带偏场校正和邻域约束的快速MR脑组织分割新方法

 由于部分容积效应(PVE)、图像的偏场(INU)和噪声的存在,脑组织磁共振(MR)图像自动准确的分割是一项具有挑战性的任务.本文提出了一个准确度高并快速鲁棒的二维(2D)和三维(3D)分割算法来将脑部MR图象分割为白质(WM)、灰质(GM)和脑脊液(CSF)三种主要的解剖组织类型.该算法在标准模糊C-均值算法(FCM)的基础上提出了一个新的目标函数,包含偏场校正和邻域约束.在该算法中,采用参数模型表示INU,并且一个类似马尔可夫随机场(MRF)的邻域约束来表示脑组织空间分布一致性信息.本文给出了该算法的模拟和真实脑MR图像的分割结果,同时与其它算法进行了比较.比较结果显示该算法具有较高的准确度和较快的收敛速度.     

视频对象分割中基于Gibbs随机场模型的空分割结合方法

本文提出了一种基于Gibbs随机场模型的时空分割结合方法,用于视频对象的分割.该方法为每一帧图像的分割模板建立Gibbs随机场模型,将时间域分割结果作为初始标记场,空间域的分割结果作为一个图像观察场,然后利用Gibbs模型的约束条件将二者结合起来,得到该帧最后的分割标记场.实验结果表明,这种时空结合方法可以较好地避免以往的比重法过分依赖于空间域分割精度的问题.     

视频对象分割中基于Gibbs随机场模型的时空分割结合方法

本文提出了一种基于Gibbs随机场模型的时空分割结合方法 ,用于视频对象的分割 .该方法为每一帧图像的分割模板建立Gibbs随机场模型 ,将时间域分割结果作为初始标记场 ,空间域的分割结果作为一个图像观察场 ,然后利用Gibbs模型的约束条件将二者结合起来 ,得到该帧最后的分割标记场 .实验结果表明 ,这种时空结合方法可以较好地避免以往的比重法过分依赖于空间域分割精度的问题 .     

基于GAN的ISAR图像语义分割方法

逆合成孔径雷达（ISAR）成像技术能够对空间目标进行远距离成像,刻画目标的外形、结构和尺寸等信息。ISAR图像语义分割能够获取目标的感兴趣区域,是ISAR图像解译的重要技术支撑,具有非常重要的研究价值。由于ISAR图像表征性较差,图像中散射点的不连续和强散射点存在的旁瓣效应使得人工精准标注十分困难,基于交叉熵损失的传统深度学习语义分割方法在语义标注不精准情况下无法保证分割性能的稳健。针对这一问题,提出了一种基于生成对抗网络（GAN）的ISAR图像语义分割方法,采用对抗学习思想学习ISAR图像分布到其语义分割图像分布的映射关系,同时通过构建分割图像的局部信息和全局信息来保证语义分割的精度。基于仿真卫星目标ISAR图像数据集的实验结果证明,本文方法能够取得较好的语义分割结果,且在语义标注不够精准的情况下模型更稳健。     

甲状腺结节超声图像分割算法研究

超声甲状腺结节分割是发现与识别甲状腺良恶性肿瘤的关键技术之一.针对模糊聚类法无法准确分割超声图像甲状腺结节边缘,而局部拟合(RSF)模型法对手动初始化轮廓敏感的问题,提出一种融合空间约束模糊C均值聚类和局部拟合RSF模型的分割结节方法.用空间约束模糊C均值聚类法(SKFCM)对图像进行聚类并二值化聚类结果作为RSF模型法初始轮廓,克服了RSF模型法对初始轮廓敏感问题,水平集演化参数也将通过聚类结果自动给出,不再需要人为设定.同时改进了RSF模型法拟合项,并利用高斯正则化规则RSF模型水平集,提高了RSF模型演化效率,缩短了收敛时间.仿真实验结果表明,提出的甲状腺结节超声图像分割方法能够快速准确地分割出结节区域.     

