Constrained Gaussian mixture model framework for automatic segmentation of MR brain images

An automated algorithm for tissue segmentation of noisy, low-contrast magnetic resonance (MR) images of the brain is presented. A mixture model composed of a large number of Gaussians is used to represent the brain image. Each tissue is represented by a large number of Gaussian components to capture the complex tissue spatial layout. The intensity of a tissue is considered a global feature and is incorporated into the model through tying of all the related Gaussian parameters. The expectation-maximization (EM) algorithm is utilized to learn the parameter-tied, constrained Gaussian mixture model. An elaborate initialization scheme is suggested to link the set of Gaussians per tissue type, such that each Gaussian in the set has similar intensity characteristics with minimal overlapping spatial supports. Segmentation of the brain image is achieved by the affiliation of each voxel to the component of the model that maximized the a posteriori probability. The presented algorithm is used to segment three-dimensional, T1-weighted, simulated and real MR images of the brain into three different tissues, under varying noise conditions. Results are compared with state-of-the-art algorithms in the literature. The algorithm does not use an atlas for initialization or parameter learning. Registration processes are therefore not required and the applicability of the framework can be extended to diseased brains and neonatal brains.     

Markov random field segmentation of brain MR images

Describes a fully-automatic three-dimensional (3-D)-segmentation technique for brain magnetic resonance (MR) images. By means of Markov random fields (MRF's) the segmentation algorithm captures three features that are of special importance for MR images, i.e., nonparametric distributions of tissue intensities, neighborhood correlations, and signal inhomogeneities. Detailed simulations and real MR images demonstrate the performance of the segmentation algorithm. In particular, the impact of noise, inhomogeneity, smoothing, and structure thickness are analyzed quantitatively. Even single-echo MR images are well classified into gray matter, white matter, cerebrospinal fluid, scalp-bone, and background. A simulated annealing and an iterated conditional modes implementation are presented     

A unifying framework for partial volume segmentation of brain MR images

Accurate brain tissue segmentation by intensity-based voxel classification of magnetic resonance (MR) images is complicated by partial volume (PV) voxels that contain a mixture of two or more tissue types. In this paper, we present a statistical framework for PV segmentation that encompasses and extends existing techniques. We start from a commonly used parametric statistical image model in which each voxel belongs to one single tissue type, and introduce an additional downsampling step that causes partial voluming along the borders between tissues. An expectation-maximization approach is used to simultaneously estimate the parameters of the resulting model and perform a PV classification. We present results on well-chosen simulated images and on real MR images of the brain, and demonstrate that the use of appropriate spatial prior knowledge not only improves the classifications, but is often indispensable for robust parameter estimation as well. We conclude that general robust PV segmentation of MR brain images requires statistical models that describe the spatial distribution of brain tissues more accurately than currently available models.     

Automatic segmentation of the caudate nucleus from human brain MR images

We describe a knowledge-driven algorithm to automatically delineate the caudate nucleus (CN) region of the human brain from a magnetic resonance (MR) image. Since the lateral ventricles (LVs) are good landmarks for positioning the CN, the algorithm first extracts the LVs, and automatically localizes the CN from this information guided by anatomic knowledge of the structure. The face validity of the algorithm was tested with 55 high-resolution T1-weighted magnetic resonance imaging (MRI) datasets, and segmentation results were overlaid onto the original image data for visual inspection. We further evaluated the algorithm by comparing automated segmentation results to a "gold standard" established by human experts for these 55 MR datasets. Quantitative comparison showed a high intraclass correlation between the algorithm and expert as well as high spatial overlap between the regions-of-interest (ROIs) generated from the two methods. The mean spatial overlap +/- standard deviation (defined by the intersection of the 2 ROIs divided by the union of the 2 ROIs) was equal to 0.873 +/- 0.0234. The algorithm has been incorporated into a public domain software program written in Java and, thus, has the potential to be of broad benefit to neuroimaging investigators interested in basal ganglia anatomy and function.     

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.     

Rule-based automatic segmentation of color images

A rule-based segmentation algorithm for color images has been presented in this paper. The proposed strategy is similar to region growing algorithm where the seed points are automatically selected and grown. The similarity percents of neighboring pixels are calculated by means of fuzzy reasoning rules, and the merging of the pixels with regions is performed by comparing the similarity percent with the similarity threshold value. The algorithm does not require any prior knowledge of the number of regions existing in the image and decreases the computational load required for the fuzzy c-means (FCM). Several computer simulations have been performed and the results have been discussed. The simulation results indicate that the proposed algorithm yields segmented color image of perfect accuracy.     

