White matter hyper-intensities automatic identification and segmentation in magnetic resonance images

A methodology for automatic identification and segmentation of white matter hyper-intensities appearing in magnetic resonance images of brain axial cuts is presented. To this end, a sequence of image processing technics is employed to form an image where the hyper-intensities in white matter differ notoriously from the rest of the objects. This pre-processing stage facilitates the posterior process of identification and segmentation of the hyper-intensity volumes. The proposed methodology was tested on 55 magnetic resonance images from six patients. These images were analysed by the proposed system and the resulted hyper-intensity images were compared with the images manually segmented by experts. The experimental results show the mean rate of true positives of 0.9, the mean rate of false positives of 0.7 and the similarity index of 0.7; it is worth commenting that the false positives are found mostly within the grey matter not causing problems in early diagnosis. The proposed methodology for magnetic resonance image processing and analysis may be useful in the early detection of white matter lesions.     

基于级联随机森林与活动轮廓的3D MR图像分割

针对医学磁共振（Magnetic resonance，MR）图像三维分割中随机森林（Random forest，RF）方法难以获得具有几何约束的结果以及活动轮廓模型（Active contour model，ACM）不能自动分割发生信号混叠的组织结构的问题，提出了一种整合了级联随机森林与活动轮廓模型的磁共振图像三维分割方法.该方法首先从多模态磁共振体数据中提取图像多尺度局部鲁棒统计信息，以此驱动级联随机森林对磁共振图像进行迭代的体素分类，从而获得对组织结构的初步分割结果，进一步将此结果作为初始轮廓与形状先验，整合进一个尺度可调的活动轮廓模型中，将独立的体素分类转化为轮廓曲线演化，最终得到具有几何约束的精确分割结果.在公开数据集上的实验结果表明，本文的自动化分割方法在分割精度和鲁棒性等方面，相比其他同类方法具有较大的性能提升.     

多图谱与联合标签融合策略相结合的主动脉CT图像分割

主动脉图像自动分割技术在主动脉疾病的早期诊断、风险评估及手术治疗中发挥重要作用。本文采用了基于多图谱的医学图像分割技术,并将之与联合标签融合（Joint label fusion,JLF）策略相结合应用于3D主动脉CT图像的自动分割问题中。联合标签融合策略考虑了各个图谱之间的相互关系,能够有效抑制图谱间冗余信息的干扰,进而提高标签融合精度。本文提出了一种图谱更新算法以应对图谱数量不足的问题,在提高分割精度的同时,保持了较低的计算复杂度。在15例主动脉CT图像数据上的分割结果表明,本文方法能有效地对3D主动脉图像进行分割,与3种基于传统融合方式的图谱分割法相比,本文方法具有更高的分割精度。     

基于双水平集的图像分割模型

针对水平集模型对于具有细长拓扑部分的目标和弱边界目标进行分割时存在的问题,提出了双水平集方法.在新的方法中通过两条水平集之间的相互吸引来加速解的收敛,同时提出了一种快速有符号距离函数生成方法,提高了计算效率.传统的水平集通常利用图像边界信息来构造速度函数进行求解,但在待分割目标具有很强噪音或具有弱边界时往往得不到真实解,对此,提出了一种新的基于区域信息的速度构造方法.将双水平集模型应用到合成图像与左心室MR图像的分割实验,结果表明该方法具有较好的分割效果和较高的分割效率.     

基于高斯混合模型的活动轮廓模型脑MRI分割

传统的活动轮廓模型用于图像分割往往基于目标的边界信息,在图像含有强噪音或目标具有弱边界时很难得到真实解.引入高斯混合模型构造新的约束项,在新的约束项作用下模型可以减少噪音的影响,并防止从弱边界泄漏.高斯混合模型求解通常使用Expectation-maximization(EM)算法,该算法是局部优化算法,且对初值敏感.因此引入粒子群算法,并提出一种改进的算法,利用该算法的全局优化性求解高斯混合模型的参数,以提高参数精度.对脑核磁共振图像(MRI)分割实验表明该模型具有较好的分割效果.     