基于一种动态颜色模型的模糊C-均值图像分割

不同颜色的可见光本质上是具有不同波长范围的电磁波.本文试探性地提出了一种动态颜色模型,它模拟了成像曝光时间内图像平面所接收到的电磁波的动态变化.离散化之后,彩色图像的颜色特征能够被表示成一个K维矢量,称为彩色图像的动态颜色空间表示.然后建立了模糊C-均值分割算法,分别在动态颜色空间和RGB空间分割彩色图像,实验结果表明动态颜色空间的分割结果优于RGB空间的分割,从而验证了动态颜色空间的性能.笔者相信本文所提出的动态颜色模型也能够被用于纹理分析或其它的图像处理领域.     

基于HSV空间的大壁虎脑图谱图像分割研究

针对大壁虎染色脑切片图像的分割需要,提出一种基于HSV空间的图像分割方法.根据脑图谱本身的染色特征以及人工识别脑图谱的经验,建立脑图谱颜色特征经验模型,分析该模型中的特征图片,从而得到分割阈值,据此利用Matlab 图像编程,对脑图谱图像进行多闲值分割.对试验结果进行分析,发现该算法能够将大壁虎脑图谱分割为简洁、直观和清晰的特征脑区,并且具有一定的抗噪声能力.     

融合背景能量项的Graph Cuts PCB CT图像分割

提出了一种新的交互式图像分割方法,该方法通过空间曲面拟合方法生成背景能量项,并将其融入Graph Cuts的分割模型中,以实现对背景灰度不均匀的PCBCT图像分割。本文的方法通过概率模型将自顶向下的全局信息和传统图割模型中自底向上的局部信息有效结合起来,其中背景能量项依据用户添加的背景种子点自适应地生成。通过对多组PCB CT图像进行分割实验,结果表明与经典的Graph Cuts和Grabcut等算法相比,本文方法能得到更准确的分割结果。     

图像分割中局部能量驱动的快速主动轮廓模型

为了解决图像对象灰度分布不一致性的分割难题,提高图像分割速度,提出了一个全新的快速主动轮廓模型。它由曲线周围局部的统计信息驱动曲线发生形变演化,并使用图像中的边缘信息来引导曲线的演化方向。模型中,根据区域模板与演化曲线共同定义的局部统计信息创建数据拟合项,并应用水平集方法求解曲线的演化。对合成图像和医学图像的实验结果表明,本文提出的分割模型可以同时分割多个灰度不一致的对象,分割速度快,结果稳定,对噪声具有很好的鲁棒性。     

基于注意力时空解耦3D卷积LSTM的视频预测

为高效提取视频时空特征以提高视频预测准确性,提出了注意力时空解耦3D卷积LSTM算法.首先,将卷积LSTM内部单元的传统2D卷积运算改为3D卷积,额外提取视频帧间短期空间运动信息;并借助注意力机制自动捕捉视频帧间长期动态信息的相关性.其次,由于卷积LSTM网络中特征信息在所有层的Z型传递方式会导致梯度消失,为此在网络结构中加入层间高速通道优化不同层间LSTM单元视频信息流的传递过程.同时,时间特征和空间特征在网络中会彼此干扰学习冗余功能,造成特征信息的低效获取以及网络预测质量的降低,为此在损失函数中加入时空解耦运算分离时间特征和空间特征的学习.最后,针对训练编码阶段和预测解码阶段的数据输入过程,提出数据输入重采样,在模型训练和预测阶段使用相近相反的数据输入策略减少编码器和解码器的差异.在合成数据集以及人体动作数据库上的实验结果表明,该算法模型在时空特征提取上有更好的性能.     

Brain MR image segmentation based on local Gaussian mixture model and nonlocal spatial regularization

Brain Magnetic Resonance (MR) images often suffer from the inhomogeneous intensities caused by the bias field and heavy noise. The most widely used image segmentation algorithms, which typically rely on the homogeneity of image intensities in different regions, often fail to provide accurate segmentation results due to the existence of bias field and heavy noise. This paper proposes a novel variational approach for brain image segmentation with simultaneous bias correction. We define an energy functional with a local data fitting term and a nonlocal spatial regularization term. The local data fitting term is based on the idea of local Gaussian mixture model (LGMM), which locally models the distribution of each tissue by a linear combination of Gaussian function. By the LGMM, the bias field function in an additive form is embedded to the energy functional, which is helpful for eliminating the influence of the intensity inhomogeneity. For reducing the influence of noise and getting a smooth segmentation, the nonlocal spatial regularization is drawn upon, which is good at preserving fine structures in brain images. Experiments performed on simulated as well as real MR brain data and comparisons with other related methods are given to demonstrate the effectiveness of the proposed method.     

