引入门控轴向自注意力的多通道病理图像分割

在苏木精-伊红（HE）染色病理图像中,细胞染色分布的不均匀和各类组织形态的多样性给自动化分割带来了极大挑战。针对传统卷积无法捕获大邻域范围内像素间的关联特征,导致分割效果难以进一步提升的问题,提出引入门控轴向自注意力的多通道分割网络（MCSegNet）模型,以实现病理图像细胞核的精准分割。所提模型采用双编码器和解码器结构,在其中使用轴向自注意力编码通道捕获全局特征,并使用基于残差结构的卷积编码通道获取局部精细特征;在编码通道末端,通过特征融合增强特征表示,从而为解码器提供良好的信息基础;而解码器通过级联多个上采样模块逐步生成分割结果。此外,使用改进的混合损失函数有效解决了病理图像中普遍存在的样本不均衡问题。在MoNuSeg2020公开数据集上的实验结果表明,改进的分割方法比U-Net在F1、交并比（IoU）指标上分别提升了2.66个百分点、2.77个百分点,有效改善了病理图像的分割效果,提高了临床诊断的可靠性。     

分区域去运动模糊

去运动模糊一直是计算机视觉领域中面向画质增强的一个热点研究方向。模糊核的估算是去运动模糊中的关键问题。提出一种新的思路,即首先将模糊图像按照模糊核的相似度进行图像分割,再对分割后的图像应用空间不变去模糊算法。本文方法主要包含以下几个步骤:分离输入图像中的光照、颜色和纹理信息;分割图像;分区域估算模糊核,计算重叠区域模糊核,并根据计算出的模糊核进行分区域单核去模糊;利用重叠区域整合拼接去模糊结果并还原光照和颜色信息。实验结果表明本文方法比基于单核的去运动模糊算法效果要好。     

HSI颜色模型在有核骨髓细胞图像分割中的应用

对经染色得到的有核骨髓细胞图像进行分割可以为白血病形态学诊断提供依据。颜色模型的选择对于彩色图像中颜色信息的充分利用、图像分割效果的好坏起着重要作用。HSI颜色模型符合人类的视觉习惯与视觉心理，有利于程序对图像颜色信息的利用，在本文中采用它作为表达有核骨髓细胞图像颜色信息的模型，根据不同系列细胞在颜色上的不同表现，分别使用H分量、S分量作为阈值条件分割细胞的核区域和浆区域。     

基于色彩均匀度的自然图像色彩—纹理分割方法

针对现有图像分割方法中存在的计算复杂，分割结果平滑性不好等问题，提出了基于色彩均匀度的自然图像色彩—纹理分割方法ISBEC。该方法首先对输入图像进行色彩量化，然后利用量化得到的索引图同时进行色彩分割和基于色彩均匀度的多尺度纹理分析，接着将纹理和色彩分割的结果加以结合，最后合并去掉过分割区域。将灰度图像转为色彩分量相同的彩色图像后，ISBEC算法同样可以用于灰度图像的分割。实验结果验证了ISBEC对自然图像色彩—纹理分割的有效性。     

基于生成对抗网络的自动细胞核分割半监督学习方法

为了减少对标注图像数量的依赖，提出一种新颖的半监督学习方法用于细胞核的自动分割。首先，通过新的卷积神经网络（CNN）从背景中自动提取细胞区域。其次，判别器网络通过应用全卷积网络来为输入的图像生成置信图；同时耦合对抗性损失和标准交叉熵损失，以改善分割网络的性能。最后，将标记图像和无标记图像与置信图结合来训练分割网络，使分割网络可以在提取的细胞区域中识别单个细胞核。对84张图像（训练集中的1/8图像带标注，其余图像无标注）的实验结果表明，提出的细胞核分割方法的分割准确率度量（SEG）得分可以达到77.9%，F1得分可以达到76.0%，这比该方法使用670张图像且训练集中的所有图像都带标注时的表现要好。     