Adaptive model initialization and deformation for automatic segmentation of T1-weighted brain MRI data

A fully automatic, two-step, T1-weighted brain magnetic resonance imaging (MRI) segmentation method is presented. A preliminary mask of parenchyma is first estimated through adaptive image intensity analysis and mathematical morphological operations. It serves as the initial model and probability reference for a level-set algorithm in the second step, which finalizes the segmentation based on both image intensity and geometric information. The Dice coefficient and Euclidean distance between boundaries of automatic results and the corresponding references are reported for both phantom and clinical MR data. For the 28 patient scans acquired at our institution, the average Dice coefficient was 98.2% and the mean Euclidean surface distance measure was 0.074 mm. The entire segmentation for either a simulated or a clinical image volume finishes within 2 min on a modern PC system. The accuracy and speed of this technique allow us to automatically create patient-specific finite element models within the operating room on a timely basis for application in image-guided updating of preoperative scans.     

Tumor-Cut: segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications

In this paper, we present a fast and robust practical tool for segmentation of solid tumors with minimal user interaction to assist clinicians and researchers in radiosurgery planning and assessment of the response to the therapy. Particularly, a cellular automata (CA) based seeded tumor segmentation method on contrast enhanced T1 weighted magnetic resonance (MR) images, which standardizes the volume of interest (VOI) and seed selection, is proposed. First, we establish the connection of the CA-based segmentation to the graph-theoretic methods to show that the iterative CA framework solves the shortest path problem. In that regard, we modify the state transition function of the CA to calculate the exact shortest path solution. Furthermore, a sensitivity parameter is introduced to adapt to the heterogeneous tumor segmentation problem, and an implicit level set surface is evolved on a tumor probability map constructed from CA states to impose spatial smoothness. Sufficient information to initialize the algorithm is gathered from the user simply by a line drawn on the maximum diameter of the tumor, in line with the clinical practice. Furthermore, an algorithm based on CA is presented to differentiate necrotic and enhancing tumor tissue content, which gains importance for a detailed assessment of radiation therapy response. Validation studies on both clinical and synthetic brain tumor datasets demonstrate 80%-90% overlap performance of the proposed algorithm with an emphasis on less sensitivity to seed initialization, robustness with respect to different and heterogeneous tumor types, and its efficiency in terms of computation time.     

Atlas-based segmentation of pathological MR brain images using a model of lesion growth

We propose a method for brain atlas deformation in the presence of large space-occupying tumors, based on an a priori model of lesion growth that assumes radial expansion of the lesion from its starting point. Our approach involves three steps. First, an affine registration brings the atlas and the patient into global correspondence. Then, the seeding of a synthetic tumor into the brain atlas provides a template for the lesion. The last step is the deformation of the seeded atlas, combining a method derived from optical flow principles and a model of lesion growth. Results show that a good registration is performed and that the method can be applied to automatic segmentation of structures and substructures in brains with gross deformation, with important medical applications in neurosurgery, radiosurgery, and radiotherapy.     

Quantification and segmentation of brain tissues from MR images: aprobabilistic neural network approach

This paper presents a probabilistic neural network based technique for unsupervised quantification and segmentation of brain tissues from magnetic resonance images. It is shown that this problem can be solved by distribution learning and relaxation labeling, resulting in an efficient method that may be particularly useful in quantifying and segmenting abnormal brain tissues where the number of tissue types is unknown and the distributions of tissue types heavily overlap. The new technique uses suitable statistical models for both the pixel and context images and formulates the problem in terms of model-histogram fitting and global consistency labeling. The quantification is achieved by probabilistic self-organizing mixtures and the segmentation by a probabilistic constraint relaxation network. The experimental results show the efficient and robust performance of the new algorithm and that it outperforms the conventional classification based approaches     

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.     

Fully automatic segmentation of the brain in MRI

A robust fully automatic method for segmenting the brain from head magnetic resonance (MR) images has been developed, which works even in the presence of radio frequency (RF) inhomogeneities. It has been successful in segmenting the brain in every slice from head images acquired from several different MRI scanners, using different-resolution images and different echo sequences. The method uses an integrated approach which employs image processing techniques based on anisotropic filters and “snakes” contouring techniques, and a priori knowledge, which is used to remove the eyes, which are tricky to remove based on image intensity alone. It is a multistage process, involving first removal of the background noise leaving a head mask, then finding a rough outline of the brain, then refinement of the rough brain outline to a final mask. The paper describes the main features of the method, and gives results for some brain studies     

