A technique for single-channel MR brain tissue segmentation: Application to a pediatric sample

A segmentation method is presented for gray matter, white matter, and cerebrospinal fluid (CSF) in thinsliced single-channel brain magnetic resonance (MR) scans. The method is based on probabilistic modeling of intensity distributions and on a region growing technique. Interrater and intrarater reliabilities for the method were high, and comparison with phantom studies and hand-traced results from an experienced rater indicated good validity. The method was designed to account for spatially dependent image intensity inhomogeneities. Segmentation of MR brain scans of 105 (56 male and 49 female) healthy children and adolescents showed that although the total brain volume was stable over age 4–18, white matter increased and gray matter decreased significantly. There were no sex differences in total gray and white matter growth after correction for total brain volume. White matter volume increased the most in superior and posterior regions and laterality effects were seen in hemisphere tissue volumes. These findings are consistent with other reports, and further validate the segmentation technique.     

Multispectral MR images segmentation based on fuzzy knowledge and modified seeded region growing

Magnetic resonance imaging (MRI) is a valuable diagnostic tool in medical science due to its capability for soft-tissue characterization and three-dimensional visualization. One potential application of MRI in clinical practice is brain parenchyma classification and segmentation. Based on fuzzy knowledge and modified seeded region growing, this work proposes a novel image segmentation method, called Fuzzy Knowledge-Based Seeded Region Growing (FKSRG), for multispectral MR images. In this work, fuzzy knowledge includes the fuzzy edge, fuzzy similarity and fuzzy distance, which are obtained from relationships between pixels in multispectral MR images and are applied to the modified seeded regions growing process. In conventional regions merging, the final number of regions is unknown. Therefore, a Target Generation Process is proposed and applied to support conventional regions merging, such that the FKSRG method does not over- or undersegment images. Finally, two image sets, namely, computer-generated phantom images and real MR images, are used in experiments to assess the effectiveness of the proposed FKSRG method. Experimental results demonstrate that the FKSRG method segments multispectral MR images much more effectively than the Functional MRI of the Brain Automated Segmentation Tool, K-means and Support Vector Machine methods.     

Perfusion MR imaging and proton MR spectroscopic imaging in differentiating necrotizing cerebritis from glioblastoma multiforme

We describe a lesion with the magnetic resonance imaging (MRI) characteristics of a glioblastoma mutiforme and demonstrate how perfusion MRI and proton MR spectroscopic imaging can be used to differentiate necrotizing cerebritis from what appeared to be a high-grade glioma. A 43-year-old woman presented to her physician complaining of progressive visual disturbance and headache for several weeks. Conventional MRI demonstrated a parietal peripherally enhancing mass with central necrosis and moderate to severe surrounding T2 hyperintensity, suggesting an infiltrating high-grade glioma. However, advanced imaging, including dynamic susceptibility contrast MRI (DSC MRI) and magnetic resonance spectroscopic imaging (MRSI), suggested a nonneoplastic lesion. The DSC MRI data demonstrated no hyperperfusion within the lesion and surrounding T2 signal abnormality, and the MRSI data showed overall decrease in metabolites in this region, except for lactate. Because of the aggressive appearance to the lesion and the patients' worsening symptoms, a biopsy was performed. The pathologic diagnosis was necrotizing cerebritis. After the commencement of steroid therapy, imaging findings and patient symptoms improved. This report will review the utility of advanced imaging for differentiating inflammatory from neoplastic appearing lesions on conventional imaging.     

Neural network-based segmentation of dynamic MR mammographic images

The usefulness of neural networks for the classification of signal-time curves from dynamic MR mammography was recently demonstrated by our group. The multi-layer perceptron under study consists of 28 input, 4 hidden, and 3 output nodes, and was trained to classify signal-time curves into three tissue classes: "carcinoma," "benign lesion," and "parenchyma." Extending this approach, it was the aim of the present study to evaluate the performance of the developed network in the segmentation of dynamic MR mammographic images in comparison to a pixel-by-pixel two-compartment pharmacokinetic analysis. The population investigated in this pilot study comprised 15 women with suspicious lesions in the breast, which were confirmed histologically after the MR examination. The neural network classified the same areas as malignant as those which were marked as being highly suspicious by the pharmacokinetic mapping approach but with the advantage that no a priori knowledge on tissue microcirculation was needed, that computation proved to be much faster, and that it yielded a unique classification into just three tissue classes.     

