Adaptive fuzzy segmentation of magnetic resonance images

MRI intensity inhomogeneity correction by combining intensity and spatial information

We propose a novel fully automated method for retrospective correction of intensity inhomogeneity, which is an undesired phenomenon in many automatic image analysis tasks, especially if quantitative analysis is the final goal. Besides most commonly used intensity features, additional spatial image features are incorporated to improve inhomogeneity correction and to make it more dynamic, so that local intensity variations can be corrected more efficiently. The proposed method is a four-step iterative procedure in which a non-parametric inhomogeneity correction is conducted. First, the probability distribution of image intensities and corresponding second derivatives is obtained. Second, intensity correction forces, condensing the probability distribution along the intensity feature, are computed for each voxel. Third, the inhomogeneity correction field is estimated by regularization of all voxel forces, and fourth, the corresponding partial inhomogeneity correction is performed. The degree of inhomogeneity correction dynamics is determined by the size of regularization kernel. The method was qualitatively and quantitatively evaluated on simulated and real MR brain images. The obtained results show that the proposed method does not corrupt inhomogeneity-free images and successfully corrects intensity inhomogeneity artefacts even if these are more dynamic.

Cortical surface segmentation and mapping

Segmentation and mapping of the human cerebral cortex from magnetic resonance (MR) images plays an important role in neuroscience and medicine. This paper describes a comprehensive approach for cortical reconstruction, flattening, and sulcal segmentation. Robustness to imaging artifacts and anatomical consistency are key achievements in an overall approach that is nearly fully automatic and computationally fast. Results demonstrating the application of this approach to a study of cortical thickness changes in aging are presented.

A general framework for nonlinear multigrid inversion

A variety of new imaging modalities, such as optical diffusion tomography, require the inversion of a forward problem that is modeled by the solution to a three-dimensional partial differential equation. For these applications, image reconstruction is particularly difficult because the forward problem is both nonlinear and computationally expensive to evaluate. In this paper, we propose a general framework for nonlinear multigrid inversion that is applicable to a wide variety of inverse problems. The multigrid inversion algorithm results from the application of recursive multigrid techniques to the solution of optimization problems arising from inverse problems. The method works by dynamically adjusting the cost functionals at different scales so that they are consistent with, and ultimately reduce, the finest scale cost functional. In this way, the multigrid inversion algorithm efficiently computes the solution to the desired fine-scale inversion problem. Importantly, the new algorithm can greatly reduce computation because both the forward and inverse problems are more coarsely discretized at lower resolutions. An application of our method to Bayesian optical diffusion tomography with a generalized Gaussian Markov random-field image prior model shows the potential for very large computational savings. Numerical data also indicates robust convergence with a range of initialization conditions for this nonconvex optimization problem.

Classification of structural images via high-dimensional image warping, robust feature extraction, and SVM

This paper presents a method for classification of medical images, using machine learning and deformation-based morphometry. A morphological representation of the anatomy of interest is first obtained using high-dimensional template warping, from which regions that display strong correlations between morphological measurements and the classification (clinical) variable are extracted using a watershed segmentation, taking into account the regional smoothness of the correlation map which is estimated by a cross-validation strategy in order to achieve robustness to outliers. A Support Vector Machine-Recursive Feature Elimination (SVM-RFE) technique is then used to rank computed features from the extracted regions, according to their effect on the leave-one-out error bound. Finally, SVM classification is applied using the best set of features, and it is tested using leave-one-out. The results from a group of 61 brain images of female normal controls and schizophrenia patients demonstrate not only high classification accuracy (91.8%) and steep ROC curves, but also exceptional stability with respect to the number of selected features and the SVM kernel size.

Mapping techniques for aligning sulci across multiple brains

Visualization and mapping of function on the cortical surface is difficult because of its sulcal and gyral convolutions. Methods to unfold and flatten the cortical surface for visualization and measurement have been described in the literature. This makes visualization and measurement possible, but comparison across multiple subjects is still difficult because of the lack of a standard mapping technique. In this paper, we describe two methods that map each hemisphere of the cortex to a portion of a sphere in a standard way. To quantify how accurately the geometric features of the cortex -- i.e., sulci and gyri -- are mapped into the same location, sulcal alignment across multiple brains is analyzed, and probabilistic maps for different sulcal regions are generated to be used in automatic labelling of segmented sulcal regions.