基于简化PCNN模型的结构光图像自动分割方法

针对CCD获取的结构光图像因大尺寸、光照不均匀,一般分割方法容易产生过分割或欠分割,提出了一种简化的脉冲耦合神经网络(PCNN)分割方法。将结构光图像进行分块,降低光照对分割质量的影响。每块子图像采用改进的PCNN模型自动进行分割。PCNN采用线性方式动态调整脉冲门限,以最小交叉熵确定其迭代次数,并利用邻域像素间的关系自动调整连接系数,减少人工干预。通过主客观评价指标对分割结果进行了比较,结果表明,提出的算法可以有效地分割出结构光图像中的条纹及点阵模式,目标边缘光滑、连贯和清晰,可以用于结构光图像的分割处理。     

An automatic low-level segmentation procedure for remote sensing images

This paper describes a procedure which allows for the segmentation of a broad variety of aerial views with a minimum of parameter adjustments at each one of its processing steps, i.e., grey value agglomeration by means of adaptive rank order filtering and multi-level-thresholding. Each step is illustrated and the trade-off between accuracy, computational complexity and elasticity is discussed. Experimental results with remote sensing imagery of agricultural areas are shown.     

Precise segmentation of the lateral ventricles and caudate nucleusin MR brain images using anatomically driven histograms

This paper demonstrates a time-saving, automated method that helps to segment the lateral ventricles and caudate nucleus in T1-weighted coronal magnetic resonance (MR) brain images of normal control subjects. The method involves choosing intensity thresholds by using anatomical information and by locating peaks in histograms. To validate the method, the lateral ventricles and caudate nucleus were segmented in three brain scans by four experts, first using an established method involving isointensity contours and manual editing, and second using automatically generated intensity thresholds as an aid to the established method. The results demonstrate both time savings and increased reliability     

Automated segmentation of human brain MR images aided by fuzzy information granulation and fuzzy inference

This paper proposes an automated procedure for segmenting an magnetic resonance (MR) image of a human brain based on fuzzy logic. An MR volumetric image composed of many slice images consists of several parts: gray matter, white matter, cerebrospinal fluid, and others. Generally, the histogram shapes of MR volumetric images are different from person to person. Fuzzy information granulation of the histograms can lead to a series of histogram peaks. The intensity thresholds for segmenting the whole brain of a subject are automatically determined by finding the peaks of the intensity histogram obtained from the MR images. After these thresholds are evaluated by a procedure called region growing, the whole brain can be identified. A segmentation experiment was done on 50 human brain MR volumes. A statistical analysis showed that the automated segmented volumes were similar to the volumes manually segmented by a physician. Next, we describe a procedure for decomposing the obtained whole brain into the left and right cerebral hemispheres, the cerebellum and the brain stem. Fuzzy if-then rules can represent information on the anatomical locations, segmentation boundaries as well as intensities. Evaluation of the inferred result using the region growing method can then lead to the decomposition of the whole brain. We applied this method to 44 MR volumes. The decomposed portions were statistically compared with those manually decomposed by a physician. Consequently, our method can identify the whole brain, the left cerebral hemisphere, the right cerebral hemisphere, the cerebellum and the brain stem with high accuracy and therefore can provide the three dimensional shapes of these regions.     

3-D segmentation of MR images of the head for 3-D display

Algorithms for 3-D segmentation and reconstruction of anatomical surfaces from magnetic resonance imaging (MRI) data are presented. The 3-D extension of the Marr-Hildreth operator is described, and it is shown that its zero crossings are related to anatomical surfaces. For an improved surface definition, morphological filters-dilation and erosion-are applied. From these contours, 3-D reconstructions of skin, bone, brain, and the ventricular system can be generated. Results obtained with different segmentation parameters and surface rendering methods are presented. The fidelity of the generated images comes close to anatomical reality. It is noted that both the convolution and the morphological filtering are computationally expensive, and thus take a long time on a general-purpose computer. Another problem is assigning labels to the constituents of the head; in the current implementation, this is done interactively.     

Knowledge-based interpretation of MR brain images

The authors have developed a method for fully automated segmentation and labeling of 17 neuroanatomic structures such as thalamus, caudate nucleus, ventricular system, etc. in magnetic resonance (MR) brain images. The authors' method is based on a hypothesize-and-verify principle and uses a genetic algorithm (GA) optimization technique to generate and evaluate image interpretation hypotheses in a feedback loop. The authors' method was trained in 20 individual T1-weighted MR images. Observer-defined contours of neuroanatomic structures were used as a priori knowledge. The method's performance was validated in eight MR images by comparison to observer-defined independent standards. The GA-based image interpretation method correctly interpreted neuroanatomic structures in all images from the test set. Computer-identified and observer-defined neuroanatomic structure areas correlated very well (r=0.99, y=0,95x-2.1). Border positioning errors were small, with a root mean square (rms) border positioning error of 1.5+/-0.6 pixels. The authors' GA-based image interpretation method represents a novel approach to image interpretation and has been shown to produce accurate labeling of neuroanatomic structures in a set of MR brain images.