Optimization of tissue segmentation of brain MR images based on multispectral 3D feature maps

The purpose of this work was to optimize and increase the accuracy of tissue segmentation of the brain magnetic resonance (MR) images based on multispectral 3D feature maps. We used three sets of MR images as input to the in-house developed semi-automated 3D tissue segmentation algorithm: proton density (PD) and T2-weighted fast spin echo and, T1-weighted spin echo. First, to eliminate the random noise, non-linear anisotropic diffusion type filtering was applied to all the images. Second, to reduce the nonuniformity of the images, we devised and applied a correction algorithm based on uniform phantoms. Following these steps, the qualified observer "seeded" (identified training points) the tissue of interest. To reduce the operator dependent errors, cluster optimization was also used; this clustering algorithm identifies the densest clusters pertaining to the tissues. Finally, the images were segmented using k-NN (k-Nearest Neighborhood) algorithm and a stack of color-coded segmented images were created along with the connectivity algorithm to generate the entire surface of the brain. The application of pre-processing optimization steps substantially improved the 3D tissue segmentation methodology.     

Automatic segmentation for brain MR images via a convex optimized segmentation and bias field correction coupled model

Accurate segmentation of magnetic resonance (MR) images remains challenging mainly due to the intensity inhomogeneity, which is also commonly known as bias field. Recently active contour models with geometric information constraint have been applied, however, most of them deal with the bias field by using a necessary pre-processing step before segmentation of MR data. This paper presents a novel automatic variational method, which can segment brain MR images meanwhile correcting the bias field when segmenting images with high intensity inhomogeneities. We first define a function for clustering the image pixels in a smaller neighborhood. The cluster centers in this objective function have a multiplicative factor that estimates the bias within the neighborhood. In order to reduce the effect of the noise, the local intensity variations are described by the Gaussian distributions with different means and variances. Then, the objective functions are integrated over the entire domain. In order to obtain the global optimal and make the results independent of the initialization of the algorithm, we reconstructed the energy function to be convex and calculated it by using the Split Bregman theory. A salient advantage of our method is that its result is independent of initialization, which allows robust and fully automated application. Our method is able to estimate the bias of quite general profiles, even in 7T MR images. Moreover, our model can also distinguish regions with similar intensity distribution with different variances. The proposed method has been rigorously validated with images acquired on variety of imaging modalities with promising results.     

A modified fuzzy clustering algorithm for operator independent brain tissue classification of dual echo MR images.

Methods for brain tissue classification or segmentation of structural magnetic resonance imaging (MRI) data should ideally be independent of human operators for reasons of reliability and tractability. An algorithm is described for fully automated segmentation of dual echo, fast spin-echo MRI data. The method is used to assign fuzzy-membership values for each of four tissue classes (gray matter, white matter, cerebrospinal fluid and dura) to each voxel based on partition of a two dimensional feature space. Fuzzy clustering is modified for this application in two ways. First, a two component normal mixture model is initially fitted to the thresholded feature space to identify exemplary gray and white matter voxels. These exemplary data protect subsequently estimated cluster means against the tendency of unmodified fuzzy clustering to equalize the number of voxels in each class. Second, fuzzy clustering is implemented in a moving window scheme that accommodates reduced image contrast at the axial extremes of the transmitting/receiving coil. MRI data acquired from 5 normal volunteers were used to identify stable values for three arbitrary parameters of the algorithm: feature space threshold, relative weight of exemplary gray and white matter voxels, and moving window size. The modified algorithm incorporating these parameter values was then used to classify data from simulated images of the brain, validating the use of fuzzy-membership values as estimates of partial volume. Gray:white matter ratios were estimated from 20 twenty normal volunteers (mean age 32.8 years). Processing time for each three-dimensional image was approximately 30 min on a 170 MHz workstation. Mean cerebral gray and white matter volumes estimated from these automatically segmented images were very similar to comparable results previously obtained by operator dependent methods, but without their inherent unreliability.     

