Model-based 3-D segmentation of multiple sclerosis lesions in magnetic resonance brain images

Human investigators instinctively segment medical images into their anatomical components, drawing upon prior knowledge of anatomy to overcome image artifacts, noise, and lack of tissue contrast. The authors describe: 1) the development and use of a brain tissue probability model for the segmentation of multiple sclerosis (MS) lesions in magnetic resonance (MR) brain images, and 2) an empirical comparison of the performance of statistical and decision tree classifiers, applied to MS lesion segmentation. Based on MR image data obtained from healthy volunteers, the model provides prior probabilities of brain tissue distribution per unit voxel in a standardized 3-D "brain space". In comparison to purely data-driven segmentation, the use of the model to guide the segmentation of MS lesions reduced the volume of false positive lesions by 50-80%     

Trimmed-likelihood estimation for focal lesions and tissue segmentation in multisequence MRI for multiple sclerosis

We present a new automatic method for segmentation of multiple sclerosis (MS) lesions in magnetic resonance images. The method performs tissue classification using a model of intensities of the normal appearing brain tissues. In order to estimate the model, a trimmed likelihood estimator is initialized with a hierarchical random approach in order to be robust to MS lesions and other outliers present in real images. The algorithm is first evaluated with simulated images to assess the importance of the robust estimator in presence of outliers. The method is then validated using clinical data in which MS lesions were delineated manually by several experts. Our method obtains an average Dice similarity coefficient (DSC) of 0.65, which is close to the average DSC obtained by raters (0.66).     

Automatic detection of gadolinium-enhancing multiple sclerosis lesions in brain MRI using conditional random fields

Gadolinium-enhancing lesions in brain magnetic resonance imaging of multiple sclerosis (MS) patients are of great interest since they are markers of disease activity. Identification of gadolinium-enhancing lesions is particularly challenging because the vast majority of enhancing voxels are associated with normal structures, particularly blood vessels. Furthermore, these lesions are typically small and in close proximity to vessels. In this paper, we present an automatic, probabilistic framework for segmentation of gadolinium-enhancing lesions in MS using conditional random fields. Our approach, through the integration of different components, encodes different information such as correspondence between the intensities and tissue labels, patterns in the labels, or patterns in the intensities. The proposed algorithm is evaluated on 80 multimodal clinical datasets acquired from relapsing-remitting MS patients in the context of multicenter clinical trials. The experimental results exhibit a sensitivity of 98% with a low false positive lesion count. The performance of the proposed algorithm is also compared to a logistic regression classifier, a support vector machine and a Markov random field approach. The results demonstrate superior performance of the proposed algorithm at successfully detecting all of the gadolinium-enhancing lesions while maintaining a low false positive lesion count.     

Automated optic disk boundary detection by modified active contour model

This paper presents a novel deformable-model-based algorithm for fully automated detection of optic disk boundary in fundus images. The proposed method improves and extends the original snake (deforming-only technique) in two aspects: clustering and smoothing update. The contour points are first self-separated into edge-point group or uncertain-point group by clustering after each deformation, and these contour points are then updated by different criteria based on different groups. The updating process combines both the local and global information of the contour to achieve the balance of contour stability and accuracy. The modifications make the proposed algorithm more accurate and robust to blood vessel occlusions, noises, ill-defined edges and fuzzy contour shapes. The comparative results show that the proposed method can estimate the disk boundaries of 100 test images closer to the groundtruth, as measured by mean distance to closest point (MDCP) <3 pixels, with the better success rate when compared to those obtained by gradient vector flow snake (GVF-snake) and modified active shape models (ASM).     

Segmentation of multiple sclerosis lesions in intensity corrected multispectral MRI

To segment brain tissues in magnetic resonance images of the brain, the authors have implemented a stochastic relaxation method which utilizes partial volume analysis for every brain voxel, and operates on fully three-dimensional (3-D) data. However, there are still problems with automatically or semi-automatically segmenting thick magnetic resonance (MR) slices, particularly when trying to segment the small lesions present in MR images of multiple sclerosis patients. To improve lesion segmentation the authors have extended their method of stochastic relaxation by both pre- and post-processing the MR images. The preprocessing step involves image enhancement using homomorphic filtering to correct for nonhomogeneities in the coil and magnet. Because approximately 95% of all multiple sclerosis lesions occur in the white matter of the brain, the post-processing step involves application of morphological processing and thresholding techniques to the intermediate segmentation in order to develop a mask image containing only white matter and Multiple Sclerosis (MS) lesion. This white/lesion masked image is then segmented by again applying the authors' stochastic relaxation technique. The process has been applied to multispectral MRI scans of multiple sclerosis patients and the results compare favorably to manual segmentations of the same scans obtained independently by radiology health professionals.     

