A Bayesian approach for stochastic white matter tractography

White matter fiber bundles in the human brain can be located by tracing the local water diffusion in diffusion weighted magnetic resonance imaging (MRI) images. In this paper, a novel Bayesian modeling approach for white matter tractography is presented. The uncertainty associated with estimated white matter fiber paths is investigated, and a method for calculating the probability of a connection between two areas in the brain is introduced. The main merits of the presented methodology are its simple implementation and its ability to handle noise in a theoretically justified way. Theory for estimating global connectivity is also presented, as well as a theorem that facilitates the estimation of the parameters in a constrained tensor model of the local water diffusion profile.     

Robust white matter lesion segmentation in FLAIR MRI

This paper discusses a white matter lesion (WML) segmentation scheme for fluid attenuation inversion recovery (FLAIR) MRI. The method computes the volume of lesions with subvoxel precision by accounting for the partial volume averaging (PVA) artifact. As WMLs are related to stroke and carotid disease, accurate volume measurements are most important. Manual volume computation is laborious, subjective, time consuming, and error prone. Automated methods are a nice alternative since they quantify WML volumes in an objective, efficient, and reliable manner. PVA is initially modeled with a localized edge strength measure since PVA resides in the boundaries between tissues. This map is computed in 3-D and is transformed to a global representation to increase robustness to noise. Significant edges correspond to PVA voxels, which are used to find the PVA fraction α (amount of each tissue present in mixture voxels). Results on simulated and real FLAIR images show high WML segmentation performance compared to ground truth (98.9% and 83% overlap, respectively), which outperforms other methods. Lesion load studies are included that automatically analyze WML volumes for each brain hemisphere separately. This technique does not require any distributional assumptions/parameters or training samples and is applied on a single MR modality, which is a major advantage compared to the traditional methods.     

Automatic microarray spot segmentation using a Snake-Fisher model

Inspired by Paragious and Deriche's work, which unifies boundary-based and region-based image partition approaches, we integrate the snake model and the Fisher criterion to capture, respectively, the boundary information and region information of microarray images. We then use the proposed algorithm to segment the spots in the microarray images, and compare our results with those obtained by commercial software. Our algorithm is automatic because the parameters are adaptively estimated from the data without human intervention.     

A probabilistic model-based approach to consistent white matter tract segmentation

Since the invention of diffusion magnetic resonance imaging (dMRI), currently the only established method for studying white matter connectivity in a clinical environment, there has been a great deal of interest in the effects of various pathologies on the connectivity of the brain. As methods for in vivo tractography have been developed, it has become possible to track and segment specific white matter structures of interest for particular study. However, the consistency and reproducibility of tractography-based segmentation remain limited, and attempts to improve them have thus far typically involved the imposition of strong constraints on the tract reconstruction process itself. In this work we take a different approach, developing a formal probabilistic model for the relationships between comparable tracts in different scans, and then using it to choose a tract, a posteriori, which best matches a predefined reference tract for the structure of interest. We demonstrate that this method is able to significantly improve segmentation consistency without directly constraining the tractography algorithm.     

Automatic tumor segmentation using knowledge-based techniques

A system that automatically segments and labels glioblastoma-multiforme tumors in magnetic resonance images (MRIs) of the human brain is presented. The MRIs consist of T1-weighted, proton density, and T2-weighted feature images and are processed by a system which integrates knowledge-based (KB) techniques with multispectral analysis. Initial segmentation is performed by an unsupervised clustering algorithm. The segmented image, along with cluster centers for each class are provided to a rule-based expert system which extracts the intracranial region. Multispectral histogram analysis separates suspected tumor from the rest of the intracranial region, with region analysis used in performing the final tumor labeling. This system has been trained on three volume data sets and tested on thirteen unseen volume data sets acquired from a single MRI system. The KB tumor segmentation was compared with supervised, radiologist-labeled “ground truth” tumor volumes and supervised K-nearest neighbors tumor segmentations. The results of this system generally correspond well to ground truth, both on a per slice basis and more importantly in tracking total tumor volume during treatment over time     

Automatic blur region segmentation approach using image matting

For images with partial blur such as local defocus or local motion, deconvolution with just a single point spread function surely could not restore the images correctly. Thus, restoration relying on blur region segmentation is developed widely. In this paper, we propose an automatic approach for blur region extraction. Firstly, the image is divided into patches. Then, the patches are marked by three blur features: gradient histogram span, local mean square error map, and maximum saturation. The combination of three measures is employed as the initialization of iterative image matting algorithm. At last, we separate the blurred and non-blurred region through the binarization of alpha matting map. Experiments with a set of natural images prove the advantage of our algorithm.     

