Nuclei Segmentation Using Marker-Controlled Watershed, Tracking Using Mean-Shift, and Kalman Filter in Time-Lapse Microscopy

It is important to observe and study cancer cells' cycle progression in order to better understand drug effects on cancer cells. Time-lapse microscopy imaging serves as an important method to measure the cycle progression of individual cells in a large population. Since manual analysis is unreasonably time consuming for the large volumes of time-lapse image data, automated image analysis is proposed. Existing approaches dealing with time-lapse image data are rather limited and often give inaccurate analysis results, especially in segmenting and tracking individual cells in a cell population. In this paper, we present a new approach to segment and track cell nuclei in time-lapse fluorescence image sequence. First, we propose a novel marker-controlled watershed based on mathematical morphology, which can effectively segment clustered cells with less oversegmentation. To further segment undersegmented cells or to merge oversegmented cells, context information among neighboring frames is employed, which is proved to be an effective strategy. Then, we design a tracking method based on modified mean shift algorithm, in which several kernels with adaptive scale, shape, and direction are designed. Finally, we combine mean-shift and Kalman filter to achieve a more robust cell nuclei tracking method than existing ones. Experimental results show that our method can obtain 98.8% segmentation accuracy, 97.4% cell division tracking accuracy, and 97.6% cell tracking accuracy     

Lip image segmentation using fuzzy clustering incorporating an elliptic shape function

Recently, lip image analysis has received much attention because its visual information is shown to provide improvement for speech recognition and speaker authentication. Lip image segmentation plays an important role in lip image analysis. In this paper, a new fuzzy clustering method for lip image segmentation is presented. This clustering method takes both the color information and the spatial distance into account while most of the current clustering methods only deal with the former. In this method, a new dissimilarity measure, which integrates the color dissimilarity and the spatial distance in terms of an elliptic shape function, is introduced. Because of the presence of the elliptic shape function, the new measure is able to differentiate the pixels having similar color information but located in different regions. A new iterative algorithm for the determination of the membership and centroid for each class is derived, which is shown to provide good differentiation between the lip region and the nonlip region. Experimental results show that the new algorithm yields better membership distribution and lip shape than the standard fuzzy c-means algorithm and four other methods investigated in the paper.     

Bilateral filtering of diffusion tensor magnetic resonance images.

We extend the well-known scalar image bilateral filtering technique to diffusion tensor magnetic resonance images (DTMRI). The scalar version of bilateral image filtering is extended to perform edge-preserving smoothing of DT field data. The bilateral DT filtering is performed in the Log-Euclidean framework which guarantees valid output tensors. Smoothing is achieved by weighted averaging of neighboring tensors. Analogous to bilateral filtering of scalar images, the weights are chosen to be inversely proportional to two distance measures: The geometrical Euclidean distance between the spatial locations of tensors and the dissimilarity of tensors. We describe the noniterative DT smoothing equation in closed form and show how interpolation of DT data is treated as a special case of bilateral filtering where only spatial distance is used. We evaluate different recent DT tensor dissimilarity metrics including the Log-Euclidean, the similarity-invariant Log-Euclidean, the square root of the J-divergence, and the distance scaled mutual diffusion coefficient. We present qualitative and quantitative smoothing and interpolation results and show their effect on segmentation, for both synthetic DT field data, as well as real cardiac and brain DTMRI data.     

基于云模型、图论和互信息的遥感影像分割方法

针对传统的基于局部信息搜索的分割方法很少考虑图像的全局信息,而且容易忽略影像分割中的随机性和不确定性,本文提出了一种基于云模型、图论和互信息的影像分割方法.使用云模型来反映像素聚类成区域时的不确定性和随机性,将图论方法引入基于互信息的最优割集的生成从而得到全局最优分割,利用云模型区域概念所呈现出的多维特征,通过云综合异质性度量来改进边界权重的计算,从而实现对区域相异性的区分能力.从实验结果来看,本文提出的方法,能产生有意义的、完整的、内部同质的分割区域,在分割精度上基本能满足人眼的视觉要求.     