Subtraction technique for contrast-enhanced MR images of musculoskeletal tumors

Vascularized malignant tissue, fat and hemorrhage may have similar intensities on Gd-DTPA-enhanced, T1-weighted MRI performed to evaluate musculoskeletal tumors. We describe a simple, rapid post-processing subtraction technique which resulted in improved definition of these tissues in 33 of 42 examinations. While the subtraction process is susceptible to complex patient motion, the improved contrast can be obtained without modifying standard pulse sequences.     

Three-dimensional segmentation of anatomical structures in MR images on large data bases

In this paper an image-based method founded on mathematical morphology is presented in order to facilitate the segmentation of cerebral structures over large data bases of 3D magnetic resonance images (MRIs). The segmentation is described as an immersion simulation, applied to the modified gradient image, modeled by a generated 3D-region adjacency graph (RAG). The segmentation relies on two main processes: homotopy modification and contour decision. The first one is achieved by a marker extraction stage where homogeneous 3D-regions are identified. This stage uses contrasted regions from morphological reconstruction and labeled flat regions constrained by the RAG. Then, the decision stage intends to precisely locate the contours of regions detected by the marker extraction. This decision is performed by a 3D extension of the watershed transform. The method has been applied on a data base of 3D brain MRIs composed of fifty patients. Results are illustrated by segmenting the ventricles, corpus callosum, cerebellum, hippocampus, pons, medulla and midbrain on our data base and the approach is validated on two phantom 3D MRIs.     

The effects of changing water content,relaxation times,and tissue contrast on tissue segmentation and measures of cortical anatomy in MR images

Water content is the dominant chemical compound in the brain and it is the primary determinant of tissue contrast in magnetic resonance (MR) images. Water content varies greatly between individuals, and it changes dramatically over time from birth through senescence of the human life span. We hypothesize that the effects that individual- and age-related variations in water content have on contrast of the brain in MR images also have important, systematic effects on in vivo, MRI-based measures of regional brain volumes. We also hypothesize that changes in water content and tissue contrast across time may account for age-related changes in regional volumes, and that differences in water content or tissue contrast across differing neuropsychiatric diagnoses may account for differences in regional volumes across diagnostic groups.     

Segmentation techniques for tissue differentiation in MRI of ophthalmology using fuzzy clustering algorithms

This paper presents MRI segmentation techniques to differentiate abnormal and normal tissues in Ophthalmology using fuzzy clustering algorithms. Applying the best-known fuzzy c-means (FCM) clustering algorithm, a newly proposed algorithm, called an alternative fuzzy c-mean (AFCM), was used for MRI segmentation in Ophthalmology. These unsupervised segmentation algorithms can help Ophthalmologists to reduce the medical imaging noise effects originating from low resolution sensors and/or the structures that move during the data acquisition. They may be particularly helpful in the clinical oncological field as an aid to the diagnosis of Retinoblastoma, an inborn oncological disease in which symptoms usually show in early childhood. For the purpose of early treatment with radiotherapy and surgery, the newly proposed AFCM is preferred to provide more information for medical images used by Ophthalmologists. Comparisons between FCM and AFCM segmentations are made. Both fuzzy clustering segmentation techniques provide useful information and good results. However, the AFCM method has better detection of abnormal tissues than FCM according to a window selection. Overall, the newly proposed AFCM segmentation technique is recommended in MRI segmentation.     

