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1.
针对糖尿病视网膜病变(DR)图像,提出了一种基于多任务学习的图像多分类分割方法.首先,通过Otsu阈值算法将大部分无病灶信息像素去除;其次,通过滑动窗口切割的方法将图像切分为若干小尺寸的图像,以解决医学图像分辨率过大以及病灶在图像中占比较小的问题;再次,将不存在病灶的子图剔除,以增大含病灶子图的比例;最后,利用UNet++多任务学习属性,并且用转置卷积代替传统上采样,进行多输出多病灶的图像分割.通过在国际公开的IDRID和DDR数据集上进行验证,在IDRi D上取得0.713 1的m AUPR,在DDR上取得0.569 1的m AUPR.  相似文献   

2.
Multiscale Segmentation of Three-Dimensional MR Brain Images   总被引:1,自引:0,他引:1  
Segmentation of MR brain images using intensity values is severely limited owing to field inhomogeneities, susceptibility artifacts and partial volume effects. Edge based segmentation methods suffer from spurious edges and gaps in boundaries. A multiscale method to MRI brain segmentation is presented which uses both edge and intensity information. First a multiscale representation of an image is created, which can be made edge dependent to favor intra-tissue diffusion over inter-tissue diffusion. Subsequently a multiscale linking model (the hyperstack) is used to group voxels into a number of objects based on intensity. It is shown that both an improvement in accuracy and a reduction in image post-processing can be achieved if edge dependent diffusion is used instead of linear diffusion. The combination of edge dependent diffusion and intensity based linking facilitates segmentation of grey matter, white matter and cerebrospinal fluid with minimal user interaction. To segment the total brain (white matter plus grey matter) morphological operations are applied to remove small bridges between the brain and cranium. If the total brain is segmented, grey matter, white matter and cerebrospinal fluid can be segmented by joining a small number of segments. Using a supervised segmentation technique and MRI simulations of a brain phantom for validation it is shown that the errors are in the order of or smaller than reported in literature.  相似文献   

3.
In this paper, a rapid and automatic color image segmentation method for the serialized slices of the Visible Human is proposed. The main strategy is based on region growing and pixel color difference. A rapid color similarity computing method is improved and applied for classifying different pixels. An algorithm based on corrosion from four directions is proposed to automatically extract the seed points for the serialized slices. Utilizing this method, the color slice images of the Visible Human body can be segmented in series automatically. Also, the multithreading frame of parallel computing is introduced in the entire segmentation process. This method is simple but rapid and automatic. The primary organs of the Visible Human can be segmented clearly and accurately. The 3D models of these organs after 3D reconstruction are satisfactory. This novel method can provide support to the Visible Human research.  相似文献   

4.
基于自适应模糊阈值的植物黑腐病叶片病斑的分割   总被引:2,自引:0,他引:2       下载免费PDF全文
为了更好地研究植物黑腐病,对植物黑腐病病斑图像进行了分割研究,即根据病斑图像的特点,用图像模糊阈值分割法来分割病斑。针对目前图像模糊阈值分割法存在窗口宽度自动选取困难的问题,首先在预先给定隶属函数和图像像素类别数的情况下,提出了图像模糊阈值分割法的自适应窗宽选取方法;然后,针对用图像模糊阈值分割方法难于分割直方图具有单峰或双峰差别很大的图像的问题,提出了一种直方图变换方法,用来对直方图进行变换;最后根据变换后的直方图,再利用自适应模糊阈值分割法对植物黑腐病病斑图像进行分割。用采集到的病斑叶片进行的病斑分割实验结果表明,该算法是有效的与鲁棒的。  相似文献   