Unsupervised multistage image classification using hierarchical clustering with a Bayesian similarity measure.

A new multistage method using hierarchical clustering for unsupervised image classification is presented. In the first phase, the multistage method performs segmentation using a hierarchical clustering procedure which confines merging to spatially adjacent clusters and generates an image partition such that no union of any neighboring segments has homogeneous intensity values. In the second phase, the segments resulting from the first stage are classified into a small number of distinct states by a sequential merging operation. The region-merging procedure in the first phase makes use of spatial contextual information by characterizing the geophysical connectedness of a digital image structure with a Markov random field, while the second phase employs a context-free similarity measure in the clustering process. The segmentation procedure of region merging is implemented as a hierarchical clustering algorithm whereby a multiwindow approach using a pyramid-like structure is employed to increase computational efficiency while maintaining spatial connectivity in merging. From experiments with both simulated and remotely sensed data, the proposed method was determined to be quite effective for unsupervised analysis. In particular, the region-merging approach based on spatial contextual information was shown to provide more accurate classification of images with smooth spatial patterns.     

图像分割中的交叉熵和模糊散度算法

本文将交叉熵和模糊散度应用于图像分割中,提出了中最优灰度值选取算法,其一是基于均匀分布假设的最小交叉熵算法,其二是利用后难概率的最大类间交叉熵算法,其三是类间最大模糊散度的改进算法,其四是最小模糊散度算法,针对图像阈什化分割的要求,在后两种算法中构造一种新的模糊录改度函数,本文采用均匀测试和开头测试比较各算法的性能,利用多种类型测试 是到的分割结果,显示了所筛算法的有效性和通用性。     

A novel approach to motion segmentation based on gamma distribution

We provide a new motion segmentation method in image sequences based on gamma distribution. Motion segmentation is very important because it can be employed for video surveillance, object tracking, and action recognition. The Gaussian mixture model (GMM) has been widely used as a distribution model for motion segmentation. However, we found that the gamma distribution model is more suitable than the GMM for the optical flow based motion segmentation. Experimental results show that the proposed method is very effective in producing accurate motion segmentation results in image sequences.     

Coupled nonparametric shape and moment-based intershape pose priors for multiple basal ganglia structure segmentation

This paper presents a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. In biological tissues, such as the human brain, neighboring structures exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models on the shapes and intershape (relative) poses of the structures of interest. This provides a principled mechanism to bring high level information about the shapes and the relationships of anatomical structures into the segmentation problem. For learning the prior densities we use a nonparametric multivariate kernel density estimation framework. We combine these priors with data in a variational framework and develop an active contour-based iterative segmentation algorithm. We test our method on the problem of volumetric segmentation of basal ganglia structures in magnetic resonance images. We present a set of 2-D and 3-D experiments as well as a quantitative performance analysis. In addition, we perform a comparison to several existent segmentation methods and demonstrate the improvements provided by our approach in terms of segmentation accuracy.     

Segmentation and tracking of migrating cells in videomicroscopy with parametric active contours: a tool for cell-based drug testing

This paper presents a segmentation and tracking method for quantitative analysis of cell dynamics from in vitro videomicroscopy data. The method is based on parametric active contours and includes several adaptations that address important difficulties of cellular imaging, particularly the presence of low-contrast boundary deformations known as pseudopods, and the occurence of multiple contacts between cells. First, we use an edge map based on the average intensity dispersion that takes advantage of relative background homogeneity to facilitate the detection of both pseudopods and interfaces between adjacent cells. Second, we introduce a repulsive interaction between contours that allows correct segmentation of objects in contact and overcomes the shortcomings of previously reported techniques to enforce contour separation. Our tracking technique was validated on a realistic data set by comparison with a manually defined ground-truth and was successfully applied to study the motility of amoebae in a biological research project.     