Accurate segmentation of leukocyte in blood cell images using Atanassov's intuitionistic fuzzy and interval Type II fuzzy set theory

In this paper automatic leukocyte segmentation in pathological blood cell images is proposed using intuitionistic fuzzy and interval Type II fuzzy set theory. This is done to count different types of leukocytes for disease detection. Also, the segmentation should be accurate so that the shape of the leukocytes is preserved. So, intuitionistic fuzzy set and interval Type II fuzzy set that consider either more number of uncertainties or a different type of uncertainty as compared to fuzzy set theory are used in this work. As the images are considered fuzzy due to imprecise gray levels, advanced fuzzy set theories may be expected to give better result. A modified Cauchy distribution is used to find the membership function. In intuitionistic fuzzy method, non-membership values are obtained using Yager's intuitionistic fuzzy generator. Optimal threshold is obtained by minimizing intuitionistic fuzzy divergence. In interval type II fuzzy set, a new membership function is generated that takes into account the two levels in Type II fuzzy set using probabilistic T co norm. Optimal threshold is selected by minimizing a proposed Type II fuzzy divergence. Though fuzzy techniques were applied earlier but these methods failed to threshold multiple leukocytes in images. Experimental results show that both interval Type II fuzzy and intuitionistic fuzzy methods perform better than the existing non-fuzzy/fuzzy methods but interval Type II fuzzy thresholding method performs little bit better than intuitionistic fuzzy method. Segmented leukocytes in the proposed interval Type II fuzzy method are observed to be distinct and clear.     

Development of new population-averaged standard templates for spatial normalization and segmentation of MR images for postnatal piglet brains

Propose

To design a set of brain templates for postnatal piglet brains based on high-resolution T1-weighted imaging for voxel-based morphometric analysis.

Materials and methods

Using a 3.0 T magnetic resonance (MR) scanner, a population-based whole brain template was developed by averaging forty T1 images in the brains of postnatal piglets at 38 days of age. The templates for gray and white matter, and cerebrospinal fluid were designed based on the corresponding probability maps by adapting individual data sets using statistical parametric mapping. Anatomical labeling maps were generated from labeling propagation derived from the established Pig Brain Atlas. Differences in the coordinates from four significant structural landmarks in the template, plus an additional 12 normalized images and anatomical labeling maps were measured to validate the accuracy of the registration of the template.

Results

A whole brain template, a set of tissue-specific probability and anatomical labeling maps were developed. The location deviation of the four significant structural landmarks, including the anterior and posterior regions in the corpus callosum, and the left and right caudate nucleus, was found to be < 0.25 cm, validating the sensitivity and resolution of the template.

Conclusion

A whole brain template map and a set of tissue-specific probability and anatomical labeling maps were developed to analyze the morphometric imaging of the postnatal piglet brain, an animal model of the human infant.     

A fully automatic three-step liver segmentation method on LDA-based probability maps for multiple contrast MR images

Automatic 3D liver segmentation in magnetic resonance (MR) data sets has proven to be a very challenging task in the domain of medical image analysis. There exist numerous approaches for automatic 3D liver segmentation on computer tomography data sets that have influenced the segmentation of MR images. In contrast to previous approaches to liver segmentation in MR data sets, we use all available MR channel information of different weightings and formulate liver tissue and position probabilities in a probabilistic framework. We apply multiclass linear discriminant analysis as a fast and efficient dimensionality reduction technique and generate probability maps then used for segmentation. We develop a fully automatic three-step 3D segmentation approach based upon a modified region growing approach and a further threshold technique. Finally, we incorporate characteristic prior knowledge to improve the segmentation results. This novel 3D segmentation approach is modularized and can be applied for normal and fat accumulated liver tissue properties.     

Novel active contour model based on multi-variate local Gaussian distribution for local segmentation of MR brain images

Optical Review - Active contour model (ACM) has been one of the most widely utilized methods in magnetic resonance (MR) brain image segmentation because of its ability of capturing topology...     