核磁共振图像脑组织自动提取方法

核磁共振图像的脑组织提取是神经图像处理研究中的一个重要步骤。将传统的几何活动轮廓模型与二值水平集函数相结合,提出了一种新型的二值水平集活动轮廓模型,并基于该模型提出了一种能够自动、准确实现MRI脑组织提取的方法。该方法在脑组织内部自动设定最优初始轮廓曲线,将该演化曲线隐含地表示成一个高维函数的零水平集,零水平集在基于区域的图像力驱动下不断演化并达到待分割脑部图像的边缘。将基于该方法的脑组织提取结果与作为金标准的专家手动分割结果和其他流行算法相比较,结果表明提出的脑组织提取方法能够自动、准确和快速地提取MRI脑组织,是一种鲁棒性较好的MRI脑组织提取方法。     

一种改进FCN 的肝脏肿瘤CT 图像分割方法

精准的医学图像分割是辅助疾病诊断和手术规划的必要步骤。由于腹部器官边界 模糊、对比度不高,肝脏肿瘤的自动分割一直是一个难题。针对传统全卷积神经网络(FCN)实 现端到端分割精度不佳等问题,提出了一种卷积型多尺度融合FCN 的CT 图像肝脏肿瘤分割方 法。首先,通过提高对比度、增强和去噪的方式对原始的CT 图像数据集进行预处理;然后使 用处理后的数据集对所设计好的FCN 网络进行训练;最终得出能够精确分割肝脏肿瘤的网络模 型。实验效果采用多种评价指标进行分割结果的评估,并且与多种常见的分割网络进行对比。 实验结果表明本文方法可以精准分割CT 图像中各种形状和大小的肝脏肿瘤,分割效果良好, 能够为临床的诊断提供可靠的依据。     

A novel multiphase active contour model for inhomogeneous image segmentation

The problem of image segmentation has been investigated with a focus on inhomogeneous multiphase image segmentation. Intensity inhomogeneity is an undesired phenomenon that represents the main obstacle for magnetic resonance (MR) and natural images segmentation. The complex images usually contain an arbitrary number of objects. This paper presents a new multiphase active contour model method for simultaneous regions classification of MR images and natural images without bias field correction. In this model, a simple and effective initialization method is taken to speed up the curve evolution toward final results; a new multiphase level set method is proposed to segment the multiple regions. This model not only extracts multiple objects simultaneously, but also provides smooth and accurate boundaries of the objects. The results for experiments on several synthetic and real images demonstrate the effectiveness and accuracy of our model.     

Model-based quantitative AAA image analysis using a priori knowledge

Abdominal aortic aneurysm (AAA) is a serious vascular disease which may have a fatal outcome. AAA shape and size is important for diagnostics and intervention planning. In this paper, we present a new method for segmentation of AAA from computed tomography (CT) angiography images. The method works by segmenting the inner and the outer aortic border. Segmentation of AAA is a challenging problem because of low contrast of the outer aortic border. In our method, the inner aortic border is segmented using a geometric deformable model (GDM) and morphological postprocessing. The GDM is implemented using the level-set algorithm. The outer aortic border is segmented by a preprocessing method utilizing a priori knowledge about the aorta shape, followed by the GDM-based method, and morphological postprocessing. The preprocessing algorithm operates on a slice-by-slice basis with some information flow among neighboring slices. The GDM performs three-dimensional (3D) segmentation, reducing possible errors in the previous step. The proposed method is automatic and requires minimal user assistance. The method was statistically validated on 12 patient scans having a total number of 497 image slices. Statistical analysis has confirmed high correlation between the results obtained by the proposed method and the gold standard obtained by manual segmentation by an expert radiologist.     

分水岭算法分割显微图像中重叠细胞

为实现医学临床显微图像自动快速分析,通过先将二值化后的图像进行距离变换,然后采用快速灰度重建算法重建距离变换后图像,最终用分水岭算法分割变换图像,有效地避免了为防止过分割而提取分水岭标记点过分依赖于图像先验知识的缺陷,实现自动探测目标细胞并分割重叠细胞,并使其适合于临床对算法速度的要求。将算法进行了C++程序实现,并应用于实际临床脱落细胞和病理免疫组化显微图像的自动分割。经过多幅不同疾病、不同背景的临床图像的分割验证,在光照均匀的情况下,该算法可以快速实现图像中细胞的提取以及粘连细胞的自动分割,完成一幅768×576图片的分割在AMD1600+的CPU上处理时间小于2 s,分割效果得到主任医师的认可,因此,该算法应用于临床细胞图像的分割是可行的。     

Automatic magnetic resonance image segmentation by fuzzy intercluster hostility index based genetic algorithm: An application

Segmentation of magnetic resonance (MR) images plays an important role in the medical science or clinical research. In this article, an application of a genetic algorithm (GA) based segmentation algorithm is presented for automatic grouping of unlabeled pixels of the MR images into different homogeneous clusters. Before the segmentation, the information about the optimal number of segments as well as the underlying pixel distribution of an image is not required in this method. The centroid of different segments is demarcated as active/inactive centroid by the fuzzy intercluster hostility index. After that, the test images are segmented by the selected active centroids. The optimal number of segments and their respective centroids are determined by this method. A performance comparison is manifested between the fuzzy intercluster hostility index based GA method and the well-known automatic clustering using differential evolution (ACDE) algorithm and one genetic algorithm based non-automatic algorithm with the help of two real life MR images. The comparison depicted the superiority of the GA based automatic image segmentation method with the help of fuzzy intercluster hostility index over other two algorithms.     