Spatially constrained level-set tracking and segmentation of non-rigid objects

Level-set is a widely used technique in segmentation-based tracking due to its flexibility in handling 2D topological changes and computational efficiency. Most existing level-set models aim at grouping pixels that have similar features into a region, without consideration of the spatial relationship of these pixels. In this paper, we present a novel level-set tracking method that incorporates spatial information to improve the robustness and accuracy of tracking non-rigid objects. Both tracking and segmentation are performed in a unified probabilistic framework, with additional spatial constraints from a part-based model—the Hough Forests. In the stage of tracking, the rigid motion of the target object is estimated by rigid registration in both the color space and the Hough voting space. Then in the stage of segmentation, some support points are obtained from back-projection, and guide the level-set evolution to capture the shape deformation. We conduct quantitative evaluation on two recently proposed public benchmarks: a non-rigid object tracking dataset and the CVPR2013 online tracking benchmark, involving 61 sequences in total. The experimental results demonstrate that our tracking method performs comparably to the state-of-the-arts in the CVPR2013 benchmark, while shows significantly improved performance in tracking non-rigid objects.     

Fractal image compression with region-based functionality

Region-based functionality offered by the MPEG-4 video compression standard is also appealing for still images, for example to permit object-based queries of a still-image database. A popular method for still-image compression is fractal coding. However, traditional fractal image coding uses rectangular range and domain blocks. Although new schemes have been proposed that merge small blocks into irregular shapes, the merging process does not, in general, produce semantically-meaningful regions. We propose a new approach to fractal image coding that permits region-based functionalities; images are coded region by region according to a previously-computed segmentation map. We use rectangular range and domain blocks, but divide boundary blocks into segments belonging to different regions. Since this prevents the use of standard dissimilarity measure, we propose a new measure adapted to segment shape. We propose two approaches: one in the spatial and one in the transform domain. While providing additional functionality, the proposed methods perform similarly to other tested methods in terms of PSNR but often result in images that are subjectively better. Due to the limited domain-block codebook size, the new methods are faster than other fractal coding methods tested. The results are very encouraging and show the potential of this approach for various internet and still-image database applications.     

Modeling Interaction for Segmentation of Neighboring Structures

This paper presents a new method for segmenting medical images by modeling interaction between neighboring structures. Compared to previously reported methods, the proposed approach enables simultaneous segmentation of multiple neighboring structures for improved robustness. During the segmentation process, the object contour evolution and shape prior estimates are influenced by the interactions between neighboring shapes consisting of attraction, repulsion, and competition. Instead of estimating the a priori shape of each structure independently, an interactive maximum a posteriori shape estimation method is used for estimating the shape priors by considering shape prior distribution, neighboring shapes, and image features. Energy functionals are then formulated to model the interaction and segmentation. With the proposed method, neighboring structures with similar intensities and/or textures, and blurred boundaries can be extracted simultaneously. Experimental results obtained on both synthetic data and medical images demonstrate that the introduced interaction between neighboring structures improves segmentation performance compared with other existing approaches.      

Image segmentation and labeling using the Polya urn model

We propose a segmentation method based on Polya's (1931) urn model for contagious phenomena. A preliminary segmentation yields the initial composition of an urn representing the pixel. The resulting urns are then subjected to a modified urn sampling scheme mimicking the development of an infection to yield a segmentation of the image into homogeneous regions. This process is implemented using contagion urn processes and generalizes Polya's scheme by allowing spatial interactions. The composition of the urns is iteratively updated by assuming a spatial Markovian relationship between neighboring pixel labels. The asymptotic behavior of this process is examined and comparisons with simulated annealing and relaxation labeling are presented. Examples of the application of this scheme to the segmentation of synthetic texture images, ultra-wideband synthetic aperture radar (UWB SAR) images and magnetic resonance images (MRI) are provided.     

