基于灰度-梯度二维对称Tsallis交叉熵的阈值分割

针对灰度级-平均灰度级直方图的二维Tsallis交叉熵阈值分割法存在错分、计算复杂度较高问题,提出一种基于灰度-梯度二维对称Tsallis交叉熵的阈值分 割方法。构建新的灰度-梯度二维直方图,更加全面地考虑目标点和背景点;导出基于该直方图区域划分的对称Tsallis交叉熵阈值选取公式;采用基于tent映射的 混沌小生境粒子群优化算法搜寻二维最佳阈值向量,并引入快速递推算法降低其适应度函数的计算复杂度。实验结果表明,与基于灰度级-平均灰度级直方图的 二维Tsallis交叉熵阈值分割法相比,该方法能够使分割后的图像边缘更加准确,类内灰度更加均匀,且实时性提高了30倍。     

改进的2维Otsu法及混沌粒子群递推的阈值分割

鉴于现常用的灰度级-平均灰度级2维直方图区域划分将部分目标和背景点错分成边缘和噪声点这一不足,为此提出了一种基于灰度级-梯度2维直方图的Otsu阈值选取新方法,利用混沌粒子群优化算法来寻找分割阈值,并提出在迭代过程中,采用递推方法来大大减少适应度函数的重复计算。实验结果表明,与最近提出的基于灰度级-平均灰度级2维直方图Otsu法及粒子群的快速图像分割方法相比,该新方法由于尽可能地考虑了所有目标点和背景点,从而使分割后的图像区域内部均匀、边界形状准确、特征细节清晰,同时运行时间几乎不到现有算法的1/3,而且粒子群处理的收敛精度得到了进一步提高。     

融合非局部空间灰度信息的三维Otsu法

为了提高最大类间方差阈值分割法(Otsu)对于图像噪声的鲁棒性,提出融合非局部空间灰度信息的三维Otsu法。该方法利用图像像素的灰度信息、邻域中值灰度信息和非局部空间灰度信息进行直方图统计,构建新颖的三维直方图,采用最大类间方差作为阈值选取准则。实验结果表明新方法对于噪声的鲁棒性要优于原始三维Otsu法,能够获得更加令人满意的分割结果。     

A novel histogram transformation to improve the performance of thresholding methods in edge detection

The gradient image is used to detect edge points, and the gradient histogram is a typical case of a unimodal histogram. It is well-documented that bi-modal thresholding methods (such as the Otsu method) detect edges poorly. Therefore, specific unimodal thresholding methods are used to detect edge points. However, unimodal thresholding methods (such as the Rosin method) sometimes obtain very noisy results. In this paper, we propose a histogram transformation to improve the performance of some thresholding methods. Using the Berkeley Segmentation Dataset, we present quantitative performance results in an edge detection task to show that our transformation improves the performance of the Otsu and Rosin methods. Our histogram transformation can be used by any histogram thresholding method, but the performance of the method, using the transformed histogram, will depend of the criterion used by this method.     

Maximum similarity thresholding

Otsu method is one of the most popular image thresholding methods. The segmentation results of Otsu method are in general acceptable for the gray level images with bimodal histogram patterns that can be approximated with mixture Gaussian modal. However, it is difficult for Otsu method to determine the reliable thresholds for the images with mixture non-Gaussian modal, such as mixture Rayleigh modal, mixture extreme value modal, mixture Beta modal, mixture uniform modal, comb-like modal. In order to determine automatically the robust and optimum thresholds for the images with various histogram patterns, this paper proposes a new global thresholding method based on a maximum-image-similarity idea. The idea is inspired by analyzing the relationship between Otsu method and Pearson correlation coefficient (PCC), which provides a novel interpretation of Otsu method from the perspective of maximizing image similarity. It is then natural to construct a maximum similarity thresholding (MST) framework by generalizing Otsu method with the maximum-image-similarity concept. As an example, a novel MST method is directly designed according to this framework, and its robustness and effectiveness are confirmed by the experimental results on 41 synthetic images and 86 real world images with various histogram shapes. Its extension to multilevel thresholding case is also discussed briefly.     

基于图像边缘信息的2维阈值分割方法

为了改善2维阈值分割性能,提高图像分割的效率,在传统2维Otsu阈值分割算法的基础上,提出了一种基于图像边缘信息的2维阈值分割方法。这种改进的方法保留了2维Otsu阈值分割算法分割结果准确的优点,并在此基础上充分利用图像的边缘信息,通过分析图像的边缘直方图和阈值的关系来得到最优分割阈值。仿真实验结果表明,该方法与传统2维分割算法相比,不仅计算简单,而且实时性好。     

A Local Contrast Fusion Based 3D Otsu Algorithm for Multilevel Image Segmentation

To overcome the shortcomings of 1D and 2D Otsu’s thresholding techniques, the 3D Otsu method has been developed. Among all Otsu’s methods, 3D Otsu technique provides the best threshold values for the multi-level thresholding processes. In this paper, to improve the quality of segmented images, a simple and effective multilevel thresholding method is introduced. The proposed approach focuses on preserving edge detail by computing the 3D Otsu along the fusion phenomena. The advantages of the presented scheme include higher quality outcomes, better preservation of tiny details and boundaries and reduced execution time with rising threshold levels. The fusion approach depends upon the differences between pixel intensity values within a small local space of an image; it aims to improve localized information after the thresholding process. The fusion of images based on local contrast can improve image segmentation performance by minimizing the loss of local contrast, loss of details and gray-level distributions. Results show that the proposed method yields more promising segmentation results when compared to conventional 1D Otsu, 2D Otsu and 3D Otsu methods, as evident from the objective and subjective evaluations.      