Automatic segmentation of ultrasound images using morphological operators

A method for the automatic measurement of femur length in fetal ultrasound images is presented. Fetal femur length measurements are used to estimate gestational age by comparing the measurement to a typical growth chart. Using a real-time ultrasound system, sonographers currently indicate the femur endpoints on the ultrasound display station with a mouse-like device. The measurements are subjective, and have been proven to be inconsistent. The automatic approach described exploits prior knowledge of the general range of femoral size and shape by using morphological operators, which process images based on shape characteristics. Morphological operators are used first to remove the background (noise) from the image, next to refine the shape of the femur and remove spurious artifacts, and finally to produce a single pixel-wide skeleton of the femur. The skeleton endpoints are assumed to be the femur endpoints. The length of the femur is calculated as the distance between those endpoints. A comparison of the measurements obtained with the manual and with the automated techniques is included.     

Atlas-based segmentation of neonatal brain MR images using a gray matter enhancing step

We propose an atlas-based segmentation framework for brain magnetic resonance images, specially designed to fit neonatal images, which pose additional difficulties due to the poor differentiation between the gray and white matter. The main contribution of our work consists of a gray matter enhancing step, which is applied to either the T1w or T2w modalities after standard preprocessing and alignment steps are carried out. Our enhancing step uses Hessian and box filters for the cortical gray matter and takes advantage of both local and non-local information for the subcortical gray matter. We consider four classes, and our framework has been evaluated using publicly available data from the NeoBrainS12 challenge.     

用无需选取参数的Unit-linking PCNN进行自动图像分割

脉冲耦合神经网络（PCNN-Pulse Coupled Neural Network）是一种有生物学依据的人工神经网络，它可有效地用于图像分割。基于PCNN的图像分割效果取决于PCNN中各参数的选择。然而，图像分割时，各种不同的图像对应的PCNN参数是不同的，而PCNN参数的选择是困难的。本文提出了一种基于Unit-linking PCNN的图像分割新方法，解决了PCNN图像分割参数选择的难题。用本文提出的新方法可有效地自动分割各种图像，而无需考虑PCNN参数的选择，这对于PCNN的理论研究和实际应用有重要的意义。     

基于局部Walsh变换和非负矩阵分解的脑白质图像分割

脑白质病变诊断是医学研究和病理分析的重要方面。颅脑核磁共振图像的白质分割在诊断中起着非常重要的作用,其分割的准确性直接影响后续的分析和诊断研究。本文提出了一种基于局部Walsh变换和非负矩阵分解的大脑核磁共振图像白质分割算法。算法首先对颅脑图像进行局部Walsh变换,选择鉴别性能好的特征得到特征矩阵,然后对其进行非负矩阵分解并得到白质的分割结果。实验表明,本方法计算简单,精度比较高,可以得到比较理想的分割结果。     

Representing diffusion MRI in 5-D simplifies regularization and segmentation of white matter tracts

We present a new five-dimensional (5-D) space representation of diffusion magnetic resonance imaging (dMRI) of high angular resolution. This 5-D space is basically a non-Euclidean space of position and orientation in which crossing fiber tracts can be clearly disentangled, that cannot be separated in three-dimensional position space. This new representation provides many possibilities for processing and analysis since classical methods for scalar images can be extended to higher dimensions even if the spaces are not Euclidean. In this paper, we show examples of how regularization and segmentation of dMRI is simplified with this new representation. The regularization is used with the purpose of denoising and but also to facilitate the segmentation task by using several scales, each scale representing a different level of resolution. We implement in five dimensions the Chan-Vese method combined with active contours without edges for the segmentation and the total variation functional for the regularization. The purpose of this paper is to explore the possibility of segmenting white matter structures directly as entirely separated bundles in this 5-D space. We will present results from a synthetic model and results on real data of a human brain acquired with diffusion spectrum magnetic resonance imaging (MRI), one of the dMRI of high angular resolution available. These results will lead us to the conclusion that this new high-dimensional representation indeed simplifies the problem of segmentation and regularization.     