A 3D balanced-SSFP Dixon technique with group-encoded k-space segmentation for breath-held non-contrast-enhanced MR angiography

A three-dimensional balanced steady-state free precession (b-SSFP)-Dixon technique with a novel group-encoded k-space segmentation scheme called GUINNESS (Group-encoded Ungated Inversion Nulling for Non-contrast Enhancement in the Steady State) was developed. GUINNESS was evaluated for breath-held non-contrast-enhanced MR angiography of the renal arteries on 18 subjects (6 healthy volunteers, 12 patients) at 3.0 T. The method provided high signal-to-noise and contrast renal angiograms with homogeneous fat and background suppression in short breath-holds on the order of 20 s with high spatial resolution and coverage. GUINNESS has potential as a short breath-hold alternative to conventional respiratory-gated methods, which are often suboptimal in pediatric subjects and patients with significant diaphragmatic drift/sleep apnea.     

Automatic measurement of changes in brain volume on consecutive 3D MR images by segmentation propagation

This article presents a technique to automatically measure changes in the volume of a structure of interest in successive 3D magnetic resonance (MR) images and its application in the study of the brain and lateral cerebral ventricles. The only manual step is a segmentation of the structure of interest in the first image. The analysis comprises, first, precise rigid co-registration of the time series of images; second, computation of residual deformations between pairs of images; third, automatic quantification of the volume change, obtained by propagation of the segmentation of the structure of interest through the series of MR images. This approach has been applied to monitor changes in the volume of the brain and lateral cerebral ventricles in a healthy subject and a patient with primary progressive aphasia (PPA). Results are consistent with those obtained by application of the boundary shift integral (BSI) and by stereology in the same subjects.     

基于分段模糊c-均值的连续密度HMM语音识别模型参数估计

本文在分析了连续密度隐马尔可夫模型(CDHMM)的两种主要最大似然参数估计方法的基础上,引入模糊集思想,提出了分段模糊c-均值算法代替普通的分段k-均值算法进行CDHMM的最大似然参数估计。文中给出了其实现方法。实验结果证明其在语音识别中具有很好的性能。     

Acute renal failure: common occurrence of preservation of corticomedullary differentiation on MR images

The purpose of this study was to evaluate the relationship between renal corticomedullary differentiation (CMD) on MR imaging and serum creatinine (sCr) level in patients with acute renal failure (ARF). Twenty-one patients with ARF were retrospectively investigated. In all 21 patients, sCr levels were obtained on the same date as the MR study, and within 8 days before and after the MR study. CMD was assessed on non-contrast T(1)-weighted images and immediate post-gadolinium spoiled gradient echo (Gd-SGE) images. Presence of CMD was graded into 3 groups as 'preserved', 'intermediate', or 'loss'. On non-contrast T(1)-weighted images, 12/21 (57%) showed loss of CMD and 9/21 (43%) showed preserved CMD. On immediate Gd-SGE images, 5/21 (24%) showed loss of CMD, 12/21 (57%) preserved CMD, and 4/21 (19%) intermediate CMD. The sCr levels of 9 patients with preserved CMD on non-contrast T(1)-weighted images ranged from 1.4 to 10.5 mg/dl (mean 4.6 mg/dl), while those of 12 patients with loss of CMD ranged from 1.6 to 7.6 mg/dl (mean 4.8 mg/dl), which was not statistically significant (p > 0.2). Renal CMD can remain preserved on non-contrast T(1)-weighted or immediate Gd-SGE images in patients with acute presentation of ARF, independent of sCr level.     

Automated classification of multispectral MR images using unsupervised constrained energy minimization based on fuzzy logic

Constrained energy minimization (CEM) has proven highly effective for hyperspectral (or multispectral) target detection and classification. It requires a complete knowledge of the desired target signature in images. This work presents “Unsupervised CEM (UCEM),” a novel approach to automatically target detection and classification in multispectral magnetic resonance (MR) images. The UCEM involves two processes, namely, target generation process (TGP) and CEM. The TGP is a fuzzy-set process that generates a set of potential targets from unknown information and then applies these targets to be desired targets in CEM. Finally, two sets of images, namely, computer-generated phantom images and real MR images, are used in the experiments to evaluate the effectiveness of UCEM. Experimental results demonstrate that UCEM segments a multispectral MR image much more effectively than either Functional MRI of the Brain's (FMRIB's) automated segmentation tool or fuzzy C-means does.