Automated segmentation of stromal tissue in histology images using a voting Bayesian model

Over the past two decades, digital histology has been clinically approved for the various cancer diagnosis and prognosis tasks including proliferation rate estimation (PRE). Histology images contain two types of regions: epithelial and stromal. PRE is clinically restricted to epithelial tissue because stromal cells do not become cancerous. PRE has very high inter- and intra-pathologist variability and especially among juniors. The major cause of this variability is the stromal area. In this paper, we digitally segment out all stromal areas and present the pathologist with only epithelial areas for PRE. This reduces inter- and intra-pathologist variability. To that end, we propose a Bayesian voting-based model for removal of stromal cells utilizing cells texture and color. Our results on fifty clinical images show that pathologists’ PRE become more accurate and reproducible. Furthermore, PRE of expert pathologists shows very high inter-observer reliability after our fully automated segmentation. We validate our proposed model by testing three aspects and we find: (i) the effect of our segmentation on the clinical decision is the same before and after our segmentation. (ii) the segmentation similarity dice measure is 86.78 % which is a high similarity level. (iii) the time reduction of the pathologist is, on average, over 39 % which also supports the clinical benefit of our proposed work.     

一种基于异常值检测和群体智能优化神经网络的电价混合预测模型

为了提高短期电价预测精度,本文提出了一种将异常值检测、时间序列分析、神经网络以及群体智能算法相结合的混合算法。作为混合算法的具体实现,文中的异常值检测利用了残差比方法和正态分布方法,群体智能优化算法选取了粒子群(PSO)算法和布谷鸟(CS)算法。作为实例研究,本文将混合模型应用用于澳大利亚新南威尔士州短期电价预测中,结果表明,混合预测方法能在一定程度上提高模型的预测精度。     

Furniture board segmentation based on multiple sensors and integer programming model

In the current furniture production line, the level of automation in the stage of loading and unloading is not high enough. In order to improve its automation, a novel integer programming based method for automatically segmenting board is proposed and a multi-sensor configuration is given. In such a configuration, we include multiple cameras and Lidar sensors. The cameras attached on each board are used to collect quick response (QR) code information, while Lidar sensors can obtain each board''s contours information. We then formulate each board''s segmentation as the integer programming problem. The experimental results show that our method can achieve a very high segmentation accuracy of 95% on average.     

Automated segmentation of cells with IHC membrane staining

This study presents a fully automated membrane segmentation technique for immunohistochemical tissue images with membrane staining, which is a critical task in computerized immunohistochemistry (IHC). Membrane segmentation is particularly tricky in immunohistochemical tissue images because the cellular membranes are visible only in the stained tracts of the cell, while the unstained tracts are not visible. Our automated method provides accurate segmentation of the cellular membranes in the stained tracts and reconstructs the approximate location of the unstained tracts using nuclear membranes as a spatial reference. Accurate cell-by-cell membrane segmentation allows per cell morphological analysis and quantification of the target membrane proteins that is fundamental in several medical applications such as cancer characterization and classification, personalized therapy design, and for any other applications requiring cell morphology characterization. Experimental results on real datasets from different anatomical locations demonstrate the wide applicability and high accuracy of our approach in the context of IHC analysis.     

