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

To overcome the shortcomings of 1 D and 2 D Otsu’s thresholding techniques, the 3 D Otsu method has been developed.Among all Otsu’s methods, 3 D 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 3 D 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 conventional1 D Otsu, 2 D Otsu and 3 D Otsu methods, as evident from the objective and subjective evaluations.

Image Segmentation of Brain MR Images Using Otsu’s Based Hybrid WCMFO Algorithm

In this study,a novel hybrid Water Cycle Moth-Flame Optimization(WCMFO)algorithm is proposed for multilevel thresholding brain image segmentation in Magnetic Resonance(MR)image slices.WCMFO constitutes a hybrid between the two techniques,comprising the water cycle and moth-flame optimization algorithms.The optimal thresholds are obtained by maximizing the between class variance(Otsu’s function)of the image.To test the performance of threshold searching process,the proposed algorithm has been evaluated on standard benchmark of ten axial T2-weighted brain MR images for image segmentation.The experimental outcomes infer that it produces better optimal threshold values at a greater and quicker convergence rate.In contrast to other state-of-the-art methods,namely Adaptive Wind Driven Optimization(AWDO),Adaptive Bacterial Foraging(ABF)and Particle Swarm Optimization(PSO),the proposed algorithm has been found to be better at producing the best objective function,Peak Signal-to-Noise Ratio(PSNR),Standard Deviation(STD)and lower computational time values.Further,it was observed thatthe segmented image gives greater detail when the threshold level increases.Moreover,the statistical test result confirms that the best and mean values are almost zero and the average difference between best and mean value 1.86 is obtained through the 30 executions of the proposed algorithm.Thus,these images will lead to better segments of gray,white and cerebrospinal fluid that enable better clinical choices and diagnoses using a proposed algorithm.

二维Otsu的Z字折线阈值分割方法

针对二维直方图阈值分割算法存在的因误分类而导致的分割精度下降的问题,结合二维直方图的先验知识的阈值分割方法,提出了二维Otsu的Z字折线阈值分割方法。基于二维Otsu区域斜分法的基础上进行改进。我们结合二维直方图中的边界区域和噪声区域的信息,采用Z字折线阈值作为分割标准,纠正了普遍存在的错误分类,并利用小概率事件原则自适应确定折线方程,实现高精度图像分割的同时还有效提高了算法的适应性。实验结果表明,改进方法能够有效提高图像分割的精度,增强自适应性,与同类分割算法比较,性能有了明显提升。

Automated retinal blood vessels segmentation based on simplified PCNN and fast 2D-Otsu algorithm

According to the characteristics of dynamic firing in pulse coupled neural network (PCNN) and regional configuration in retinal blood vessel network, a new method combined with simplified PCNN and fast 2D-Otsu algorithm was proposed for automated retinal blood vessels segmentation. Firstly, 2D Gaussian matched filter was used to enhance the retinal images and simplified PCNN was employed to segment the blood vessels by firing neighborhood neurons. Then, fast 2D-Otsu algorithm was introduced to search the best segmentation results and iteration times with less computation time. Finally, the whole vessel network was obtained via analyzing the regional connectivity. Experiments implemented on the public Hoover database indicate that this new method gets a 0.803 5 true positive rate and a 0.028 0 false positive rate on an average. According to the test results, compared with Hoover algorithm and method of PCNN and 1D-Otsu, the proposed method shows much better performance.

改进沙猫群优化算法的2D-OTSU多阈值图像分割

针对传统多级阈值图像分割方法精度低、收敛速度慢的问题,提出一种改进的沙猫群优化算法(Improved Sand Cat Swarm Optimization, ISCSO)用于全局优化,并应用于2D-OTSU多阈值图像分割任务。通过使用Henon混沌映射和反向折射机制初始化种群,使得种群的分布更加均匀,提高搜索的起始状态,从而增加算法的全局搜索能力;采用非线性灵敏度更新公式来平衡搜索多样性和收敛精度;引入可变螺旋搜索策略改进位置更新算法,以确保算法具有较好的搜索多样性和跳出局部最优解的能力。选取6张测试图像对ISCSO算法进行2DOTSU多阈值图像分割实验,采用峰值信噪比(PSNR)、特征相似性指数(FSIM)和结构相似性指数(SSIM)对实验结果进行评价。实验结果表明,基于ISCSO算法的2D-OSTU多阈值图像分割方法在图像分割任务中85.2%的结果优于对比算法,具有较强的搜索精度和收敛速度,这证明了ISCSO算法在图像分割领域的有效性和潜力。