Strong fuzzy c-means in medical image data analysis

This paper presents a robust fuzzy c-means (FCM) for an automatic effective segmentation of breast and brain magnetic resonance images (MRI). This paper obtains novel objective functions for proposed robust fuzzy c-means by replacing original Euclidean distance with properties of kernel function on feature space and using Tsallis entropy. By minimizing the proposed effective objective functions, this paper gets membership partition matrices and equations for successive prototypes. In order to reduce the computational complexity and running time, center initialization algorithm is introduced for initializing the initial cluster center. The initial experimental works have done on synthetic image and benchmark dataset to investigate the effectiveness of proposed, and then the proposed method has been implemented to differentiate the different region of real breast and brain magnetic resonance images. In order to identify the validity of proposed fuzzy c-means methods, segmentation accuracy is computed by using silhouette method. The experimental results show that the proposed method is more capable in segmentation of medical images than existed methods.     

基于圆形约束C-V水平集的肺部CT图像病灶分割

针对肺部CT图像中圆形病灶区域的分割问题,对Chan-Vese水平集图像分割方法进行了分析和改进,提出了基于圆形约束的C-V水平集模型,进而提出了基于圆形约束水平集的肺部图像病灶分割算法,解决了图像中大小不同的多圆检测问题。对合成图像和实际临床肺部CT图像进行了分割实验,结果表明,该方法可以较好地分割出图像中的多个圆形区域,算法具有较好的抗噪性,实现速度较快,有利于实现肺部CT图像肺结节自动检测。     

Graph-cut based interactive segmentation of 3D materials-science images

Segmenting materials’ images is a laborious and time-consuming process, and automatic image segmentation algorithms usually contain imperfections and errors. Interactive segmentation is a growing topic in the areas of image processing and computer vision, which seeks to find a balance between fully automatic methods and fully-manual segmentation processes. By allowing minimal and simplistic interaction from the user in an otherwise automatic algorithm, interactive segmentation is able to simultaneously reduce the time taken to segment an image while achieving better segmentation results. Given the specialized structure of materials’ images and level of segmentation quality required, we show an interactive segmentation framework for materials’ images that has three key contributions: (1) a multi-labeling approach that can handle a large number of structures while still quickly and conveniently allowing manual addition and removal of segments in real-time, (2) multiple extensions to the interactive tools which increase the simplicity of the interaction, and (3) a web interface for using the interactive tools in a client/server architecture. We show a full formulation of each of these contributions and example results from their application.     

基于多目标规划的模糊C均值聚类算法

模糊C均值聚类算法(FCM)是一种非常经典的非监督聚类技术,已被广泛地应用到医学图像分割。由于传统的FCM聚类算法在分割图像时仅利用了图像的灰度信息,未利用图像的空间信息,在分割叠加了噪声的磁共振(MR)图像时分割效果不理想。考虑到脑部MR图像真实的灰度值具有分片为常数的特性,按照合理利用图像空间信息的原则,对传统的FCM聚类算法进行了改进,引入多目标规划的概念,提出了一种新的,更加合理的应用图像空间信息的聚类算法。实验结果表明,应用该算法可以有效地分割含有噪声的图像。     

Machine vision scheme for stain-release evaluation using Gabor filters with optimized coefficients

This paper presents an efficient and practical approach for automatic, unsupervised object detection and segmentation in two-texture images based on the concept of Gabor filter optimization. The entire process occurs within a hierarchical framework and consists of the steps of detection, coarse segmentation, and fine segmentation. In the object detection step, the image is first processed using a Gabor filter bank. Then, the histograms of the filtered responses are analyzed using the scale-space approach to predict the presence/absence of an object in the target image. If the presence of an object is reported, the proposed approach proceeds to the coarse segmentation stage, wherein the best Gabor filter (among the bank of filters) is automatically chosen, and used to segment the image into two distinct regions. Finally, in the fine segmentation step, the coefficients of the best Gabor filter (output from the previous stage) are iteratively refined in order to further fine-tune and improve the segmentation map produced by the coarse segmentation step. In the validation study, the proposed approach is applied as part of a machine vision scheme with the goal of quantifying the stain-release property of fabrics. To that end, the presented hierarchical scheme is used to detect and segment stains on a sizeable set of digitized fabric images, and the performance evaluation of the detection, coarse segmentation, and fine segmentation steps is conducted using appropriate metrics. The promising nature of these results bears testimony to the efficacy of the proposed approach.     