Fast and robust parameter estimation for statistical partial volume models in brain MRI

Due to the finite spatial resolution of imaging devices, a single voxel in a medical image may be composed of mixture of tissue types, an effect known as partial volume effect (PVE). Partial volume estimation, that is, the estimation of the amount of each tissue type within each voxel, has received considerable interest in recent years. Much of this work has been focused on the mixel model, a statistical model of PVE. We propose a novel trimmed minimum covariance determinant (TMCD) method for the estimation of the parameters of the mixel PVE model. In this method, each voxel is first labeled according to the most dominant tissue type. Voxels that are prone to PVE are removed from this labeled set, following which robust location estimators with high breakdown points are used to estimate the mean and the covariance of each tissue class. Comparisons between different methods for parameter estimation based on classified images as well as expectation--maximization-like (EM-like) procedure for simultaneous parameter and partial volume estimation are reported. The robust estimators based on a pruned classification as presented here are shown to perform well even if the initial classification is of poor quality. The results obtained are comparable to those obtained using the EM-like procedure, but require considerably less computation time. Segmentation results of real data based on partial volume estimation are also reported. In addition to considering the parameter estimation problem, we discuss differences between different approximations to the complete mixel model. In summary, the proposed TMCD method allows for the accurate, robust, and efficient estimation of partial volume model parameters, which is crucial to a variety of brain MRI data analysis procedures such as the accurate estimation of tissue volumes and the accurate delineation of the cortical surface.

CRUISE: Cortical reconstruction using implicit surface evolution

Segmentation and representation of the human cerebral cortex from magnetic resonance (MR) images play an important role in neuroscience and medicine. A successful segmentation method must be robust to various imaging artifacts and produce anatomically meaningful and consistent cortical representations. A method for the automatic reconstruction of the inner, central, and outer surfaces of the cerebral cortex from T1-weighted MR brain images is presented. The method combines a fuzzy tissue classification method, an efficient topology correction algorithm, and a topology-preserving geometric deformable surface model (TGDM). The algorithm is fast and numerically stable, and yields accurate brain surface reconstructions that are guaranteed to be topologically correct and free from self-intersections. Validation results on real MR data are presented to demonstrate the performance of the method.

Automated morphometric study of brain variation in XXY males

This paper studies brain morphometry variation associated with XXY males (Klinefelter's syndrome) by using an automated whole-brain volumetric analysis method. The application to 34 XXY males and 62 normal male controls reveals pronounced volume reduction in the brains of XXY males, relative to the brains of normal controls, localized at the insula, temporal gyri, amygdala, hippocampus, cingulate, and occipital gyri. Most of these statistically significant regions are in the gray matter structures, with the exception of one cluster of atrophy involved in white matter structure, i.e., right parietal lobe white matter. Compared to previous findings documented in the literature, our findings provide a better spatial localization of the affected regions. In addition to the reduction of local volume, overall enlargement of ventricles and overall volume reduction of both white matter and gray matter are also found in XXY males.

Classification of Anatomical Structures in MR Brain Images Using Fuzzy Parameters

We present an algorithm that automatically segments and classifies the brain structures in a set of magnetic resonance (MR) brain images using expert information contained in a small subset of the image set. The algorithm is intended to do the segmentation and classification tasks mimicking the way a human expert would reason. The algorithm uses a knowledge base taken from a small subset of semiautomatically classified images that is combined with a set of fuzzy indexes that capture the experience and expectation a human expert uses during recognition tasks. The fuzzy indexes are tissue specific and spatial specific, in order to consider the biological variations in the tissues and the acquisition inhomogeneities through the image set. The brain structures are segmented and classified one at a time. For each brain structure the algorithm needs one semiautomatically classified image and makes one pass through the image set. The algorithm uses low-level image processing techniques on a pixel basis for the segmentations, then validates or corrects the segmentations, and makes the final classification decision using higher level criteria measured by the set of fuzzy indexes. We use single-echo MR images because of their high volumetric resolution; but even though we are working with only one image per brain slice, we have multiple sources of information on each pixel: absolute and relative positions in the image, gray level value, statistics of the pixel and its three-dimensional neighborhood and relation to its counterpart pixels in adjacent images. We have validated our algorithm for ease of use and precision both with clinical experts and with measurable error indexes over a Brainweb simulated MR set.

Method for bias field correction of brain T1-weighted magnetic resonance images minimizing segmentation error

This work presents a new algorithm (nonuniform intensity correction; NIC) for correction of intensity inhomogeneities in T1-weighted magnetic resonance (MR) images. The bias field and a bias-free image are obtained through an iterative process that uses brain tissue segmentation. The algorithm was validated by means of realistic phantom images and a set of 24 real images. The first evaluation phase was based on a public domain phantom dataset, used previously to assess bias field correction algorithms. NIC performed similar to previously described methods in removing the bias field from phantom images, without introduction of degradation in the absence of intensity inhomogeneity. The real image dataset was used to compare the performance of this new algorithm to that of other widely used methods (N3, SPM'99, and SPM2). This dataset included both low and high bias field images from two different MR scanners of low (0.5 T) and medium (1.5 T) static fields. Using standard quality criteria for determining the goodness of the different methods, NIC achieved the best results, correcting the images of the real MR dataset, enabling its systematic use in images from both low and medium static field MR scanners. A limitation of our method is that it might fail if the bias field is so high that the initial histogram does not show bimodal distribution for white and gray matter.