基于改进PSO-Otsu的电气设备红外图像分割算法

红外图像分割对于电气设备的热故障检测至关重要。鉴于红外图像存在图像模糊、分辨率不高等缺点,为了增强红外图像的清晰度,改进红外图像分割效果,文章提出一种基于改进PSO优化的Otsu电气设备红外图像分割方法。该方法首先使用Sobel算子锐化图像;其次通过PSO-Otsu算法对电气设备红外图像进行分割;最后依据红外图像的特征优化PSO-Otsu算法中的参数,提高算法运行效率。实验结果表明,所提算法分割后的图像轮廓清晰,可识别性高,在图像分割效果和分割速度上都优于对比算法,能够满足对电气设备红外图像实时处理的需求。

Infrared image segmentation method based on 2D histogram shape modification and optimal objective function

In the methods of image thresholding segmentation, such methods based on two-dimensional （2D） histogram and optimal objective functions are important. However, when they are used for infrared image segmentation, they are weak in suppressing background noises and worse in segmenting targets with non-uniform gray level. The concept of 2D histogram shape modification is proposed, which is realized by target information prior restraint after enhancing target information using plateau histogram equalization. The formula of 2D minimum Renyi entropy is deduced for image segmentation, then the shape-modified 2D histogram is combined wfth four optimal objective functions （i.e., maximum between-class variance, maximum entropy, maximum correlation and minimum Renyi entropy） respectively for the appli- cation of infrared image segmentation. Simultaneously, F-measure is introduced to evaluate the segmentation effects objectively. The experimental results show that F-measure is an effective evaluation index for image segmentation since its value is fully consistent with the subjective evaluation, and after 2D histogram shape modification, the methods of optimal objective functions can overcome their original forms＇ deficiency and their segmentation effects are more or less improvements, where the best one is the maximum entropy method based on 2D histogram shape modification.

A new level set model for cell image segmentation

In this paper we first determine three phases of cell images： background, cytoplasm and nucleolus according to the general physical characteristics of cell images, and then develop a variational model, based on these characteristics, to segment nucleolus and cytoplasm from their relatively complicated backgrounds. In the meantime, the preprocessing obtained information of cell images using the OTSU algorithm is used to initialize the level set function in the model, which can speed up the segmentation and present satisfactory results in cell image processing.

Deer Body Adaptive Threshold Segmentation Algorithm Based on Color Space

In large-scale deer farming image analysis,K-means or maximum between-class variance(Otsu)algorithms can be used to distinguish the deer from the background.However,in an actual breeding environment,the barbed wire or chain-link fencing has a certain isolating effect on the deer which greatly interferes with the identification of the individual deer.Also,when the target and background grey values are similar,the multiple background targets cannot be completely separated.To better identify the posture and behaviour of deer in a deer shed,we used digital image processing to separate the deer from the background.To address the problems mentioned above,this paper proposes an adaptive threshold segmentation algorithm based on color space.First,the original image is pre-processed and optimized.On this basis,the data are enhanced and contrasted.Next,color space is used to extract the several backgrounds through various color channels,then the adaptive space segmentation of the extracted part of the color space is performed.Based on the segmentation effect of the traditional Otsu algorithm,we designed a comparative experiment that divided the four postures of turning,getting up,lying,and standing,and successfully separated multiple target deer from the background.Experimental results show that compared with K-means,Otsu and hue saturation value(HSV)+K-means,this method is better in performance and accuracy for adaptive segmentation of deer in artificial breeding scenes and can be used to separate artificially cultivated deer from their backgrounds.Both the subjective and objective aspects achieved good segmentation results.This article lays a foundation for the effective identification of abnormal behaviour in sika deer.