基于标记提取的GrowCut自动分割算法

Grow Cut算法是基于细胞自动机的交互式图像分割方法,针对该算法要求用户标记初始种子需要较多工作量,且带有一定的主观性和不确定性,导致分割结果出现较大误差的问题,文中提出了简化标记,自动生成初始种子模板的基于标记提取的Grow Cut分割算法。该算法在Grow Cut算法基础上通过阈值和形态学方法预处理生成初始种子模板,运用细胞自动机迭代算法完成目标的提取。算法避免了用户人工交互约束的繁琐操作,实现了完全自动分割。通过实验对彩色图像进行自动分割,实验结果证明该算法简便、用时少,分割结果比较精确。     

Automatic segmentation of subcortical brain structures in MR images using information fusion

This paper reports a new automated method for the segmentation of internal cerebral structures using an information fusion technique. The information is provided both by images and expert knowledge, and consists in morphological, topological, and tissue constitution data. All this ambiguous, complementary and redundant information is managed using a three-step fusion scheme based on fuzzy logic. The information is first modeled into a common theoretical frame managing its imprecision and incertitude. The models are then fused and a decision is taken in order to reduce the imprecision and to increase the certainty in the location of the structures. The whole process is illustrated on the segmentation of thalamus, putamen, and head of the caudate nucleus from expert knowledge and magnetic resonance images, in a protocol involving 14 healthy volunteers. The quantitative validation is achieved by comparing computed, manually segmented structures and published data by means of indexes assessing the accuracy of volume estimation and spatial location. Results suggest a consistent volume estimation with respect to the expert quantification and published data, and a high spatial similarity of the segmented and computed structures. This method is generic and applicable to any structure that can be defined by expert knowledge and morphological images.     

水平集分层分割遥感图像中的建筑物

针对高分辨率遥感图像,结合建筑物特征,提出水平集分层模型分割图像中的建筑物。首先,学习植被样本得到其在HSV空间中色调与饱和度的联合分布函数,利用阴影灰度方差通常小于非阴影区域的特点,将植被和阴影剔除以简化背景利于后续分割。然后,根据灰度级高低将一幅图像看作多层图像层,把建筑物的屋顶灰度特征和边缘特征融合到传统Chan-Vese（C-V）水平集算法中,分割出每层中灰度级相似的建筑物候选区域,从而将不同灰度级建筑物候选区域分层分割出来再整合。最后利用建筑物面积、建筑物与阴影位置关系等先验知识排除误分割,得到最终结果。实验表明:该方法能更好地分割出形状各异、各个灰度级的建筑物,甚至是灰度不均匀的建筑物,分割漏检率较传统C-V法降低了25%,虚检率降低了22%。有效减少了漏分割和过分割。     

Construction of an abdominal probabilistic atlas and its application in segmentation

There have been significant efforts to build a probabilistic atlas of the brain and to use it for many common applications, such as segmentation and registration. Though the work related to brain atlases can be applied to nonbrain organs, less attention has been paid to actually building an atlas for organs other than the brain. Motivated by the automatic identification of normal organs for applications in radiation therapy treatment planning, we present a method to construct a probabilistic atlas of an abdomen consisting of four organs (i.e., liver, kidneys, and spinal cord). Using 32 noncontrast abdominal computed tomography (CT) scans, 31 were mapped onto one individual scan using thin plate spline as the warping transform and mutual information (MI) as the similarity measure. Except for an initial coarse placement of four control points by the operators, the MI-based registration was automatic. Additionally, the four organs in each of the 32 CT data sets were manually segmented. The manual segmentations were warped onto the "standard" patient space using the same transform computed from their gray scale CT data set and a probabilistic atlas was calculated. Then, the atlas was used to aid the segmentation of low-contrast organs in an additional 20 CT data sets not included in the atlas. By incorporating the atlas information into the Bayesian framework, segmentation results clearly showed improvements over a standard unsupervised segmentation method.     