Unsupervised multi class segmentation of 3D images with intensity inhomogeneities

Intensity inhomogeneities in images cause problems in gray-value based image segmentation since the varying intensity often dominates over gray-value differences of the image structures. In this paper we propose a novel biconvex variational model that includes the intensity inhomogeneities to tackle this task. We combine a total variation approach for multi class segmentation with a multiplicative model to handle the inhomogeneities. In our model we assume that the image intensity is the product of a smoothly varying part and a component which resembles important image structures such as edges. Therefore, we penalize in addition to the total variation of the label assignment matrix a quadratic difference term to cope with the smoothly varying factor. A critical point of the resulting biconvex functional is computed by a modified proximal alternating linearized minimization method (PALM). We show that the assumptions for the convergence of the algorithm are fulfilled. Various numerical examples demonstrate the very good performance of our method. Particular attention is paid to the segmentation of 3D FIB tomographical images serving as a motivation for our work.     

基于时序信息的红外图像缺陷信息提取

主动红外热像检测技术中,红外图像的缺陷信息提取是其核心内容。传统的红外图像处理方法在一定程度上可以消除噪声、提高图像的对比度,但是仍存在一些问题,如:需要手动选择特征信息丰富的红外图像,红外图像增强和图像分割过程中会引入主观成分,仅仅分析单张红外图像可能存在信息丢失等问题。针对上述问题,本文根据主动红外热成像的数据特征提出了一种基于时序信息的红外图像缺陷信息提取方法。首先,通过室内实验制作含缺陷分层的混凝土试块;然后,利用主动红外热像检测技术进行三维红外图像数据的采集,提取每个像素点的时序信息;最后,采用基于时序信息的K-means方法进行缺陷特征提取。结果表明,基于时序信息的缺陷提取方法是可行的,其可以提取到隐藏的分层缺陷信息,提取效果优于基于空域信息的K-means方法。     

一种新的基于CV模型的图像分割算法

CV模型是一种重要的图像分割模型,本文针对其收敛速度慢、效率低的缺点提出一种求解CV模型的新方法。首先将CV模型的能量泛函改写成与原来有相同稳定解的总变分公式形式,然后使用对偶公式法求总变分公式的极小值,再在其中引入一速度项以加快模型的收敛速度。新方法一方面克服了梯度下降法要求时间步长小、迭代次数多的缺点,经过较少次的迭代就能收敛,减少了迭代计算的次数;另一方面,引入的速度项能够减少每次迭代的时间,从而缩短求解模型的时间。速度项的引入同时减少了对梯度的依赖,增强了抗噪性。另外,可以通过调节速度项得到不同数目的同质区域,以适应相同图像不同分割任务的需求。实验结果表明本文方法是有效的。      

基于面阵三维成像激光雷达的目标点云分割技术

针对偏振三维成像系统的高效目标三维点云分割问题,提出一种多维信息融合的高效分割理念。系统采用高分辨率EMCCD相机作为面阵探测器,在一次成像过程中,可同时获得视场中的灰度图像以及三维点云数据。根据该成像特点,建立灰度图的像素坐标与点云数据像素坐标之间的点对点映射关系,结合粒子群优化算法的边缘分割方法,将灰度图中目标分割后的坐标信息映射到三维点云数据中,得到其三维点云数据。该方法将三维点云数据降维处理为二维图像处理,显著降低了计算复杂度,避免了点云数据误差对分割精度造成的影响。实验验证了多维数据融合目标三维点云分割方法的有效性。     

A change detection method for remotely sensed multispectral andmultitemporal images using 3-D segmentation

A new method is proposed fur detection of the temporal changes using three-dimensional (3D) segmentation. The method is a kind of clustering methods for temporal changes. In the method, multitemporal images form a image block in 3D space; x-y plane and time axis. The image block is first divided into spatially uniform sub-blocks by applying binary division process. The division rule is based on the statistical t-test using Mahalanobis distance between spatial coefficient vectors of a local regression model fitted to neighboring sub-blocks to be divided. The divided sub-blocks are then merged into clusters using a clustering technique. The block-based processing, like the spatial segmentation technique, is very effective in reduction of apparent changes due to noise. Temporal change is detected as a boundary perpendicular to the time axis in the segmentation result. The proposed method is successfully applied to actual multitemporal and multispectral LANDSAT/TM images     