一种基于流量聚类性和切分性的改进异常检测模型

针对链路层异常检测中,由固定反馈时间点而导致的计算量积压以及大量无意义的采样流量数据等现象,提出了一种基于流量特征值的改进异常检测模型,重点探讨如何通过反馈计算机制实现周期内计算任务的合理优化和缩减采样数据。一方面,在对流持续时间的聚类性进行了深入分析并给出其可能聚类的最优簇基础上,将统一的反馈时间分散到各个聚类时间点;另一方面,基于流时序的可切分性对流量数据进行周期划分,并设计拟合函数对周期内流量特征进行量化表达。在此基础上,设计了改进反馈机制和异常检测算法流程。仿真实验表明,所提出的模型和算法不仅通过优化反馈计算时间提高了检测精度,而且通过降低采样数据冗余提高了检测效率。     

Automated segmentation refinement of small lung nodules in CT scans by local shape analysis

One of the most important problems in the segmentation of lung nodules in CT imaging arises from possible attachments occurring between nodules and other lung structures, such as vessels or pleura. In this report, we address the problem of vessels attachments by proposing an automated correction method applied to an initial rough segmentation of the lung nodule. The method is based on a local shape analysis of the initial segmentation making use of 3-D geodesic distance map representations. The correction method has the advantage that it locally refines the nodule segmentation along recognized vessel attachments only, without modifying the nodule boundary elsewhere. The method was tested using a simple initial rough segmentation, obtained by a fixed image thresholding. The validation of the complete segmentation algorithm was carried out on small lung nodules, identified in the ITALUNG screening trial and on small nodules of the lung image database consortium (LIDC) dataset. In fully automated mode, 217/256 (84.8%) lung nodules of ITALUNG and 139/157 (88.5%) individual marks of lung nodules of LIDC were correctly outlined and an excellent reproducibility was also observed. By using an additional interactive mode, based on a controlled manual interaction, 233/256 (91.0%) lung nodules of ITALUNG and 144/157 (91.7%) individual marks of lung nodules of LIDC were overall correctly segmented. The proposed correction method could also be usefully applied to any existent nodule segmentation algorithm for improving the segmentation quality of juxta-vascular nodules.     

基于显著特征引导的红外舰船目标快速分割方法研究

分割的难度可以利用关于视觉任务的知识来降低。在分析现有分割方法的基础上,提出了一种用目标的显著特征来限定分割区域的分割方法,使基于类间方差的门限法得以应用。同时,考虑了红外图像中船体与发动机等区域存在不同灰度分布的情况,定义了局部分割准则。实验结果证明所提出的方法能成功完成红外舰船目标分割,并能应用于实时舰船目标检测与识别。     

采用图像分割方法进行木材表面缺陷的定量检测

随着木材加工业的集约化发展以及对木材表面加工质量高水平的苛求,传统的人工检测方式已经难以满足木材产品的加工生产。在了解木材表面缺陷的分类、缺陷产生原因和木材缺陷表面图像的特征的基础上,对比分析平均值法、最大值法和加权平均值法3种图像灰度化方法效果,并选定加权平均值法对木材缺陷图像进行灰度化预处理。在Matlab 6.5GUI编程框架下实现木材缺陷检测系统,通过选取Isodata聚类迭代法、Otsu最大方差法、最大熵法和Sobel边缘分割法为基础的4种阈值化图像分割方法对木材缺陷特征的分割效果和分割速率进行实验对比分析。实验结果表明,运用Isodata聚类迭代法的图像分割方法能够快速准确分割图像实现木材缺陷检测。     

基于改进CenterNet的图像多篡改检测模型

针对目前的图像篡改数据集中缺少同时包含多种篡改操作的单张图像的问题,构建了包含多种图像篡改手段的综合数据集(MTO Dataset),每张图片包含复制移动、拼接和移除中的2种或3种篡改操作。针对多篡改检测,提出了一种基于改进CenterNet的图像多篡改检测模型,将RGB图像和经过隐写分析得到的噪声特征图作为特征提取网络的输入,在特征提取网络ResNet-50的每一层卷积前加入门控通道注意力转换单元以促进特征通道间关系。为得到更具辨别性的特征,通过改进后的注意力机制自适应学习并调节特征权重,最后使用改进的损失函数优化边框预测的准确度。实验结果证明,与当前先进模型DETR、EfficientDet和VarifocalNet相比,该模型的F1分数提升0.4%～7.4%,检测速率提高1.32～3.06倍。     

Automated segmentation of intraretinal cystoid fluid in optical coherence tomography

Cystoid macular edema (CME) is observed in a variety of ocular disorders and is strongly associated with vision loss. Optical coherence tomography (OCT) provides excellent visualization of cystoid fluid, and can assist clinicians in monitoring the progression of CME. Quantitative tools for assessing CME may lead to better metrics for choosing treatment protocols. To address this need, this paper presents a fully automated retinal cyst segmentation technique for OCT image stacks acquired from a commercial scanner. The proposed method includes a computationally fast bilateral filter for speckle denoising while maintaining CME boundaries. The proposed technique was evaluated in images from 16 patients with vitreoretinal disease and three controls. The average sensitivity and specificity for the classification of cystoid regions in CME patients were found to be 91% and 96%, respectively, and the retinal volume occupied by cystoid fluid obtained by the algorithm was found to be accurate within a mean and median volume fraction of 1.9% and 0.8%, respectively.     