Central type benzodiazepine receptors in Gulf War veterans with posttraumatic stress disorder

BACKGROUND

A previous single photon emission computed tomography study showed decreased central type benzodiazepine receptors in the prefrontal cortex in Vietnam War veterans with posttraumatic stress disorder. To assess the generalizability of this finding to patients with more recent history, we studied central type benzodiazepine receptors in Gulf War veterans with posttraumatic stress disorder.

METHODS

Nineteen Gulf War veterans with posttraumatic stress disorder and 19 age-matched, healthy, nondeployed veterans participated in a single photon emission computed tomography study using [(123)I]iomazenil. Regional total distribution volume (V(T)') was compared between two groups using Statistical Parametric Mapping 99 (Wellcome Department of Imaging Neuroscience, London, United Kingdom) and volumes of interest analysis.

RESULTS

Benzodiazepine receptor levels did not show regional differences between the two groups, either with or without global normalization. Average difference in V(T)' was 2% across brain areas; however, by applying global normalization, V(T)' in the patient group showed significant negative correlation with childhood trauma scores in the right superior temporal gyrus.

CONCLUSIONS

Less severe symptoms and shorter duration of the illness in the current group than the prior one may be the source of the difference in the results of the two studies.

Positron emission tomography imaging of the serotonin transporter in subjects with a history of alcoholism

BACKGROUND

Our purpose was to investigate the serotonin transporter (SERT) in various brain regions of alcoholics using positron emission tomography and C-11 McN5652.

METHOD

Thirty-two adult subjects were involved, 17 social drinkers as control subjects and 15 subjects who were abstinent or recovering alcoholics. Concomitant psychiatric diseases were ruled out based on DSM-IV criteria. The majority of subjects were men. Radioligand binding in 11 brain areas was expressed as the total distribution volume (DV), distribution volume of specific binding (DV(spec)), and distribution volume ratio (DVR). The cerebellum was used as reference tissue for calculation of DV(spec) and DVR.

RESULTS

In subjects with a history of alcoholism, DV was lower in all brain regions, with significant differences in the midbrain, thalamus, amygdala, pons, cingulate gyrus, frontal cortex, and cerebellum. Additionally, DV(spec) was lower in all brain regions, but differences were only significant in the midbrain; DVR was lower in nine regions but the differences did not reach statistical significance.

CONCLUSIONS

These studies demonstrate lower binding of [(11)C](+)McN5652 to the SERT in the brain of abstinent or recovering alcoholics compared with control subjects. Differences in the radioligand distribution volumes are more significant before than after correction for nonspecific binding of the radioligand.

Automated segmentation of human brain MR images using a multi-agent approach

Image interpretation consists in finding a correspondence between radiometric information and symbolic labeling with respect to specific spatial constraints. It is intrinsically a distributed process in terms of goals to be reached, zones in the image to be processed and methods to be applied. To cope the the difficulty inherent in this process, several information processing steps are required to gradually extract information. In this paper we advocate the use of situated cooperative agents as a framework for managing such steps. Dedicated agent behaviors are dynamically adapted depending on their position in the image, of their topographic relationships and of the radiometric information available. The information collected by the agents is gathered, shared via qualitative maps, or used as soon as available by acquaintances. Incremental refinement of interpretation is obtained through a coarse to fine strategy. Our work is essentially focused on radiometry-based tissue interpretation where knowledge is introduced or extracted at several levels to estimate models for tissue-intensity distribution and to cope with noise, intensity non-uniformity and partial volume effect. Several experiments on phantom and real images were performed. A complete volume can be segmented in less than 5 min with about 0.84% accuracy of the segmented reference. Comparison with other techniques demonstrates the potential interest of our approach for magnetic resonance imaging (MRI) brain scan interpretation.

Accurate template-based correction of brain MRI intensity distortion with application to dementia and aging

This paper examines an alternative approach to separating magnetic resonance imaging (MRI) intensity inhomogeneity from underlying tissue-intensity structure using a direct template-based paradigm. This permits the explicit spatial modeling of subtle intensity variations present in normal anatomy which may confound common retrospective correction techniques using criteria derived from a global intensity model. A fine-scale entropy driven spatial normalisation procedure is employed to map intensity distorted MR images to a tissue reference template. This allows a direct estimation of the relative bias field between template and subject MR images, from the ratio of their low-pass filtered intensity values. A tissue template for an aging individual is constructed and used to correct distortion in a set of data acquired as part of a study on dementia. A careful validation based on manual segmentation and correction of nine datasets with a range of anatomies and distortion levels is carried out. This reveals a consistent improvement in the removal of global intensity variation in terms of the agreement with a global manual bias estimate, and in the reduction in the coefficient of intensity variation in manually delineated regions of white matter.

