一种鲁棒的人脑组织核磁共振图像分割算法研究

自动的人脑核磁共振(MR)图像分割是许多医学图像应用的关键问题.该文提出了一种有效的自动脑核磁共振图像的分割方法框架体系,脑MR分割框架体系由3个处理步骤构成.首先,采用基于水平集的方法将MR图像中非脑组织剔除,从脑图像中提取大脑组织结构.然后,对MR脑结构图像进行灰度不均匀性校正.最后,该算法采用最大后验分类器可以将人脑组织分为脑白质、脑灰质、脑髓液.在实验中对大量的MR脑图像数据应用该分割算法.实验结果充分证明该方法的有效性.这种分割算法适用于人脑核磁图像分析的各种实际临床应用.     

基于softmax回归与图割法的脑肿瘤分割算法

从医学图像中分割脑肿瘤区域可以为脑肿瘤的诊断以及放射治疗提供帮助.但肿瘤区域的变化异常且边界非常模糊,因此自动或半自动地分割脑肿瘤非常困难.针对这一问题,本文结合softmax回归和图割法提出一种脑肿瘤分割算法.首先融合多序列核磁共振图像(MRI)并标记训练样本,再用softmax回归训练模型参数并计算每个点属于各个类别的概率,最后将概率融入到图割法中,用最小切/最大流方法得到最终分割结果.实验表明提出的方法可以更好地得到脑肿瘤的边界,并能较准确地分割出脑肿瘤区域.     

Simultaneous vector-valued image segmentation and intensity nonuniformity correction using variational level set combined with Markov random field modeling

Object quantification requires an image segmentation to make measurements about size, material composition and morphology of the object. In vector-valued or multispectral images, each image channel has its signal characteristics and provides special information that may improve the results of image segmentation method. This paper presents a region-based active contour model for vector-valued image segmentation with a variational level set formulation. In this model, the local image intensities are characterized using Gaussian distributions with different means and variances. Furthermore, by utilizing Markov random field, the spatial correlation between neighboring pixels and voxels is modeled. With incorporation of intensity nonuniformity model, our method is able to deal with brain tissue segmentation from multispectral magnetic resonance (MR) images. Our experiments on synthetic images and multispectral cerebral MR images with different noise and bias level show the advantages of the proposed method.     

Variational Bayes inference of spatial mixture models for segmentation

Mixture models are commonly used in the statistical segmentation of images. For example, they can be used for the segmentation of structural medical images into different matter types, or of statistical parametric maps into activating and nonactivating brain regions in functional imaging. Spatial mixture models have been developed to augment histogram information with spatial regularization using Markov random fields (MRFs). In previous work, an approximate model was developed to allow adaptive determination of the parameter controlling the strength of spatial regularization. Inference was performed using Markov Chain Monte Carlo (MCMC) sampling. However, this approach is prohibitively slow for large datasets. In this work, a more efficient inference approach is presented. This combines a variational Bayes approximation with a second-order Taylor expansion of the components of the posterior distribution, which would otherwise be intractable to Variational Bayes. This provides inference on fully adaptive spatial mixture models an order of magnitude faster than MCMC. We examine the behavior of this approach when applied to artificial data with different spatial characteristics, and to functional magnetic resonance imaging statistical parametric maps.     

基于随机游走策略改进的降雪模型图像分割方法

为了提高图像分割的准确度,尽可能降低分割边缘噪声对图像分割的影响,提出了一种基于降雪模型的图像分割方法,先对降雪模型及积雪表面效应做了详细分析,得出降雪模型运用于图像分割具有较强的适应性,接着在传统的随机游走图像分割算法中加入了自适应降雪模型的特性,生成新的算法,最后运用虚拟图像和真实图像进行算法性能实例仿真,结果表明,该算法的图像分割性能优于常见的NCut和传统随机游走图像分割算法,具有一定的研究价值。     

基于多尺度特征提取的MRI脑肿瘤图像分割研究

针对磁共振成像(magnetic resonance imaging, MRI)脑部肿瘤区域误识别及肿瘤形状 差异较大的问题,提出一种基于多尺度特征提取的 MRI 脑肿瘤图像分割方法。分割模型以 U-Net 为骨干网络,使用密集金字塔卷积(dense pyramidal convolution, DPC)提取多尺度特征, 以适应不同尺寸肿瘤的分割,同时引入条状池化(strip pooling, SP),凭借其能捕获肿瘤中远 距离区域的依赖关系,进一步加强对肿瘤图像的分割能力。提出的方法在 Kaggle_3m 数据 集上进行了实验验证,实验结果表明该方法具有良好的脑部肿瘤分割性能, 其中Dice相似 系数、杰卡德系数分别达到了91.66%,84.38% 。     

Improving the accuracy of volumetric segmentation using pre-processing boundary detection and image reconstruction

The concentration edge -detection and Gegenbauer image-reconstruction methods were previously shown to improve the quality of segmentation in magnetic resonance imaging. In this study, these methods are utilized as a pre-processing step to the Weibull E-SD field segmentation. It is demonstrated that the combination of the concentration edge detection and Gegenbauer reconstruction method improves the accuracy of segmentation for the simulated test data and real magnetic resonance images used in this study.     

MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization.

Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of magnetic resonance (MR) images. Unfortunately, MR images always contain a significant amount of noise caused by operator performance, equipment, and the environment, which can lead to serious inaccuracies with segmentation. A robust segmentation technique based on an extension to the traditional fuzzy c-means (FCM) clustering algorithm is proposed in this paper. A neighborhood attraction, which is dependent on the relative location and features of neighboring pixels, is shown to improve the segmentation performance dramatically. The degree of attraction is optimized by a neural-network model. Simulated and real brain MR images with different noise levels are segmented to demonstrate the superiority of the proposed technique compared to other FCM-based methods. This segmentation method is a key component of an MR image-based classification system for brain tumors, currently being developed. Index Terms-Improved fuzzy c-means clustering (IFCM), magnetic resonance imaging (MRI), neighborhood attraction, segmentation.     

Region detection by minimizing intraclass variance with geometric constraints, global optimality, and efficient approximation

Efficient segmentation of globally optimal surfaces in volumetric images is a central problem in many medical image analysis applications. Intraclass variance has been successfully utilized for object segmentation, for instance, in the Chan-Vese model, especially for images without prominent edges. In this paper, we study the optimization problem of detecting a region (volume) between two coupled smooth surfaces by minimizing the intraclass variance using an efficient polynomial-time algorithm. Our algorithm is based on the shape probing technique in computational geometry and computes a sequence of minimum-cost closed sets in a derived parametric graph. The method has been validated on computer-synthetic volumetric images and in X-ray CT-scanned datasets of plexiglas tubes of known sizes. Its applicability to clinical data sets was also demonstrated. In all cases, the approach yielded highly accurate results. We believe that the developed technique is of interest on its own. We expect that it can shed some light on solving other important optimization problems arising in medical imaging. Furthermore, we report an approximation algorithm which runs much faster than the exact algorithm while yielding highly comparable segmentation accuracy.     

基于多参数配准模型的脑核磁影像分割算法

配准技术在基于多图谱的分割方法中能有效地将医学图谱的先验知识融入分割过程,再结合以高效的标记融合算法,最终实现精确地自动分割.针对图谱配准的较大误差及其对标记融合的重要影响,本文建立了一种新的概率图模型框架并以此提出了基于多参数配准模型的分割算法,将此方法与高效的标记融合算法相结合,可以提高目标图像中特定组织区域的分割精度,更使其在少量图谱分割的情形下具有重要应用.首先,使用多种配准参数对所有目标图像进行配准;然后,分别采用不同的算法对配准图像进行灰度融合和标记融合,实现训练图像的重构过程;最后,利用高效的标记融合算法对重构后的图像进行融合得到最终精确的分割结果.实验结果表明该方法均优于本文其他分割算法,能够有效提升脑部组织分割精度.     

Combining high-performance computing and networking for advanced3-D cardiac imaging

The paper deals with the integration of a powerful parallel computer based image analysis and visualization system for cardiology into a hospital information system. Further services are remote access to the hospital Web server through an Internet network. The visualization system includes dynamic three dimensional representation of two types of medical images (e.g., magnetic resonance and nuclear medicine) as well as two images in the same modality (e.g., basal versus stress images). A series of software tools for quantitative image analysis developed for supporting diagnosis of cardiac disease are also available, including automated image segmentation and quantitative time evaluation of left ventricular volumes and related indices during cardiac cycle, myocardial mass, and myocardial perfusion indices. The system has been tested both at a specialized cardiologic center and for remote consultation in diagnosis of cardiac disease by using anatomical and perfusion magnetic resonance images     

EM algorithm for image segmentation initialized by a tree structurescheme

In this correspondence, the objective is to segment vector images, which are modeled as multivariate finite mixtures. The underlying images are characterized by Markov random fields (MRFs), and the applied segmentation procedure is based on the expectation-maximization (EM) technique. We propose an initialization procedure that does not require any prior information and yet provides excellent initial estimates for the EM method. The performance of the overall segmentation is demonstrated by segmentation of simulated one-dimensional (1D) and multidimensional magnetic resonance (MR) brain images.     

Semi-supervised cardiac MRI image of the left ventricle segmentation algorithm based on contrastive learning

A semi-supervised convolutional neural network segmentation method of medical images based on contrastive learning is proposed. The cardiac magnetic resonance imaging (MRI) images to be segmented are preprocessed to obtain positive and negative samples by labels. The U-Net shrinks network is applied to extract features of the positive samples, negative samples, and input samples. In addition, an unbalanced contrastive loss function is proposed, which is weighted with the binary cross-entropy loss function to obtain the total loss function. The model is pre-trained with labeled samples, and unlabeled images are predicted by the pre-trained model to generate pseudo-labels. A pseudo-label post-processing algorithm for removing disconnected regions and hole filling of pseudo-labels is proposed to guide the training process of semi-supervised networks. The results on the Sunnybrook dataset show that the segmentation results of this model are better, with a higher dice coefficient, accuracy, and recall rate.     