Automated brain structure segmentation based on atlas registration and appearance models

Accurate automated brain structure segmentation methods facilitate the analysis of large-scale neuroimaging studies. This work describes a novel method for brain structure segmentation in magnetic resonance images that combines information about a structure's location and appearance. The spatial model is implemented by registering multiple atlas images to the target image and creating a spatial probability map. The structure's appearance is modeled by a classifier based on Gaussian scale-space features. These components are combined with a regularization term in a Bayesian framework that is globally optimized using graph cuts. The incorporation of the appearance model enables the method to segment structures with complex intensity distributions and increases its robustness against errors in the spatial model. The method is tested in cross-validation experiments on two datasets acquired with different magnetic resonance sequences, in which the hippocampus and cerebellum were segmented by an expert. Furthermore, the method is compared to two other segmentation techniques that were applied to the same data. Results show that the atlas- and appearance-based method produces accurate results with mean Dice similarity indices of 0.95 for the cerebellum, and 0.87 for the hippocampus. This was comparable to or better than the other methods, whereas the proposed technique is more widely applicable and robust.     

Surface-based labeling of cortical anatomy using a deformable atlas

The authors describe a computerized method to automatically find and label the cortical surface in three-dimensional (3-D) magnetic resonance (MR) brain images. The approach the authors take is to model a prelabeled brain atlas as a physical object and give it elastic properties, allowing it to warp itself onto regions in a preprocessed image. Preprocessing consists of boundary-finding and a morphological procedure which automatically extracts the brain and sulci from an MR image and provides a smoothed representation of the brain surface to which the deformable model can rapidly converge. The authors' deformable models are energy-minimizing elastic surfaces that can accurately locate image features. The models are parameterized with 3-D bicubic B-spline surfaces. The authors design the energy function such that cortical fissure (sulci) points on the model are attracted to fissure points on the image and the remaining model points are attracted to the brain surface. A conjugate gradient method minimizes the energy function, allowing the model to automatically converge to the smoothed brain surface. Finally, labels are propagated from the deformed atlas onto the high-resolution brain surface     

Automatic computation of histogram threshold for lip segmentation using feedback of shape information

Threshold-based segmentation methods provide a simple and efficient way to implement lip segmentation. However, automatic computation of robust thresholds presents a major challenge. This research proposes an adaptive method for selecting the histogram threshold, based on feedback of shape information. The proposed method reduces unnecessary overhead by first comparing the initial segmentation to a reference lip shape model to decide if optimisation is required. In cases where optimisation is required, the algorithm adjusts the threshold until the segmentation is sufficiently similar to a reference shape model. The algorithm is tested on the AR Face Database by comparing the segmentation accuracy before and after optimisation. The proposed method increases the number of segmentations classified as ‘good’ (overlap above 90 %) by 7.1 % absolute, and significantly improves the segmentation in challenging cases containing facial hair.     

Automatic objects segmentation with RGB-D cameras

Automatic object segmentation is a fundamentally difficult problem due to issues such as shadow, lighting, and semantic gaps. Edges play a critical role in object segmentation; however, it is almost impossible for the computer to know which edges correspond to object boundaries and which are caused by internal texture discontinuities. Active 3-D cameras, which provide streams of depth and RGB frames, are poised to become inexpensive and widespread. The depth discontinuities provide useful information for identifying object boundaries, which makes automatic object segmentation possible. However, the depth frames are extremely noisy. Also, the depth and RGB information often lose synchronization when the object is moving fast, due to different response time of the RGB and depth sensors. We show how to use the combined depth and RGB information to mitigate these problems and produce an accurate silhouette of the object. On a large dataset (24 objects with 1500 images), we provide both qualitative and quantitative evidences that our proposed techniques are effective.