异常宫颈细胞核的自适应局部分割

为实现宫颈液基细胞图像中异常细胞核的准确分割,提出一种新的自适应局部细胞核分割方法：在自适应阶段,采用一种利用灰度和纹理信息的快速自适应阈值算法大致检测出细胞核区域;在局部阶段,对每一个粗分割得到的连通区域,在其局部邻域内,使用一种利用边界和区域信息的、基于泊松概率分布的图割法修正分割结果。将此方法用于苏木素&伊红染色的宫颈液基细胞图像,结果显示本文所提出方法的平均计算时间为1.6秒/图,且比文献[3]的方法在细胞核检测率、和异常细胞核分割精度上均提高了19.7%。     

基于着色分离的免疫组化图像核分割研究

细胞核的分割是免疫组化定量分析中非常关键的一步。提出一个基于着色分离的核分割方法,首先采用颜色去卷积算法对多着色的IHC(immunohistochemical)图像进行着色分离,进而利用改进的SCFCM算法对单着色灰度图像进行粗分割;然后利用分水岭算法分离粘连细胞;最后通过细胞核尺寸分析进行后处理,完成对苏木素或多种染色的免疫组化图像的准确核分割。实验采用9幅乳腺癌样本图像,约1000个细胞核作为数据。与手工勾画结果进行对比分析,得出细胞核检测率为92.66%。实验表明,该方法对免疫组化图像核分割准确率高,且具有较好的鲁棒性。     

基于卷积神经网络的医学宫颈细胞图像的语义分割

显微细胞分割的精度直接影响疾病的判别诊断,特别在宫颈细胞的显微病理图像中,细胞核的形态大小、与细胞质之间的比例参数等对于病情的良恶诊断具有重大的意义。为提高宫颈细胞核质分割的精度,提出一种基于卷积神经网络的医学宫颈细胞图像的语义分割方法。标定宫颈细胞显微图像中的细胞核和细胞质轮廓,制作基于长沙市第二人民医院的基于新柏氏液基细胞学检测TCT(Thinprep cytologic test)制片技术的宫颈TCT细胞涂片的CCTCT数据集;通过卷积神经网络对核质分割模型进行训练,避免人工提取特征;通过反卷积达到图像的语义分割。实验结果表明,该算法在宫颈细胞的显微病理图像中的核质分割准确率高达94.7%,具有很高的鲁棒性和适应性。     

HE染色乳腺癌组织病理图像癌巢与间质分割研究

HE染色的乳腺癌组织病理图像是分析诊断乳腺癌常用的方法,病理学家普遍认为癌巢和间质的病理形态学特征对研究乳腺癌的生物学行为有着预示作用,所以准确分割癌巢和间质显得尤为重要。对于HE染色乳腺癌组织病理图像,视癌巢和间质的分割为图像中像素点的分类问题,提取并分析特征,选取最佳特征组合,然后分类为癌巢或间质,并结合间隔采样、归一化与阈值法。实验表明,该方法能较为准确地分割出癌巢和间质,保证较高准确率和精度,在时间效率上能达到较为满意的结果。     

一种基于色彩和纹理分析的图像分割算法

提出一种基于种子区域增长的快速图像分割方法.该算法首先对输入彩色图像进行色彩量化处理.然后根据图像中量化色彩标签的分布情况,通过设置不同尺寸的图像窗口快速寻找种子区域,并结合图像中的色彩和纹理特征,实现种子区域的快速增长.实验结果表明,本文所提出的图像分割算法在计算时间和分割效果上均有较好性能,特别适用于基于内容的图像检索等应用.     

Unsupervised segmentation and classification of cervical cell images

The Pap smear test is a manual screening procedure that is used to detect precancerous changes in cervical cells based on color and shape properties of their nuclei and cytoplasms. Automating this procedure is still an open problem due to the complexities of cell structures. In this paper, we propose an unsupervised approach for the segmentation and classification of cervical cells. The segmentation process involves automatic thresholding to separate the cell regions from the background, a multi-scale hierarchical segmentation algorithm to partition these regions based on homogeneity and circularity, and a binary classifier to finalize the separation of nuclei from cytoplasm within the cell regions. Classification is posed as a grouping problem by ranking the cells based on their feature characteristics modeling abnormality degrees. The proposed procedure constructs a tree using hierarchical clustering, and then arranges the cells in a linear order by using an optimal leaf ordering algorithm that maximizes the similarity of adjacent leaves without any requirement for training examples or parameter adjustment. Performance evaluation using two data sets show the effectiveness of the proposed approach in images having inconsistent staining, poor contrast, and overlapping cells.     