A fully automatic and robust brain MRI tissue classification method

A novel, fully automatic, adaptive, robust procedure for brain tissue classification from 3D magnetic resonance head images (MRI) is described in this paper. The procedure is adaptive in that it customizes a training set, by using a 'pruning' strategy, such that the classification is robust against anatomical variability and pathology. Starting from a set of samples generated from prior tissue probability maps (a 'model') in a standard, brain-based coordinate system ('stereotaxic space'), the method first reduces the fraction of incorrectly labeled samples in this set by using a minimum spanning tree graph-theoretic approach. Then, the corrected set of samples is used by a supervised kNN classifier for classifying the entire 3D image. The classification procedure is robust against variability in the image quality through a non-parametric implementation: no assumptions are made about the tissue intensity distributions. The performance of this brain tissue classification procedure is demonstrated through quantitative and qualitative validation experiments on both simulated MRI data (10 subjects) and real MRI data (43 subjects). A significant improvement in output quality was observed on subjects who exhibit morphological deviations from the model due to aging and pathology.

Generalized Kohonen’s competitive learning algorithms for ophthalmological MR image segmentation

Kohonen's self-organizing map is a two-layer feedforward competitive learning network. It has been used as a competitive learning clustering algorithm. In this paper, we generalize Kohonen's competitive learning (KCL) algorithm with fuzzy and fuzzy-soft types called fuzzy KCL (FKCL) and fuzzy-soft KCL (FSKCL). These generalized KCL algorithms fuse the competitive learning with soft competition and fuzzy c-means (FCM) membership functions. We then apply these generalized KCLs to MRI and MRA ophthalmological segmentations. These KCL-based MRI segmentation techniques are useful in reducing medical image noise effects using a learning mechanism. They may be particularly helpful in clinical diagnosis. Two real cases with MR image data recommended by an ophthalmologist are examined. First case is a patient with Retinoblastoma in her left eye, an inborn malignant neoplasm of the retina frequently metastasis beyond the lacrimal cribrosa. The second case is a patient with complete left side oculomotor palsy immediately after a motor vehicle accident. Her brain MRI with MRA, skull routine, orbital CT, and cerebral angiography did not reveal brainstem lesions, skull fractures, or vascular anomalies. These generalized KCL algorithms were used in segmenting the ophthalmological MRIs. KCL, FKCL and FSKCL comparisons are made. Overall, the FSKCL algorithm is recommended for use in MR image segmentation as an aid to small lesion diagnosis.

Higher in vivo muscarinic-2 receptor distribution volumes in aging subjects with an apolipoprotein E-epsilon4 allele

The apolipoprotein E-epsilon4 allele confers an increased susceptibility to age-related memory problems and Alzheimer's disease. Abnormalities in the cholinergic system are also likely contributors to memory deficits associated with aging and AD. To determine the effect of the APOE-epsilon4 allele on the muscarinic component of the cholinergic system of aging subjects, 10 healthy subjects with APOE-epsilon4 alleles (APOE-epsilon4+) and 10 without (APOE-epsilon4-), ranging in age from 52 to 75 years, were tomographically scanned with the F-18-labeled muscarinic-2 (M2) selective agonist, 3-(3-(3-[(18)F]Flouropropyl)thio)-1,2,5-thiadiazol-4-yl)-1,2,5,6-tetrahydro-1-methylpyridine ([(18)F]FP-TZTP). The distribution volumes (V(T)) of [(18)F]FP-TZTP were determined by compartmental modeling of partial volume and free fraction corrected PET scans. Regional cerebral blood flow (rCBF) measurements with H(2) (15)O were also performed. Global Gray V(T) (840 +/- 155 ml plasma/ml tissue) was greater in APOE-epsilon4+ subjects than APOE-epsilon4- subjects (660 +/- 113 ml plasma/ml tissue, P = 0.01), and previously studied younger subjects. There were no significant differences between the groups with respect to rCBF, but within the APOE-epsilon4+ group there was a trend for subjects with the higher Global Gray V(T)s to have lower Global Gray CBFs (r = -0.65, P < 0.06). A lower concentration of acetylcholine in the synapse of APOE-epsilon4+ older individuals is a likely explanation for the greater [(18)F]FP-TZTP distribution volumes.

