorithm. Kapur (entropy) and Otsu (between-class variance) criteria were used for this purpose. The advantages of the proposed method are described using twelve images and in comparison with other existing approaches, including genetic algorithm, particle swarm optimization, differential evolution, firefly algorithm, bat algorithm, gravitational search algorithm, and teaching-learning-based optimization. The obtained results indicated that the proposed method is highly efficient in multilevel image thresholding in terms of objective function value, peak signal to noise, structural similarity index, feature similarity index, and the curse of dimensionality. In addition, two nonparametric statistical tests verified the efficiency of the proposed algorithm, statistically.