Automated segmentation and classification of high throughput yeast assay spots

Several technologies for characterizing genes and proteins from humans and other organisms use yeast growth or color development as read outs. The yeast two-hybrid assay, for example, detects protein-protein interactions by measuring the growth of yeast on a specific solid medium, or the ability of the yeast to change color when grown on a medium containing a chromogenic substrate. Current systems for analyzing the results of these types of assays rely on subjective and inefficient scoring of growth or color by human experts. Here, an image analysis system is described for scoring yeast growth and color development in high throughput biological assays. The goal is to locate the spots and score them in color images of two types of plates named "X-Gal" and "growth assay" plates, with uniformly placed spots (cell areas) on each plate (both plates in one image). The scoring system relies on color for the X-Gal spots, and texture properties for the growth assay spots. A maximum likelihood projection-based segmentation is developed to automatically locate spots of yeast on each plate. Then color histogram and wavelet texture features are extracted for scoring using an optimal linear transformation. Finally, an artificial neural network is used to score the X-Gal and growth assay spots using the extracted features. The performance of the system is evaluated using spots of 60 images. After training the networks using training and validation sets, the system was assessed on the test set. The overall accuracies of 95.4% and 88.2% are achieved, respectively, for scoring the X-Gal and growth assay spots.     

基于混合Gamma建模与MRF的SAR图像分割方法

文中基于结合利用SAR图像统计信息和像素的空间约束信息的思路,提出了基于混合Gamma建模和MRF的分割方法,算法中利用混合Gamma模型对SAR图像进行统计建模,利用MRF模型对像素空间相关性进行建模。文中验证了混合Gamma模型参数估计的准确性以及自适应估计分割类别数目的有效性。最后用模拟SAR数据和实测SAR数据验证了文中所提出的分割算法的有效性。     

Automated segmentation of human brain MR images aided by fuzzy information granulation and fuzzy inference

This paper proposes an automated procedure for segmenting an magnetic resonance (MR) image of a human brain based on fuzzy logic. An MR volumetric image composed of many slice images consists of several parts: gray matter, white matter, cerebrospinal fluid, and others. Generally, the histogram shapes of MR volumetric images are different from person to person. Fuzzy information granulation of the histograms can lead to a series of histogram peaks. The intensity thresholds for segmenting the whole brain of a subject are automatically determined by finding the peaks of the intensity histogram obtained from the MR images. After these thresholds are evaluated by a procedure called region growing, the whole brain can be identified. A segmentation experiment was done on 50 human brain MR volumes. A statistical analysis showed that the automated segmented volumes were similar to the volumes manually segmented by a physician. Next, we describe a procedure for decomposing the obtained whole brain into the left and right cerebral hemispheres, the cerebellum and the brain stem. Fuzzy if-then rules can represent information on the anatomical locations, segmentation boundaries as well as intensities. Evaluation of the inferred result using the region growing method can then lead to the decomposition of the whole brain. We applied this method to 44 MR volumes. The decomposed portions were statistically compared with those manually decomposed by a physician. Consequently, our method can identify the whole brain, the left cerebral hemisphere, the right cerebral hemisphere, the cerebellum and the brain stem with high accuracy and therefore can provide the three dimensional shapes of these regions.     

Moving object segmentation and detection for monocular robot based on active contour model

A novel geodesic active contour model based on optical flow information is proposed to segment and detect the moving object for monocular robots. More specifically, an active contour is formulated using the level set method, which eliminates the need of re-initialisation. The developed scheme alleviates the effect of optical flow noise, increasing the robustness of the detection of moving objects. Experimental results show that this algorithm can successfully track a moving target, e.g. a human being.