An Improved Jellyfish Algorithm for Multilevel Thresholding of Magnetic Resonance Brain Image Segmentations

Image segmentation is vital when analyzing medical images,especially magnetic resonance(MR)images of the brain.Recently,several image segmentation techniques based on multilevel thresholding have been proposed for medical image segmentation;however,the algorithms become trapped in local minima and have low convergence speeds,particularly as the number of threshold levels increases.Consequently,in this paper,we develop a new multilevel thresholding image segmentation technique based on the jellyfish search algorithm(JSA)(an optimizer).We modify the JSA to prevent descents into local minima,and we accelerate convergence toward optimal solutions.The improvement is achieved by applying two novel strategies:Rankingbased updating and an adaptive method.Ranking-based updating is used to replace undesirable solutions with other solutions generated by a novel updating scheme that improves the qualities of the removed solutions.We develop a new adaptive strategy to exploit the ability of the JSA to find a best-so-far solution;we allow a small amount of exploration to avoid descents into local minima.The two strategies are integrated with the JSA to produce an improved JSA(IJSA)that optimally thresholds brain MR images.To compare the performances of the IJSA and JSA,seven brain MR images were segmented at threshold levels of 3,4,5,6,7,8,10,15,20,25,and 30.IJSA was compared with several other recent image segmentation algorithms,including the improved and standard marine predator algorithms,the modified salp and standard salp swarm algorithms,the equilibrium optimizer,and the standard JSA in terms of fitness,the Structured Similarity Index Metric(SSIM),the peak signal-to-noise ratio(PSNR),the standard deviation(SD),and the Features Similarity Index Metric(FSIM).The experimental outcomes and the Wilcoxon rank-sum test demonstrate the superiority of the proposed algorithm in terms of the FSIM,the PSNR,the objective values,and the SD;in terms of the SSIM,IJSA was competitive with the others.

基于Deeplabv3+与Otsu模型的输电线电晕放电紫外图像分割方法

电晕放电严重威胁输电线路的安全运行,如何提高其放电区域识别分割准确率是一个亟待解决的问题。而因环境影响及设备性能限制,夜间型紫外成像仪常出现成像不清晰、放电区域对比度不明显等特征,导致难以有效实现电晕放电区域的分割,从而影响放电故障的判定。为此提出了基于Deeplabv3+与Otsu模型的输电线电晕放电紫外图像精确分割方法,首先构建基于Deeplabv3+语义分割模型,对放电区域进行类别分割得到大致区域;然后,利用改进Otsu算法对语义分割结果中放电目标区域方差自适应加权,使得分割阈值近似理想阈值,从而实现电晕放电区域的精确分割。实验结果表明,本文提出的分割方法在测试集中平均像素精度为93.97%,平均交并比为90.85%,分割性能良好。

A Semi-Vectorial Hybrid Morphological Segmentation of Multicomponent Images Based on Multithreshold Analysis of Multidimensional Compact Histogram

In this work, we propose an original approach of semi-vectorial hybrid morphological segmentation for multicomponent images or multidimensional data by analyzing compact multidimensional histograms based on different orders. Its principle consists first of segment marginally each component of the multicomponent image into different numbers of classes fixed at K. The segmentation of each component of the image uses a scalar segmentation strategy by histogram analysis;we mainly count the methods by searching for peaks or modes of the histogram and those based on a multi-thresholding of the histogram. It is the latter that we have used in this paper, it relies particularly on the multi-thresholding method of OTSU. Then, in the case where i) each component of the image admits exactly K classes, K vector thresholds are constructed by an optimal pairing of which each component of the vector thresholds are those resulting from the marginal segmentations. In addition, the multidimensional compact histogram of the multicomponent image is computed and the attribute tuples or ‘colors’ of the histogram are ordered relative to the threshold vectors to produce (K + 1) intervals in the partial order giving rise to a segmentation of the multidimensional histogram into K classes. The remaining colors of the histogram are assigned to the closest class relative to their center of gravity. ii) In the contrary case, a vectorial spatial matching between the classes of the scalar components of the image is produced to obtain an over-segmentation, then an interclass fusion is performed to obtain a maximum of K classes. Indeed, the relevance of our segmentation method has been highlighted in relation to other methods, such as K-means, using unsupervised and supervised quantitative segmentation evaluation criteria. So the robustness of our method relatively to noise has been tested.