基于隐马尔可夫高斯随机场模型的模糊聚类高分辨率遥感影像分割算法

本文利用隐马尔可夫随机场和高斯模型分别建立标号场和特征场的邻域关系,提出了基于隐马尔可夫高斯随机场模型的模糊聚类分割算法。该算法用隐马尔可夫随机场模型定义先验概率,并将该先验概率作为尺度控制因子引入到KL（Kullback-Lerbler）信息中,在目标函数的定义中,KL信息作为规则化项,其系数表示算法的模糊程度。在基于高斯模型的后验概率中,像素相关性被定义在空间和谱间,并用该概率的负对数值表征像素点到聚类中心的非相似性测度。通过对合成遥感影像和高分辨率遥感影像进行分割实验,证明了算法的有效性和普适性。     

结合多元信息聚类与空间约束的遥感图像分割方法

为了解决传统聚类算法对聚类表征特征量的依赖性以及定义的不完备性,结合遥感图像的数据的空间位置关系提出了一种结合多元信息聚类与空间约束的遥感图像分割方法。针对某一聚类数据,以若干数据点(多元)组合的方式遍历其所有数据点,并定义多元组合的互信息,以表征该聚类的类内相似性;通过计算类外像素对类内多元组合的互信息,刻画类间的非相似性。在此基础上建立类内相似性和类间差异性,然后结合两者之间的平衡关系建立目标函数,并将Potts模型扩展到目标函数以加入空间约束,最后通过最大化目标函数实现图像分割。对模拟及真实全色遥感影像分割结果的定性、定量分析表明:结合多元信息聚类与空间约束的遥感影像分割方法可以避免聚类表征特征量的定义,从根本上消除其对图像分割的影响,并充分考虑遥感数据的空间位置关系。      

基于可变类FCM算法的多光谱遥感影像分割

为了自动确定多光谱遥感影像中地物目标类别数,该文提出一种基于可变类模糊C均值(Fuzzy C-Means, FCM)的多光谱遥感影像分割方法。首先定义像素与聚类的非相似性测度并据此构建目标函数,而后通过求解目标函数得到最优模糊隶属度和聚类中心。其次,研究模糊因子与影像地物目标类别数的关系,并通过定义划分熵(Partition Entropy, PE)指数优选模糊因子,选择PE指数值稳定收敛后所对应的最小模糊因子值为最优模糊因子,根据模糊因子与类别数的关系得到最优类别数,从而实现了影像的可变类分割。最后,利用提出算法分别对合成和真实多光谱遥感影像进行分割实验,实验结果表明,提出算法不仅能自动确定影像的最优类别数,还能获得较好的分割结果,为实现自动确定遥感影像中地物目标类别数提供新方法。     

High content cellular imaging for drug development

This paper discusses the bioinformatics issues related to high content screening (HCS) and presents a bioinformatics pipeline for HCS drug development. The paper reviews the advanced methods for bioimage processing, cell segmentation, neurite centerline extraction, and cell tracking and registration in the context of HCS of different bioassays. These methods prove that HCS is potentially a powerful tool in quantitative cell biology and drug discovery. There remain, however, many challenging issues that require the development of new bioimage analysis algorithms to fulfil the potential of HCS     

Performance measures for video object segmentation and tracking

We propose measures to evaluate quantitatively the performance of video object segmentation and tracking methods without ground-truth (GT) segmentation maps. The proposed measures are based on spatial differences of color and motion along the boundary of the estimated video object plane and temporal differences between the color histogram of the current object plane and its predecessors. They can be used to localize (spatially and/or temporally) regions where segmentation results are good or bad; and/or they can be combined to yield a single numerical measure to indicate the goodness of the boundary segmentation and tracking results over a sequence. The validity of the proposed performance measures without GT have been demonstrated by canonical correlation analysis with another set of measures with GT on a set of sequences (where GT information is available). Experimental results are presented to evaluate the segmentation maps obtained from various sequences using different segmentation approaches.