Effective Level Set Image Segmentation With a Kernel Induced Data Term

This study investigates level set multiphase image segmentation by kernel mapping and piecewise constant modeling of the image data thereof. A kernel function maps implicitly the original data into data of a higher dimension so that the piecewise constant model becomes applicable. This leads to a flexible and effective alternative to complex modeling of the image data. The method uses an active curve objective functional with two terms: an original term which evaluates the deviation of the mapped image data within each segmentation region from the piecewise constant model and a classic length regularization term for smooth region boundaries. Functional minimization is carried out by iterations of two consecutive steps: 1) minimization with respect to the segmentation by curve evolution via Euler-Lagrange descent equations and 2) minimization with respect to the regions parameters via fixed point iterations. Using a common kernel function, this step amounts to a mean shift parameter update. We verified the effectiveness of the method by a quantitative and comparative performance evaluation over a large number of experiments on synthetic images, as well as experiments with a variety of real images such as medical, satellite, and natural images, as well as motion maps.     

An automatic segmentation method for moving objects based on the spatial-temporal information of video

The new MPEG-4 video coding standard enables content-based functions. In order to support the new standard, frames should be decomposed into Video Object Planes （VOP）, each VOP representing a moving object. This paper proposes an image segmentation method to separate moving objects from image sequences. The proposed method utilizes the spatial-temporal information. Spatial segmentation is applied to divide each image into connected areas and to find pre~：ise object boundaries of moving objects. To locate moving objects in image sequences, two consecutive image frames in the temporal direction are examined and a hypothesis testing is performed with Neyman-Pearson criterion. Spatial segmentation produces a spatial segmentation mask, and temporal segmentation yields a change detection mask that indicates moving objects and the background. Then spatial-temporal merging can be used to get the final results. This method has been tested on several images. Experimental results show that this segmentation method is efficient.     

Automatic identification of functional clusters in FMRI data using spatial dependence

In independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data, extracting a large number of maximally independent components provides a detailed functional segmentation of brain. However, such high-order segmentation does not establish the relationships among different brain networks, and also studying and classifying components can be challenging. In this study, we present a multidimensional ICA (MICA) scheme to achieve automatic component clustering. In our MICA framework, stable components are hierarchically grouped into clusters based on higher order statistical dependence--mutual information--among spatial components, instead of the typically used temporal correlation among time courses. The final cluster membership is determined using a statistical hypothesis testing method. Since ICA decomposition takes into account the modulation of the spatial maps, i.e., temporal information, our ICA-based approach incorporates both spatial and temporal information effectively. Our experimental results from both simulated and real fMRI datasets show that the use of spatial dependence leads to physiologically meaningful connectivity structure of brain networks, which is consistently identified across various ICA model orders and algorithms. In addition, we observe that components related to artifacts, including cerebrospinal fluid, arteries, and large draining veins, are grouped together and encouragingly distinguished from other components of interest.     

Object Oriented Motion-Segmentation for Video-Compression in the CNN-UM

Object-oriented motion segmentation is a basic step of the effective coding of image-series. Following the MPEG-4 standard we should define such objects. In this paper, a fully parallel and locally connected computation model is described for segmenting frames of image sequences based on spatial and motion information. The first type of the algorithm is called early segmentation. It is based on spatial information only and aims at providing an over-segmentation of the frame in real-time. Even if the obtained results do not minimize the number of regions, it is a good starting point for higher level post processing, when the decision on how to regroup regions in object can rely on both spatial and temporal information. In the second type of the algorithm stochastic optimization methods are used to form homogenous dense optical vector fields which act directly on motion vectors instead of 2D or 3D motion parameters. This makes the algorithm simple and less time consuming than many other relaxation methods. Then we apply morphological operators to handle disocclusion effects and to map the motion field to the spatial content. Computer simulations of the CNN architecture demonstrate the usefulness of our methods. All solutions in our approach suggest a fully parallel implementation in a newly developed CNN-UM VLSI chip architecture.