基于蛇优化算法的Otsu图像分割方法

Otsu算法是图像处理中运用广泛的图像分割方法,尽管有着计算简单、准确的特性,但因为需要进行穷举运算,所以计算效率不高。为提高图像分割的实时性,引入了蛇优化算法(SO)对Otsu进行了优化,创建了基于蛇优化算法的Otsu图像分割方法(SO-Otsu)。在该算法中,利用蛇优化算法来模拟蛇的特性进行最佳阈值的寻找,以降低迭代时间,提升计算速度。在仿真实验中,利用经典的Lena、Peppers、Goldhill、Cameraman图片进行测试,与基于果蝇优化算法的Otsu方法(FOA-Otsu)和基于麻雀搜索算法的Otsu方法(SSA-Otsu)进行对比。并通过计算峰值信噪比(PSNR)、结构相似性(SSIM)、特征相似性(FSIM)和计算时间作为评价指标结果进行评估。结果表明,与其他算法相比,算法计算效率高、分割细节效果好且综合分割性能优异,为提高图像分割的计算效率提供了一种理想的工具。

基于改进红绿色差和Otsu的葡萄果穗图像分割

为提高田间复杂环境下传统图像分割法分割葡萄果穗图像准确度低的问题,提出一种基于改进红绿色差和Otsu算法的田间葡萄果穗图像分割方法。选取与人类视觉相近的RGB颜色空间,提取并分析R、G特征图的直方图,经分析对其点乘特征图并进行Otsu运算,再经过形态学处理,实现对田间环境下葡萄果穗图像的分割。与灰度图、(R-G)特征图和(R-G)/(R+G)特征图分别采用最大阈值分割法(Otsu)分割的结果进行对比,试验结果表明,红绿色差点乘Otsu分割法的分割结果最优,准确率为92.37%,F_(1)值90.13%。对50幅图像做了测试,其中图像准确率最高为97%,准确率最低为79%,其平均准确率为88.75%。所提出的方法能够实现葡萄果穗较完整的分割,并可为葡萄果穗的识别、定位提供研究基础。

改进蜜獾算法优化OTSU的图像分割研究

群体智能算法结合图像分割技术已经成为图像处理领域中的新热点,传统的图像分割方法需要大量的人力和时间,蜜獾算法(honey badger algorithm,HBA)可以通过模拟蜜獾觅食的行为来执行优化任务,在寻找解决问题的过程中可以逐步逼近最优解来实现图像分割任务;通过反向学习策略改进蜜獾种群的初始化,提高种群多样性和分布平衡,从而提高算法的整体搜索能力;引入柯西变异因子,对算法计算得到的可行解进行扰动,使算法更易于跳出局部最优,增强算法的局部搜索能力和收敛精度;选取三幅测试图像进行分割验证,实验结果显示,融合改进蜜獾算法和二维OTSU算法得到的分割图像精度更高、效果更细致,验证了方法的有效性;综上所述,改进蜜獾算法具有更好的鲁棒性和泛化性,优化的二维OTSU算法可以更好地处理复杂场景和图像。

基于IMFO-Otsu的果实深度图像多阈值分割

为了解决传统果实图像进行阈值分割易受颜色、光照等因素影响的问题,提出一种基于改进飞蛾火焰算法(Improved Moth flame Optimization,IMFO)的多阈值分割算法(IMFO-Otsu)。算法在构建深度直方图后,根据多阈值Otsu准则获取最佳分割阈值。为了提高获取最佳阈值的计算效率,对多阈值Otsu准则进行剪枝处理,并使用提出的改进飞蛾火焰算法对算法进行加速。为验证IMFO-Otsu算法的效果,使用该算法对采集得到的果实图像进行多阈值分割,结果表明提出的算法具有良好的性能。由于提出的算法没有用到彩色图像的颜色信息且简单有效,能在夜间环境等复杂情况对果实识别与定位提供支持。