融合灰度和梯度信息的彩色细胞图像自动分割

为了开发血及骨髓涂片中白细胞自动分类及计算机辅助诊断系统,提出了一种融合灰度空间、彩色信息和数学形态学形态梯度信息的血细胞图像自动分割算法,以完成对白细胞（胞核和胞浆）的分割。在灰度空间,通过改进的迭代阈值分割算法,对白细胞的胞核进行了精确的定位和检出。通过彩色空间变换,有效地利用了图像中血细胞胞浆的颜色信息及先验知识,并且为了抑制过度分割,充分利用梯度信息,合理地对白细胞的胞核和胞浆进行了标记。在灰度梯度图像上提取血细胞的轮廓,并分别赋予不同的标记,表明数学形态梯度算法较传统的边缘检测算子具有更好的边缘提取能力。结果表明,胞核和胞浆的分割正确率分别为95.5％和92.6％,验证了该算法对彩色白细胞图像分割的有效性。     

Combining shape, texture and intensity features for cell nuclei extraction in Pap smear images

In this work, we present an automated method for the detection and boundary determination of cells nuclei in conventional Pap stained cervical smear images. The detection of the candidate nuclei areas is based on a morphological image reconstruction process and the segmentation of the nuclei boundaries is accomplished with the application of the watershed transform in the morphological color gradient image, using the nuclei markers extracted in the detection step. For the elimination of false positive findings, salient features characterizing the shape, the texture and the image intensity are extracted from the candidate nuclei regions and a classification step is performed to determine the true nuclei. We have examined the performance of two unsupervised (K-means, spectral clustering) and a supervised (Support Vector Machines, SVM) classification technique, employing discriminative features which were selected with a feature selection scheme based on the minimal-Redundancy-Maximal-Relevance criterion. The proposed method was evaluated on a data set of 90 Pap smear images containing 10,248 recognized cell nuclei. Comparisons with the segmentation results of a gradient vector flow deformable (GVF) model and a region based active contour model (ACM) are performed, which indicate that the proposed method produces more accurate nuclei boundaries that are closer to the ground truth.     

基于K-L变换和模糊集理论的彩色字符图像分割

根据彩色印刷字符图像的特点,在Lab颜色空间下提取a分量,将彩色图像转换为灰度图像。根据模糊逻辑和阈值分割方法将图像分为目标区域、背景区域以及模糊区域。用K-L变换组合邻域的区域隶属信息和灰度信息,将灰度域换成模糊域,在该模糊域上进行分割。经实践,该算法在工业环境中对复杂背景的彩色印刷图像可以得到较好的分割效果,其时间复杂度不高于传统的阈值分割算法,并且在分割的精确度上要优于传统的阈值分割算法。     

Segmentation of psoriasis vulgaris images using multiresolution-based orthogonal subspace techniques.

In this paper, a method is proposed for the segmentation of color images using a multiresolution-based signature subspace classifier (MSSC) with application to psoriasis images. The essential techniques consist of feature extraction and image segmentation (classification) methods. In this approach, the fuzzy texture spectrum and the two-dimensional fuzzy color histogram in the hue-saturation space are first adopted as the feature vector to locate homogeneous regions in the image. Then these regions are used to compute the signature matrices for the orthogonal subspace classifier to obtain a more accurate segmentation. To reduce the computational requirement, the MSSC has been developed. In the experiments, the method is quantitatively evaluated by using a similarity function and compared with the well-known LS-SVM method. The results show that the proposed algorithm can effectively segment psoriasis images. The proposed approach can also be applied to general color texture segmentation applications.     