5.
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。  相似文献   

6.
二维直方图θ-划分最大平均离差阈值分割算法   总被引:2,自引:0,他引:2  
鉴于常用二维直方图区域直分法存在错分, 最近提出的斜分法不具普遍性, 而基于L1范数的最小一乘准则比最小二乘准则更为合理且简捷, 提出了适用面更广的基于二维直方图θ-划分和最大类间平均离差的图像阈值分割算法. 首先给出了二维直方图θ-划分方法, 采用4条平行斜线及1条其法线与灰度级轴成 θ 角的直线划分二维直方图区域, 按灰度级和邻域平均灰度级的加权和进行阈值分割, 斜分法可视为该方法中θ=45° 的特例; 然后导出了二维直方图θ-划分最大类间平均离差阈值选取公式及其快速递推算法; 最后给出了θ 取不同值时的分割结果及运行时间. θ 取较小值时, 边界形状准确性较高, θ 取较大值时, 抗噪性较强, 应用时可根据实际图像特点及需求合理选取 θ 的值. 与常规二维直方图直分最大类间方差法及最大类间平均离差法相比, 所需运行时间相近, 但本文提出的方法所得分割结果更为准确, 抵抗噪声更为稳健, 且存储空间也大为减少.  相似文献   

7.
This paper describes the design and implementation of a machine vision system CATALOG for detection and classification of some important internal defects in hardwood logs via analysis of computer axial tomography (CT or CAT) images. The defect identification and classification in CATALOG consists of two phases. The first phase comprises of the segmentation of a single CT image slice, which results in the extraction of 2D defect-like regions from the CT image slice. The second phase comprises of the correlation of the 2D defect-like regions across CT image slices in order to establish 3D support. The segmentation algorithm for a single CT image is a complex form of multiple-value thresholding that exploits both, the prior knowledge of the wood structure within the log and the gray-level characteristics of the image. The algorithm for extraction of 2D defect-like regions in a single CT image first locates the pith of the log cross section, groups the pixels in the segmented image on the basis of their connectivity and classifies each 2D region as either a defect-like region or a defect-free region using shape, orientation and morphological features. Each 2D defect-like region is classified as a defect or non-defect via correlation across corresponding 2D defect-like regions in neighboring CT image slices. The 2D defect-like regions with adequate 3D support are labeled as true defects. The current version of CATALOG is capable of 3D reconstruction and rendering of the log and its internal defects from the individual CT image slices. CATALOG is also capable of simulation and rendering of key machining operations such as sawing and veneering on the 3D reconstructions of the logs. The current version of CATALOG is intended as a decision aid for sawyers and machinists in lumber mills and also as an interactive training tool for novice sawyers and machinists. Received: 1 August 1997 / Accepted: 25 August 1999  相似文献   

8.
基于区域分割的水下目标实时识别系统   总被引:1,自引:0,他引:1  
提出了一种基于最优阈值分割算法的水下目标自动实时识别系统。该系统首先运用去噪、图像均衡等方法对实时摄取的水下图像进行预处理。然后运用基于遗传算法优化的 Otsu(即大津方法)最优阈值分割算法对所得图像进行区域分割并提取图像的特征向量。最后采用 BP 神经网络对提取的特征向量进行自动分类从而最终确定了水下目标的类型。水槽仿真试验表明该方法能够在恶劣的环境下自动地检测水下目标,而且该方法具有较强的抗光线干扰能力和较高的准确度。  相似文献   

9.
郭斯羽  鲍美华  翟文娟  唐求 《计算机应用》2010,30(12):3274-3277
针对内皮细胞提出了一种自动分割与细胞区域荧光强度测量方法。通过形态学重建和背景减除消除图像的光照不均匀背景;利用直方图均衡化进行图像增强;增强图像经自动阈值分割与形态学滤波得到二值化粗分割结果;在粗分割结果上利用形态学细化和分水岭算法获取前景与背景的标记点;结合增强图像的梯度及标记点,利用标记点控制的分水岭算法完成分割;最终通过求取分割所得细胞区域内的平均荧光强度完成测量。在实际图像上的实验结果表明,相比于直接使用阈值分割方法,所提出的方法能更准确地完成图像分割及荧光强度的测量。  相似文献   

10.
In this paper, we present a fuzzy Markovian method for brain tissue segmentation from magnetic resonance images. Generally, there are three main brain tissues in a brain dataset: gray matter, white matter, and cerebrospinal fluid. However, due to the limited resolution of the acquisition system, many voxels may be composed of multiple tissue types (partial volume effects). The proposed method aims at calculating a fuzzy membership in each voxel to indicate the partial volume degree, which is statistically modeled. Since our method is unsupervised, it first estimates the parameters of the fuzzy Markovian random field model using a stochastic gradient algorithm. The fuzzy Markovian segmentation is then performed automatically. The accuracy of the proposed method is quantitatively assessed on a digital phantom using an absolute average error and qualitatively tested on real MRI brain data. A comparison with the widely used fuzzy C-means algorithm is carried out to show numerous advantages of our method.  相似文献   