A level set approach for shape-driven segmentation and tracking of the left ventricle

Knowledge-based segmentation has been explored significantly in medical imaging. Prior anatomical knowledge can be used to define constraints that can improve performance of segmentation algorithms to physically corrupted and incomplete data. In this paper, the objective is to introduce such knowledge-based constraints while preserving the ability of dealing with local deformations. Toward this end, we propose a variational level set framework that can account for global shape consistency as well as for local deformations. In order to improve performance, the problems of segmentation and tracking of the structure of interest are dealt with simultaneously by introducing the notion of time in the process and looking for a solution that satisfies that prior constraints while being consistent along consecutive frames. Promising experimental results in magnetic resonance and ultrasonic cardiac images demonstrate the potentials of our approach.     

Hierarchical statistical shape models of multiobject anatomical structures: application to brain MRI

The accurate segmentation of subcortical brain structures in magnetic resonance (MR) images is of crucial importance in the interdisciplinary field of medical imaging. Although statistical approaches such as active shape models (ASMs) have proven to be particularly useful in the modeling of multiobject shapes, they are inefficient when facing challenging problems. Based on the wavelet transform, the fully generic multiresolution framework presented in this paper allows us to decompose the interobject relationships into different levels of detail. The aim of this hierarchical decomposition is twofold: to efficiently characterize the relationships between objects and their particular localities. Experiments performed on an eight-object structure defined in axial cross sectional MR brain images show that the new hierarchical segmentation significantly improves the accuracy of the segmentation, and while it exhibits a remarkable robustness with respect to the size of the training set.     

Atlas renormalization for improved brain MR image segmentation across scanner platforms

Atlas-based approaches have demonstrated the ability to automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. Unfortunately, the accuracy of this type of method often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this paper, we improve the performance of an atlas-based whole brain segmentation method by introducing an intensity renormalization procedure that automatically adjusts the prior atlas intensity model to new input data. Validation using manually labeled test datasets has shown that the new procedure improves the segmentation accuracy (as measured by the Dice coefficient) by 10% or more for several structures including hippocampus, amygdala, caudate, and pallidum. The results verify that this new procedure reduces the sensitivity of the whole brain segmentation method to changes in scanner platforms and improves its accuracy and robustness, which can thus facilitate multicenter or multisite neuroanatomical imaging studies.     

A shape-based approach to the segmentation of medical imagery using level sets

We propose a shape-based approach to curve evolution for the segmentation of medical images containing known object types. In particular, motivated by the work of Leventon, Grimson, and Faugeras, we derive a parametric model for an implicit representation of the segmenting curve by applying principal component analysis to a collection of signed distance representations of the training data. The parameters of this representation are then manipulated to minimize an objective function for segmentation. The resulting algorithm is able to handle multidimensional data, can deal with topological changes of the curve, is robust to noise and initial contour placements, and is computationally efficient. At the same time, it avoids the need for point correspondences during the training phase of the algorithm. We demonstrate this technique by applying it to two medical applications; two-dimensional segmentation of cardiac magnetic resonance imaging (MRI) and three-dimensional segmentation of prostate MRI.     

Atlas-based fuzzy connectedness segmentation and intensity nonuniformity correction applied to brain MRI

A framework that combines atlas registration, fuzzy connectedness (FC) segmentation, and parametric bias field correction (PABIC) is proposed for the automatic segmentation of brain magnetic resonance imaging (MRI). First, the atlas is registered onto the MRI to initialize the following FC segmentation. Original techniques are proposed to estimate necessary initial parameters of FC segmentation. Further, the result of the FC segmentation is utilized to initialize a following PABIC algorithm. Finally, we re-apply the FC technique on the PABIC corrected MRI to get the final segmentation. Thus, we avoid expert human intervention and provide a fully automatic method for brain MRI segmentation. Experiments on both simulated and real MRI images demonstrate the validity of the method, as well as the limitation of the method. Being a fully automatic method, it is expected to find wide applications, such as three-dimensional visualization, radiation therapy planning, and medical database construction     

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.     

Cortex segmentation: a fast variational geometric approach

An automatic cortical gray matter segmentation from a three-dimensional (3-D) brain images [magnetic resonance (MR) or computed tomography] is a well known problem in medical image processing. In this paper, we first formulate it as a geometric variational problem for propagation of two coupled bounding surfaces. An efficient numerical scheme is then used to implement the geodesic active surface model. Experimental results of cortex segmentation on real 3-D MR data are provided.