Unsupervised segmentation method for cuboidal cell nuclei in histological prostate images based on minimum cross entropy

This paper presents a novel segmentation method for cuboidal cell nuclei in images of prostate tissue stained with hematoxylin and eosin. The proposed method allows segmenting normal, hyperplastic and cancerous prostate images in three steps: pre-processing, segmentation of cuboidal cell nuclei and post-processing. The pre-processing step consists of applying contrast stretching to the red (R) channel to highlight the contrast of cuboidal cell nuclei. The aim of the second step is to apply global thresholding based on minimum cross entropy to generate a binary image with candidate regions for cuboidal cell nuclei. In the post-processing step, false positives are removed using the connected component method. The proposed segmentation method was applied to an image bank with 105 samples and measures of sensitivity, specificity and accuracy were compared with those provided by other segmentation approaches available in the specialized literature. The results are promising and demonstrate that the proposed method allows the segmentation of cuboidal cell nuclei with a mean accuracy of 97%.     

模糊相关图割的非监督层次化彩色图像分割

目的 基于阈值的分割方法能根据像素的信息将图像划分为同类的区域,其中常用的最大模糊相关分割方法,因能利用模糊相关度量划分的适当性,得到较好的分割结果,而广受关注。然而该算法存在划分数需预先确定,阈值的分割结果存在孤立噪声,无法对彩色图像实施分割的问题。为此,提出基于模糊相关图割的非监督层次化分割策略来解决该问题。方法 算法首先将图像划分为若干超像素,以提高层次化图像分割的效率;随后将快速模糊相关算法与图割结合,构成模糊相关图割2-划分算子,在确保分割效率的基础上,解决单一阈值分割存在孤立噪声的问题;最后设计了自顶向下层次化分割策略,利用构建的2-划分算子选择合适的区域及通道,迭代地对超像素实施层次化分割,直到算法收敛,划分数自动确定。结果 对Berkeley分割数据库上300幅图像进行了测试,结果表明算法能有效分割彩色图像,分割精度优于Ncut、JSEG方法,运行时间较这两种方法也提高了近20%。结论 本文算法为最大模糊相关算法在非监督彩色图像分割领域的应用提供指导依据,能用于目标检测和识别领域。     

Hybrid Active Contour Mammographic Mass Segmentation and Classification

This research implements a novel segmentation of mammographic mass. Three methods are proposed, namely, segmentation of mass based on iterative active contour, automatic region growing, and fully automatic mask selection-based active contour techniques. In the first method, iterative threshold is performed for manual cropped preprocessed image, and active contour is applied thereafter. To overcome manual cropping in the second method, an automatic seed selection followed by region growing is performed. Given that the result is only a few images owing to over segmentation, the third method uses a fully automatic active contour. Results of the segmentation techniques are compared with the manual markup by experts, specifically by taking the difference in their mean values. Accordingly, the difference in the mean value of the third method is 1.0853, which indicates the closeness of the segmentation. Moreover, the proposed method is compared with the existing fuzzy C means and level set methods. The automatic mass segmentation based on active contour technique results in segmentation with high accuracy. By using adaptive neuro fuzzy inference system, classification is done and results in a sensitivity of 94.73%, accuracy of 93.93%, and Mathew’s correlation coefficient (MCC) of 0.876.     

一种基于前景与背景划分的区域图像检索方法及实现

区域图像检索(RBIR)是基于内容图像检索(CBIR)的一个分支,它以图像分割为基础,通过图像局部视觉特征的相似性进行图像检索。由于准确的图像分割技术尚不成熟,区域图像检索性能容易受到冗余分割和错误分割的影响。为了降低RBIR中图像分割的影响,提出了一种基于前景和背景划分的区域图像检索方法。该方法通过规则分块、图像分类和有效区域定位来得到图像分割区域,然后应用中心对象提取算法(COEA)获得图像主体对象,最后提取颜色和纹理特征进行相似度匹配。实现了一个基于上述方法的RBIR系统ObFind,实验结果表明该方法不仅具有与SIMPLIcity相当的检索性能,而且计算复杂度更低。