11.
结合超体素和区域增长的植物器官点云分割   总被引:1,自引:0,他引:1       下载免费PDF全文
点云分割是点云识别与建模的基础。为提高点云分割准确率和效率,提出一种结合超体素和区域增长的自适应分割算法。根据三维点云的空间位置和法向量信息,利用八叉树对点云进行初始分割得到超体素。选取超体素的中心体素组成一个新的重采样后的密度均匀点云,降低原始点云数据处理量,从而减少运算时间。建立重采样后点云数据的K-D树索引,根据其局部特征得到点云簇。最后将聚类结果返回到原始点云空间。分别选取植物三个物候期的激光扫描点云,对该方法的有效性进行验证。实验结果表明,该方法分割后点云与手工分割平均拟合度达到93.38%,高于其他同类方法,且算法效率得到明显提升。  相似文献   

12.
Evaluation of segmentation methods is a crucial aspect in image processing, especially in the medical imaging field, where small differences between segmented regions in the anatomy can be of paramount importance. Usually, segmentation evaluation is based on a measure that depends on the number of segmented voxels inside and outside of some reference regions that are called gold standards. Although some other measures have been also used, in this work we propose a set of new similarity measures, based on different features, such as the location and intensity values of the misclassified voxels, and the connectivity and the boundaries of the segmented data. Using the multidimensional information provided by these measures, we propose a new evaluation method whose results are visualized applying a Principal Component Analysis of the data, obtaining a simplified graphical method to compare different segmentation results. We have carried out an intensive study using several classic segmentation methods applied to a set of MRI simulated data of the brain with several noise and RF inhomogeneity levels, and also to real data, showing that the new measures proposed here and the results that we have obtained from the multidimensional evaluation, improve the robustness of the evaluation and provides better understanding about the difference between segmentation methods.  相似文献   

13.
医学图象的识别与分析能够为临床提供定量比的诊断依据,而图象分割是其中最关键的一步。为提高医学图象侵分割效果,提出了一种基于特征距离的阈值分割算法,并将其与颜色特征分类相结合,来对眼科裂隙灯生物显微镜图象上的角膜充血区进行分割,分割结果可用于角膜充血区的定量体分析,另外,该算法中的样本典型值是通过一种三维直方图分块算法来确定的,实验结果表明,该算法可以有效地分割出角膜充血,其分割效果优于欧氏距离阈值法,且分析数据的精度能够达到临床诊断的要求。  相似文献   

14.
Context-based segmentation of image sequences   总被引:1,自引:0,他引:1  
We describe an algorithm for context-based segmentation of visual data. New frames in an image sequence (video) are segmented based on the prior segmentation of earlier frames in the sequence. The segmentation is performed by adapting a probabilistic model learned on previous frames, according to the content of the new frame. We utilize the maximum a posteriori version of the EM algorithm to segment the new image. The Gaussian mixture distribution that is used to model the current frame is transformed into a conjugate-prior distribution for the parametric model describing the segmentation of the new frame. This semisupervised method improves the segmentation quality and consistency and enables a propagation of segments along the segmented images. The performance of the proposed approach is illustrated on both simulated and real image data.  相似文献   

15.
P.D. Sathya  R. Kayalvizhi 《Neurocomputing》2011,74(14-15):2299-2313
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.  相似文献   

16.
The CV (Chan–Vese) model is a piecewise constant approximation of the Mumford and Shah model. It assumes that the original image can be segmented into two regions such that each region can be represented as constant grayscale value. In fact, the objective functional of the CV model actually finds a segmentation of the image such that the within-class variance is minimized. This is equivalent to the Otsu image thresholding algorithm which also aims to minimize the within-class variance. Similarly to the Otsu image thresholding algorithm, cross entropy is another widely used image thresholding algorithm and it finds a segmentation such that the cross entropy of the segmented image and the original image is minimized. Inspired from the cross entropy, a new active contour image segmentation algorithm is proposed. The region term in the new objective functional is the integral of the logarithm of the ratio between the grayscale of the original image and the mean value computed from the segmented image weighted by the grayscale of the original image. The new objective functional can be solved by the level set evolution method. A distance regularized term is added to the level set evolution equation so the level set need not be reinitialized periodically. A fast global minimization algorithm of the objective functional is also proposed which incorporates the edge term originated from the geodesic active contour model. Experimental results show that, the algorithm proposed can segment images more accurately than the CV model and the implementation speed of the fast global minimization algorithm is fast.  相似文献   