Analyzing the neocortical fine-structure

Cytoarchitectonic fields of the human neocortex are defined by characteristic variations in the composition of a general six-layer structure. It is commonly accepted that these fields correspond to functionally homogeneous entities. Diligent techniques were developed to characterize cytoarchitectonic fields by staining sections of post-mortem brains and subsequent statistical evaluation. Fields were found to show a considerable interindividual variability in extent and relation to macroscopic anatomical landmarks. With upcoming new high-resolution magnetic resonance imaging (MRI) protocols, it appears worthwhile to examine the feasibility of characterizing the neocortical fine-structure from anatomical MRI scans, thus, defining neocortical fields by in vivo techniques. A fixated brain hemisphere was scanned at a resolution of approximately 0.3 mm. After correcting for intensity inhomogeneities in the dataset, the cortex boundaries (the white/grey matter and grey matter/background interfaces) were determined as a triangular mesh. Radial intensity profiles following the shortest path through the cortex were computed and characterized by a sparse set of features. A statistical similarity measure between features of different regions was defined, and served to define the extent of Brodmann's Areas 4, 17, 44 and 45 in this dataset.

An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation

An adaptive spatial fuzzy c-means clustering algorithm is presented in this paper for the segmentation of three-dimensional (3-D) magnetic resonance (MR) images. The input images may be corrupted by noise and intensity nonuniformity (INU) artifact. The proposed algorithm takes into account the spatial continuity constraints by using a dissimilarity index that allows spatial interactions between image voxels. The local spatial continuity constraint reduces the noise effect and the classification ambiguity. The INU artifact is formulated as a multiplicative bias field affecting the true MR imaging signal. By modeling the log bias field as a stack of smoothing B-spline surfaces, with continuity enforced across slices, the computation of the 3-D bias field reduces to that of finding the B-spline coefficients, which can be obtained using a computationally efficient two-stage algorithm. The efficacy of the proposed algorithm is demonstrated by extensive segmentation experiments using both simulated and real MR images and by comparison with other published algorithms.

In vivo muscarinic 2 receptor imaging in cognitively normal young and older volunteers

The precise effects of normal aging on the cholinergic system are unknown, as both in vitro and PET studies have shown conflicting results. In vivo determination of muscarinic receptor distribution and density has been hampered by both poor subtype selectivity and/or blood-brain barrier permeability of known ligands. Previous in vitro and in vivo work with the F-18 labeled muscarinic agonist, 3-(3- (3-[(18)F]Flouropropyl)thio)-1,2,5-thiadiazol-4-yl)-1,2,5,6-tetrahydro-1-methylpyridine ((18)FP-TZTP) suggested the use of (18)FP-TZTP to selectively quantify M2 receptors in humans. In this study, we used (18)FP-TZTP to infer M2 receptor avidity in the brains of 15 healthy younger subjects (mean age = 28.3 +/- 5.5 years) and 20 healthy older subjects (mean age = 62.1 +/- 7.7 years). Corrections for subject motion during the 120-min acquisition and partial voluming (PVC) were performed. A one-tissue compartment model was used to estimate the volumes of distribution (V(T)) of (18)FP-TZTP. Within both groups of subjects, volumes of distribution (K(1)/k(2)) in cortical, subcortical, and cerebellar areas were consistent with M2 receptor topography. Compared to younger subjects older subjects had significantly higher means and standard deviations for the volumes of distribution of (18)FP-TZTP throughout much of the cerebellum, cortex, and subcortex (Global Gray V(T) = 742 +/- 163 in older subjects and 645 +/- 74 in younger subjects, P < 0.03). Across all subjects (18)FP-TZTP, regional, and Global Gray distribution volumes were significantly correlated to age (Global Gray V(T,) r = 0.41, P < 0.01). A lower concentration of acetylcholine in the synapse of some older subjects is one possible explanation for the data.

Survey of fuzzy logic applications in brain-related researches

The aim of this study was to survey fuzzy logic (FL) applications in brain researches. In general, these applications are related to pattern recognition for localization in brain structures or tumor detection, image segmentation, and simulations. In recent years, neural networks and FL are gaining popularity. FL is based on the observation of people. The enormous amount of information representation by the brain suggests that FL principles can be useful, especially for complex brain functions. Causal models based on functional neuroanatomy can be then implemented in computer simulations to reflect the dynamical intersection of brain structures. FL is considered as an appropriate tool for modelling and control. FL has been applied in different ways to brain researches. This paper surveys the utilization of FL in brain researches.

Multicontext fuzzy clustering for separation of brain tissues in magnetic resonance images

A local image model is proposed to eliminate the adverse impact of both artificial and inherent intensity inhomogeneities in magnetic resonance imaging on intensity-based image segmentation methods. The estimation and correction procedures for intensity inhomogeneities are no longer indispensable because the highly convoluted spatial distribution of different tissues in the brain is taken into consideration. On the basis of the local image model, multicontext fuzzy clustering (MCFC) is proposed for classifying 2D and 3D MR data into tissues of white matter, gray matter, and cerebral spinal fluid automatically. In MCFC, multiple clustering contexts are generated for each pixel, and fuzzy clustering is independently performed in each context to calculate the degree of membership of a pixel to each tissue class. To maintain the statistical reliability and spatial continuity of membership distributions, a fusion strategy is adopted to integrate the clustering outcomes from different contexts. The fusion result is taken as the final membership value of the pixel. Experimental results on both real MR images and simulated volumetric MR data show that MCFC outperforms the classic fuzzy c-means (FCM) as well as other segmentation methods that deal with intensity inhomogeneities.