改进的二维最小卡方散度图像分割方法

传统的交叉熵阈值法具有抗噪性能差,计算时间长等问题。为了改进算法的性能,提出了一种二维最小卡方散度图像阈值化分割新准则,构建了基于改进中值滤波的新型二维直方图。利用对称卡方散度描述分割前后图像之间的差异程度。使用关键阈值对滤波图像进行分割,达到最佳的分割效果。实验结果表明,与二维Otsu和二维最小交叉熵法相比,提出的方法不仅大大缩短了分割时间,而且分割性能与抗噪性能更强。     

灰度梯度映射函数改进的Otsu阈值法

针对目标与背景灰度分布不均匀的图像,基于集中于目标的图像阈值法思想,引入图像的灰度直方图信息,得到更为细致的阈值化准则。考虑图像的边缘信息,引入灰度梯度映射函数,提出了基于梯度的集中于目标的Otsu阈值法。大量经典图像阈值化结果表明,该方法在目标提取的完整性和边缘保留的清晰性方面,均表现出了更佳的效果。     

利用混沌布谷鸟优化的二维Renyi灰度熵图像阈值选取

为了进一步降低现有的Renyi熵阈值法的计算复杂度,提出了基于混沌布谷鸟算法和二维Renyi灰度熵的阈值选取。首先,引入一维Renyi灰度熵阈值选取公式,建立基于像素灰度和邻域梯度的二维直方图,推导出基于该直方图的二维Renyi灰度熵阈值选取公式,通过快速递推公式来减少阈值准则函数的计算量;最后,采用混沌布谷鸟算法搜索最优阈值来完成图像分割。结果表明,与二维Arimoto熵法、基于粒子群的二维Renyi熵法、基于混沌粒子群的二维Tsallis灰度熵法、基于布谷鸟算法的二维Renyi灰度熵法相比,所提出的方法能够准确实现图像分割,且运算速度有所提升。     

利用混沌PSO或分解的2维Tsallis灰度熵阈值分割

现有最大Shannon熵或Tsallis熵阈值选取方法没有从类内灰度均匀性出发,而仅依据图像灰度直方图,并且Tsallis熵法的分割效果通常优于Shannon熵法。为此,提出了基于混沌粒子群优化(PSO)和基于分解的两种2维Tsallis灰度熵阈值分割方法。首先,给出了1维Tsallis灰度熵阈值选取方法并将其推广到2维,导出了相应的2维Tsallis灰度熵阈值选取公式及其递推算法;其次,利用混沌PSO算法搜寻2维Tsallis灰度熵法的最佳阈值,并采用递推方式去除迭代过程中适应度函数的冗余运算,大大提高了运行速度;最后,将2维Tsallis灰度熵阈值选取方法的运算转化为两个1维Tsallis灰度熵法的运算,计算复杂度从O(L2)进一步降低到O(L)。实验结果表明,与2维最大Shannon熵法、2维最大Tsallis熵法及2维Tsallis交叉熵法相比,所提出的两种方法可以大幅提高图像分割质量和算法运行速度。     

Edge magnitude based multilevel thresholding using Cuckoo search technique

Multilevel thresholding technique is popular and extensively used in the field of image processing. In this paper, a multilevel threshold selection is proposed based on edge magnitude of an image. The gray level co-occurrence matrix (second order statistics) of the image is used for obtaining multilevel thresholds by optimizing the edge magnitude using Cuckoo search technique. New theoretical formulation for objective functions is introduced. Key to our success is to exploit the correlation among gray levels in an image for improved thresholding performance. Apart from qualitative improvements the method also provides us optimal threshold values. Results are compared with histogram (first order statistics) based between-class variance method for multilevel thresholding. It is observed that the results of our proposed method are encouraging both qualitatively and quantitatively.     

Segmentation fusion based on neighboring information for MR brain images

In this paper, we study on how to boost image segmentation algorithms. First of all, a novel fusion scheme is proposed to combine different segmentations with mutual information to reduce misclassified pixels and obtain an accurate segmentation. As the class label of each pixel depends on the pixel’s gray level and neighbors’ labels, the fusion scheme takes both spatial and intensity information of pixels into account. Then, a detail thresholding segmentation case is designed using the proposed fusion scheme. In the case, the local Laplacian filter is used to get the smoothed version of original image. To accelerate segmentation, a discrete curve evolution based Otsu method is employed to segment the original image and its smoothed version to get two different segmentation maps. The fusion scheme is used to fuse the two maps to get the final segmentation result. Experiments on medical MR-T2 brain images are conducted to demonstrate the effectiveness of the proposed segmentation fusion method. The experimental results indicate that the proposed algorithm can improve segmentation accuracy and it is superior to other multilevel thresholding methods.     