17.
针对目前服装图像分割准确率低的问题,提出一种基于HOG特征和E-SVM分类器的服装图像联合分割算法。该算法具体可分为三个迭代的步骤:超像素组合、E-SVM分类器训练、分割传播,并用到辅助数据集。将用户输入的图像结合辅助服装集进行超像素分割,并利用分割传播方法将超像素组合成多个区域。利用分割效果积极的区域的HOG信息训练E-SVM分类器。通过E-SVM分类器以及分割传播方法将输入的图像中的服装分割出来。实验结果表明,该方法能够高准确率地分割出服装图像。  相似文献   

18.
肠道息肉分割能够提供息肉在结肠中的位置和形态信息,方便医生依据其结构变化程度来推断 癌变可能性,有利于结肠癌的早期诊断和治疗。针对许多现有的卷积神经网络所提取的多尺度特征有限,且常 引入冗余和干扰特征,难以应对复杂多变的肠道息肉分割问题,提出了一种融合注意力机制的肠道息肉分割多 尺度卷积神经网络(CNN)。首先,设计不同比例金字塔池化策略提取丰富的多尺度上下文信息;然后,通过在 网络中融入通道注意力机制,模型能够根据目标自适应地选择合适的局部上下文信息和全局上下文信息进行特 征集成;最后,联合金字塔池化策略和通道注意力机制构建多尺度有效语义融合解码网络,增强模型对形状、 大小复杂多变的肠道息肉分割的鲁棒性。实验结果表明,本文模型分割的 Dice 系数、IoU 和灵敏度在 CVC-ClinicDB 数据集上分别为 90.6%,84.4%和 91.1%,在 ETIS-Larib 数据集上分别为 80.6%,72.6%和 79.0%, 其能够从肠镜图像中准确、有效地分割出肠道息肉。  相似文献   

19.
本文介绍了一个用于识别痰液涂片彩色细胞图像中肺癌细胞的彩色图像处理系统。为提高系统识别癌细胞的稳定性与有效性,我们采用了一种细胞分割与分类的分层处理结构。首先,在某一特定的归一化彩色空间中,利用自适应阈值的方法对细胞核进行分割;然后,利用细胞核的形态学特征检出可疑癌细胞;最后用细胞核区的色度学特征对可疑癌细胞进行分类与识别,从而确定是否存在癌细胞。  相似文献   

20.
目的 从影像中快速精准地分割出肺部解剖结构可以清晰直观地分辨各解剖结构间的关系,提供有效、客观的辅助诊断信息,大大提高医生的阅片效率并降低医生的工作量。随着影像分割算法的发展,越来越多的方法应用于分割肺部影像中感兴趣的解剖结构区域,但目前尚缺乏包含多种肺部精细解剖结构的影像数据集。本文创建了一个带标签的肺部CT/CTA (computer tomography/computer tomography angiography)影像数据集,以促进肺部解剖结构分割算法的发展。方法 该数据集共标记了67组肺部CT/CTA影像,包括CT影像24组、CTA影像43组,共计切片图像26 157幅。每组CT/CTA有4个不同的目标区域类别,标记对应支气管、肺实质、肺叶、肺动脉和肺静脉。结果 本文利用该数据集,用于肺部CT解剖结构分割医学影像挑战赛——2020年第四届国际图像计算与数字医学研讨会,该挑战赛提供了一个肺血管、支气管和肺实质的评估平台,通过Dice系数、过分割率、欠分割率、医学和算法行业专家对分割和3维重建效果进行了评估,目的是比较各种算法分割肺部解剖结构的性能。结论 本文详细描述了包括支气管、肺实质、肺叶、肺动脉和肺静脉等解剖结构标签的肺部影像数据集和应用结果,为相关研究人员利用本数据集进行更深入的研究提供参考。  相似文献   

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