基于OTSU图像分割算法的碎米检测

碎米作为大米加工过程的常见产物,常会对产品的口感、味道产生影响,因此针对整米中碎米的有效筛分尤为重要。针对上述问题,该文建立基于大津法(maximal variance between clusters,OTSU)图像分割算法的逻辑回归模型用以检测整米中的碎米。将检测结果与国标法进行对比,结果表明逻辑回归模型的曲线线下面积(area under the curve,AUC)值为0.987,柯尔莫可洛夫-斯米洛夫(Kolmogorov-Smirnov,KS)值为0.909,0.5为最佳阈值;而国标法的AUC值为0.922,KS值为0.669,21为最佳阈值。该文所建立的逻辑回归模型的准确率、精确率、召回率及F1分数均高于国标法。此外,逻辑回归模型的AUC值比国标法的AUC值更接近于1,KS值也更高,表明逻辑回归模型能够更好地区分碎米与整米。长轴(x_(1))、面积(x_(2))、短轴(x_(3))与长短轴比(x_(4))4个特征参数都是模型中具有显著影响的因素,对应的线性关系为z=-139.97-5.35x_(1)+10.93x_(2)+2.86x_(3)+34.59x_(4)。

对称约束的类间方差阈值方法

为了提高现有最大类间方差法(OTSU)的阈值化精度和适应性,提出了一种对称约束的类间方差阈值方法。该方法首先对输入图像使用Prewitt算子构建梯度幅值图像,并根据对称性原则提取对称采样区;然后,基于构建的对称约束类间方差目标函数最大化准则选取阈值,并判断在此阈值下对称采样区是否满足对称条件;当无法满足对称条件时,基于对称采样区对输入图像进行对称修正处理,并应用对称约束的类间方差目标函数对修正后的对称采样区选取阈值;最后,使用最终选取的阈值对输入图像阈值化。在28幅合成图像和70幅真实世界图像集上比较了提出的方法与OTSU法及4种OTSU的改进方法的阈值化性能。实验结果表明,提出方法的误分类率在合成图像和真实世界图像上分别为0.0106和0.016,相较于阈值化精度第2的方法在误分类方面分别降低了91.4%和86.1%。提出的方法虽然在计算效率方面不占有优势,但它对不同模态的测试图像具有更稳健的阈值化适应性和更高的阈值化精度。

基于加权灰度图与混合阈值分割方法的光伏热斑检测

在光伏红外热图像中,热斑和部分高温工作区的亮度非常接近。在利用传统的阈值分割技术提取热斑时,往往会将工作区也一并分割出来,形成虚假热斑。结合Otsu算法和Sauvola算法的优点,提出了一种基于加权灰度图的混合阈值分割方法。通过对灰度图加权处理,降低工作区亮度,从而增强热斑与工作区的对比度,改善热斑的可视性。利用Otsu算法与Sauvola算法二值化灰度图,并根据二值图差异度计算混合阈值,以此消除虚假热斑的干扰。实验证明,该方法适用于检测存在高温工作区的光伏板热斑,能够精准有效地分割热斑。

一种改进Canny算子的图像边缘检测算法

在移动机器人自主作业时,环境中往往存在障碍物,路径规划避障时要进行动态目标检测.Canny边缘检测算法可以与众多动态目标检测算法相结合,提高目标检测的效果.但是传统Canny边缘检测存在着自适应性不强,边缘检测可能不连续,或者检测虚假边缘的现象.本文提出了一种优化Canny边缘检测算法,通过改进的自适应中值滤波来预处理图像,对算法效率及对噪声点的处理做出了优化,紧接着增加梯度计算方向,最后结合改进的大津阈值分割法,提出了三阈值分割法代替原始的阈值分割法使图像边缘信息更加完整准确.仿真结果表明,该算法在边缘检测准确率上对比传统Canny边缘检测,Sobel算子与较新改进算法均有20%左右的提升,该算法优化了传统算法检测的连续性和准确率.