人脸检测及分割方法研究

人脸检测和分割是进行人脸分析和识别的重要组成部分,其目的是从复杂背景图像中检测出人脸的位置,并把人脸分割出来。虽然人们可以毫不困难地在复杂背景中检测和识别出人脸,但是要建立一套完全自动化的人脸和识别系统却是非常困难的。近年来随着计算机技术和图像处理方法的不断发展,出现了各种不同的人脸检测方法。应用了一种基于YIQ颜色模型的人脸检测方法,并且利用惯量最小化原理进行人脸图像的姿态调整,然后进行分割提取。试验结果表明,该方法能较好地从复杂背景中检测出人脸区域并能较完整地分割出人脸。     

一种提花织物图像的有限元分割算法

提花织物图像分割是提花图案设计的关键,曲线演化模型是一种流行的图像分割方法,但是该方法无法检测含噪环境下的图像特征.由于Mumford-Shah(MS)模型能够在噪声环境下对不连续边集进行检测,因此它比曲线演化模型更适于对含噪提花织物图像的分割.提出一种结合有限元法和拟牛顿法的MS模型数值求解算法,并有效用于含噪提花织物图像的分割.首先定义了自适应三角剖分空间上的离散MS模型,并在每次迭代前对有限元网格进行自适应调整,以提高迭代的性能.接着采用拟牛顿最小化方法,通过收敛意义上的离散有限元逼近得到离散MS模型的最小值.该算法被用到含噪提花织物图像的分割中,取得了良好的效果.     

Detection of Plaque in Coronary Artery in CMRI Images and 3D Visualization of Blood Flow

Detection of blocks in coronary arteries is becoming crucial interest for early detection of heart attacks. In this paper we propose a framework for detection of plaque in coronary arteries from cardio vascular magnetic resonance imaging(CMRI). It is a quantitative tool for the assessment of cardiovascular diseases. First, select a region of interest and segment the region of coronary artery using enhanced region based active contour (ERAC). Secondly the centreline extraction and lumen segmentation are integrated to extract the artery centreline using geometric moments and the vessel direction using Hessian matrix and segment the vessel lumen in each slice using ERAC. A boundary searching method is adapted to fine tune the segmented surface in each slice of CMRI image. Third, the soft plaques in the coronary artery are extracted by thresholding the segmented region. Finally a 3D visualization of blood flow in coronary artery is presented and the volume of blood flow is calculated. In the experiments we have employed 22 datasets of CMRI images. The experimental results show an average accuracy of 97.6% and with a mean and standard deviation of false discovery rate of 2.48 ± 0.002.

     

结合MRF能量和模糊速度的乳腺癌图像分割方法

乳腺癌灶的精确分割是乳腺癌计算机辅助诊断的重要前提. 在动态对比增强核磁共振成像(Dynamic contrast-enhanced magnetic resonance imaging, DCE-MRI)的图像中, 乳腺癌灶具有对比度低、边界模糊及亮度不均匀等特点, 传统的活动轮廓模型方法很难取得准确的分割结果. 本文提出一种结合马尔科夫随机场(Markov random field, MRF)能量和模糊速度函数的活动轮廓模型的半自动分割方法来完成乳腺癌灶的分割, 相对于专业医生的手动分割, 本文方法具有速度快、可重复性高和分割结果相对客观等优点. 首先, 计算乳腺DCE-MRI图像的MRF能量, 以增强目标区域与周围背景的差异. 其次, 在能量图中计算每个像素点的后验概率, 建立基于后验概率驱动的活动轮廓模型区域项. 最后, 结合Gabor纹理特征、DCE-MRI时域特征和灰度特征构建模糊速度函数, 将其引入到活动轮廓模型中作为边缘检测项. 在乳腺癌灶边界处, 该速度函数趋向于零, 活动轮廓曲线停止演变, 完成对乳腺癌灶的分割. 实验结果表明, 所提出的方法有助于乳腺癌灶在DCE-MRI图像中的准确分割.