基于冯诺依曼邻居的粒子群多阈值分割算法*

针对多阈值分割问题,提出了一种新的多阈值分割算法。该算法对传统的Otsu进行修改,使其能够更准确地找到直方图中谷的位置;并利用冯诺依曼拓扑邻居粒子群作为阈值优化算法,提高了优化性能。提出了构造冯诺依曼拓扑结构的方法,并分析和比较了它与全互连结构的差别。实验结果显示此算法具有较好的性能。     

Modified bacterial foraging algorithm based multilevel thresholding for image segmentation

Multilevel thresholding is one of the most popular image segmentation techniques. In order to determine the thresholds, most methods use the histogram of the image. This paper proposes multilevel thresholding for histogram-based image segmentation using modified bacterial foraging (MBF) algorithm. To improve the global searching ability and convergence speed of the bacterial foraging algorithm, the best bacteria among all the chemotactic steps are passed to the subsequent generations. The optimal thresholds are found by maximizing Kapur's (entropy criterion) and Otsu's (between-class variance) thresholding functions using MBF algorithm. The superiority of the proposed algorithm is demonstrated by considering fourteen benchmark images and compared with other existing approaches namely bacterial foraging (BF) algorithm, particle swarm optimization algorithm (PSO) and genetic algorithm (GA). The findings affirmed the robustness, fast convergence and proficiency of the proposed MBF over other existing techniques. Experimental results show that the Otsu based optimization method converges quickly as compared with Kapur's method.     

基于混沌布谷鸟优化的二维Tsallis交叉熵建筑物遥感图像分割

为了进一步提升建筑物遥感图像分割的准确性和运算速度,本文提出了基于混沌布谷鸟优化的二维Tsallis交叉熵的建筑物遥感图像分割方法。首先给出了二维Tsallis交叉熵的阈值选取公式,然后将Logistic混沌映射引入布谷鸟算法,进一步加快布谷鸟算法的收敛速度,最后通过该混沌布谷鸟算法优化基于二维Tsallis交叉熵的阈值寻找过程,并以得到的最优阈值分割建筑物遥感图像。大量实验结果表明,与二维倒数交叉熵法、二维Tsallis熵法、基于混沌粒子群优化的二维Tsallis灰度熵法等方法相比较,本文方法分割的目标更为准确,细节更为清晰,且运算时间更短。     

Optimal segmentation of brain MRI based on adaptive bacterial foraging algorithm

Segmentation of brain magnetic resonance images (MRIs) can be used to identify various neural disorders. The MRI segmentation facilitates in extracting different brain tissues such as white matter, gray matter and cerebrospinal fluids. Segmentation of these tissues helps in determining the volume of the tissues in three-dimensional brain MRI, which yields in analyzing many neural disorders such as epilepsy and Alzheimer disease. In this article, multilevel thresholding based on adaptive bacterial foraging (ABF) algorithm is presented for brain MRI segmentation. The proposed ABF algorithm employs an adaptive step size to improve both exploration and exploitation capability of the BF algorithm. Maximization of the measure of separability on the basis of the entropy (Kapur) method and the between-class variance (Otsu) method, which are the two popular thresholding techniques, are employed to evaluate the performance of the proposed method. Application results to axial, T2-weighted brain MRI slices are provided to show the performance of the proposed segmentation approach. These results are compared with bacterial foraging (BF) algorithm, particle swarm optimization (PSO) algorithm and genetic algorithm (GA) in terms of solution quality, robustness and computational efficiency.     

改进的红外图像2维Otsu分割算法

针对红外图像有别于一般灰度图像的特点,常用的灰度级平均灰度级2维直方图区域划分在红外图像分割中效果不佳,为此提出一种改进的灰度级梯度2维Otsu阈值选取方法,选取合适的梯度算子,利用改进的粒子群优化算法寻找分割阈值,在算法中加入有效判断早熟停滞的方法,一旦检索到早熟迹象,便随机改变最优解的任意1维分量值,使其跳出局部最大,实现全局寻优过程的快速收敛。仿真实验结果表明,该算法由于使用新的2维直方图,分割后的红外图像边界形状准确,特征细节清晰,运算速度也得到了有效提高。     

微粒群算法在二维模糊熵图像分割中的应用

将微粒群算法和二维模糊熵阏值分割法结合,提出了一种基于微粒群和二维模糊熵的图像分割方法.该方法根据像素点灰度值和区域灰度均值所建立的二维灰度直方图,以二维模糊熵作为微粒群算法的适应度函数,利用微粒群算法搜索点灰度值和区域灰度均值所对应的模糊参数最优组合,进而确定相应的分割阈值.对几例真实目标图像的对比分割实验结果表明,该文方法性能优越,是一种有效的图像分割方法.     

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.