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.

HAMMER: hierarchical attribute matching mechanism for elastic registration

A new approach is presented for elastic registration of medical images, and is applied to magnetic resonance images of the brain. Experimental results demonstrate very high accuracy in superposition of images from different subjects. There are two major novelties in the proposed algorithm. First, it uses an attribute vector, i.e., a set of geometric moment invariants (GMIs) that are defined on each voxel in an image and are calculated from the tissue maps, to reflect the underlying anatomy at different scales. The attribute vector, if rich enough, can distinguish between different parts of an image, which helps establish anatomical correspondences in the deformation procedure; it also helps reduce local minima, by reducing ambiguity in potential matches. This is a fundamental deviation of our method, referred to as the hierarchical attribute matching mechanism for elastic registration (HAMMER), from other volumetric deformation methods, which are typically based on maximizing image similarity. Second, in order to avoid being trapped by local minima, i.e., suboptimal poor matches, HAMMER uses a successive approximation of the energy function being optimized by lower dimensional smooth energy functions, which are constructed to have significantly fewer local minima. This is achieved by hierarchically selecting the driving features that have distinct attribute vectors, thus, drastically reducing ambiguity in finding correspondence. A number of experiments demonstrate that the proposed algorithm results in accurate superposition of image data from individuals with significant anatomical differences.

Estimating cerebral atrophy in multiple sclerosis patients from various MR pulse sequences

PURPOSE

The purpose of this study was to determine how measures reflecting cerebral atrophy (CA) are influenced by pulse sequence (PS) and segmentation algorithm (SA) used in multiple sclerosis (MS) patients and healthy control (HC)s.

METHODS

Magnetic resonance imaging (MRI) scans from 10 relapsing-remitting MS (RRMS) patients and five HCs were used to determine the change in brain fractional volume (BFV) over a two-year period. T1-weighted, fluid-attenuated inversion recovery (FLAIR), and proton density (PD)/T2-weighted sequences were analysed Image segmentation to determine brain volume was performed using the following a histogram SA, an adaptive fuzzy c-means algorithm (AFCM), and an adaptive Bayesian segmentation with a K-means clustering.

RESULTS

Combinations of the SA and PS in MS patents demonstrated significant differences in the per cent change in BFV from baseline. For the combination of PS and SA the per cent change in BFV for year one and year two varied from +2.05% to - 1.6% and +0.79% to -3.11%, respectively. Analysis of the HCs data revealed fluctuations in BFV varying from +0.26% to -0.29%.

CONCLUSIONS

MRI estimates of CA are dependent on both the type of PS and SA; therefore, the choice of SA technique and PS should be consistent during an MS treatment trial. The progression of CA in MS should only be used as a secondary or tertiary outcome measure in treatment trials until a better understanding of how this measurement is affected by the disease, the image acquisition and analysis techniques.

BrainSuite: an automated cortical surface identification tool

We describe a new magnetic resonance (MR) image analysis tool that produces cortical surface representations with spherical topology from MR images of the human brain. The tool provides a sequence of low-level operations in a single package that can produce accurate brain segmentations in clinical time. The tools include skull and scalp removal, image nonuniformity compensation, voxel-based tissue classification, topological correction, rendering, and editing functions. The collection of tools is designed to require minimal user interaction to produce cortical representations. In this paper we describe the theory of each stage of the cortical surface identification process. We then present classification validation results using real and phantom data. We also present a study of interoperator variability.

A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data

In this paper, we present a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the radio-frequency coils or to problems associated with the acquisition sequences. The result is a slowly varying shading artifact over the image that can produce errors with conventional intensity-based classification. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel) to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution toward piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. Experimental results on both synthetic images and MR data are given to demonstrate the effectiveness and efficiency of the proposed algorithm.

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.

Retrospective correction of MR intensity inhomogeneity by information minimization

In this paper, the problem of retrospective correction of intensity inhomogeneity in magnetic resonance (MR) images is addressed. A novel model-based correction method is proposed, based on the assumption that an image corrupted by intensity inhomogeneity contains more information than the corresponding uncorrupted image. The image degradation process is described by a linear model, consisting of a multiplicative and an additive component which are modeled by a combination of smoothly varying basis functions. The degraded image is corrected by the inverse of the image degradation model. The parameters of this model are optimized such that the information of the corrected image is minimized while the global intensity statistic is preserved. The method was quantitatively evaluated and compared to other methods on a number of simulated and real MR images and proved to be effective, reliable, and computationally attractive. The method can be widely applied to different types of MR images because it solely uses the information that is naturally present in an image, without making assumptions on its spatial and intensity distribution. Besides, the method requires no preprocessing, parameter setting, nor user interaction. Consequently, the proposed method may be a valuable tool in MR image analysis.

Volume delineation by fusion of fuzzy sets obtained from multiplanar tomographic images

Techniques of three-dimensional (3-D) volume delineation from tomographic medical imaging are usually based on 2-D contour definition. For a given structure, several different contours can be obtained depending on the segmentation method used or the user's choice. The goal of this work is to develop a new method that reduces the inaccuracies generally observed. A minimum volume that is certain to be included in the volume concerned (membership degree mu = 1), and a maximum volume outside which no part of the volume is expected to be found (membership degree mu = 0), are defined semi-automatically. The intermediate fuzziness region (0 < mu < 1) is processed using the theory of possibility. The resulting fuzzy volume is obtained after data fusion from multiplanar slices. The influence of the contrast-to-noise ratio was tested on simulated images. The influence of slice thickness as well as the accuracy of the method were studied on phantoms. The absolute volume error was less than 2% for phantom volumes of 2-8 cm3, whereas the values obtained with conventional methods were much larger than the actual volumes. Clinical experiments were conducted, and the fuzzy logic method gave a volume lower than that obtained with the conventional method. Our fuzzy logic method allows volumes to be determined with better accuracy and reproducibility.

Interactive 3-dimensional segmentation of MRI data in personal computer environment

We describe a method of interactive three-dimensional segmentation and visualization for anatomical magnetic resonance imaging (MRI) data in a personal computer environment. The visual feedback necessary during 3-D segmentation was provided by a ray casting algorithm, which was designed to allow users to interactively decide the visualization quality depending on the task-requirement. Structures such as gray matter, white matter, and facial skin from T1-weighted high-resolution MRI data were segmented and later visualized with surface rendering. Personal computers with central processing unit (CPU) speeds of 266, 400, and 700 MHz, were used for the implementation. The 3-D visualization upon each execution of the segmentation operation was achieved in the order of 2 s with a 700 MHz CPU. Our results suggest that 3-D volume segmentation with semi real-time visual feedback could be effectively implemented in a PC environment without the need for dedicated graphics processing hardware.

Automated graph-based analysis and correction of cortical volume topology

The human cerebral cortex is topologically equivalent to a sheet and can be considered topologically spherical if it is closed at the brain stem. Low-level segmentation of magnetic resonance (MR) imagery typically produces cerebral volumes whose tessellations are not topologically spherical. We present a novel algorithm that analyzes and constrains the topology of a volumetric object. Graphs are formed that represent the connectivity of voxel segments in the foreground and background of the image. These graphs are analyzed and minimal corrections to the volume are made prior to tessellation. We apply the algorithm to a simple test object and to cerebral white matter masks generated by a low-level tissue identification sequence. We tessellate the resulting objects using the marching cubes algorithm and verify their topology by computing their Euler characteristics. A key benefit of the algorithm is that it localizes the change to a volume to the specific areas of its topological defects.

Magnetic resonance image tissue classification using a partial volume model

We describe a sequence of low-level operations to isolate and classify brain tissue within T1-weighted magnetic resonance images (MRI). Our method first removes nonbrain tissue using a combination of anisotropic diffusion filtering, edge detection, and mathematical morphology. We compensate for image nonuniformities due to magnetic field inhomogeneities by fitting a tricubic B-spline gain field to local estimates of the image nonuniformity spaced throughout the MRI volume. The local estimates are computed by fitting a partial volume tissue measurement model to histograms of neighborhoods about each estimate point. The measurement model uses mean tissue intensity and noise variance values computed from the global image and a multiplicative bias parameter that is estimated for each region during the histogram fit. Voxels in the intensity-normalized image are then classified into six tissue types using a maximum a posteriori classifier. This classifier combines the partial volume tissue measurement model with a Gibbs prior that models the spatial properties of the brain. We validate each stage of our algorithm on real and phantom data. Using data from the 20 normal MRI brain data sets of the Internet Brain Segmentation Repository, our method achieved average kappa indices of kappa = 0.746 +/- 0.114 for gray matter (GM) and kappa = 0.798 +/- 0.089 for white matter (WM) compared to expert labeled data. Our method achieved average kappa indices kappa = 0.893 +/- 0.041 for GM and kappa = 0.928 +/- 0.039 for WM compared to the ground truth labeling on 12 volumes from the Montreal Neurological Institute's BrainWeb phantom.

An adaptive-focus statistical shape model for segmentation and shape modeling of 3-D brain structures

This paper presents a deformable model for automatically segmenting brain structures from volumetric magnetic resonance (MR) images and obtaining point correspondences, using geometric and statistical information in a hierarchical scheme. Geometric information is embedded into the model via a set of affine-invariant attribute vectors, each of which characterizes the geometric structure around a point of the model from a local to a global scale. The attribute vectors, in conjunction with the deformation mechanism of the model, warranty that the model not only deforms to nearby edges, as is customary in most deformable surface models, but also that it determines point correspondences based on geometric similarity at different scales. The proposed model is adaptive in that it initially focuses on the most reliable structures of interest, and gradually shifts focus to other structures as those become closer to their respective targets and, therefore, more reliable. The proposed techniques have been used to segment boundaries of the ventricles, the caudate nucleus, and the lenticular nucleus from volumetric MR images.

Automated separation of gray and white matter from MR images of the human brain

A simple automatic procedure for segmentation of gray and white matter in high resolution 1.5T T1-weighted MR human brain images was developed and validated. The algorithm is based on histogram shape analysis of MR images that were corrected for scanner nonuniformity. Calibration and validation was done on a set of 80 MR images of human brains. The automatic method's values for the gray and white matter volumes were compared with the values from thresholds set twice by the best three of six raters. The automatic procedure was shown to perform as good as the best rater, where the average result of the best three raters was taken as reference. The method was also compared with two other histogram-based threshold methods, which yielded comparable results. The conclusion of the study thus is that automated threshold based methods can separate gray and white matter from MR brain images as reliably as human raters using a thresholding procedure.

Hybrid artificial neural network segmentation of precise and accurate inversion recovery (PAIR) images from normal human brain

This paper presents a novel semi-automated segmentation and classification method based on raw signal intensities from a quantitative T1 relaxation technique with two novel approaches for the removal of partial volume effects. The segmentation used a Kohonen Self Organizing Map that eliminated inter- and intra-operator variability. A Multi-layered Backpropagation Neural Network was able to classify the test data with a predicted accuracy of 87.2% when compared to manual classification. A linear interpolation of the quantitative T1 information by region and on a pixel-by-pixel basis was used to redistribute voxels containing a partial volume of gray matter (GM) and white matter (WM) or a partial volume of GM and cerebrospinal fluid (CSF) into the principal components of GM, WM, and CSF. The method presented was validated against manual segmentation of the base images by three experienced observers. Comparing segmented outputs directly to the manual segmentation revealed a difference of less than 2% in GM and less than 6% in WM for pure tissue estimations for both the regional and pixel-by-pixel redistribution techniques. This technique produced accurate estimates of the amounts of GM and WM while providing a reliable means of redistributing partial volume effects.

Restoration of low resolution metabolic images with a priori anatomic information: 23Na MRI in myocardial infarction

A new iterative extrapolation image reconstruction algorithm is presented, which enhances low resolution metabolic magnetic resonance images (MRI) with information about the bounds of signal sources obtained from a priori anatomic proton ((1)H) MRI. The algorithm ameliorates partial volume and ringing artefacts, leaving unchanged local metabolic heterogeneity that is present in the original dataset but not evident at (1)H MRI. Therefore, it is ideally suited to metabolic studies of ischemia, infarction and other diseases where the extent of the abnormality at (1)H MRI is uncertain. The performance of the algorithm is assessed by simulations, MRI of phantoms, and by surface coil 23Na MRI studies of canine myocardial infarction on a clinical scanner where the injury was not evident at (1)H MRI. The algorithm includes corrections for transverse field inhomogeneity, and for the leakage of intense signals into regions of interest such as 23Na MRI signals from ventricular blood ringing into the myocardium. The simulations showed that the algorithm reduced ringing artefacts by 15%, was stable at low SNR ( approximately 7), but is sensitive to the positioning of the (1)H MRI boundaries. The 23Na MRI showed hyperenhancement of regions identified as infarcted at post-mortem histological staining. The areas of hyperenhancement were measured by five independent observers in four 23Na images of infarction reconstructed with and without the algorithm. The infarct areas were correlated with areas determined by post-mortem histological staining with coefficient 0.85 for the enhanced images, compared to 0.58 with the conventional images. The scatter in the amplitude and in the area measurements of ischemia-associated hyper-enhancement in 23Na MRI was reduced by the algorithm by 1.6-fold and by at least 3-fold, respectively, demonstrating its ability to substantially improve quantification of the extent and intensity of metabolic changes in injured tissue that is not evident by (1)H MRI.

Reconstruction of the human cerebral cortex from magnetic resonance images

Reconstructing the geometry of the human cerebral cortex from MR images is an important step in both brain mapping and surgical path planning applications. Difficulties with imaging noise, partial volume averaging, image intensity inhomogeneities, convoluted cortical structures, and the requirement to preserve anatomical topology make the development of accurate automated algorithms particularly challenging. In this paper we address each of these problems and describe a systematic method for obtaining a surface representation of the geometric central layer of the human cerebral cortex. Using fuzzy segmentation, an isosurface algorithm, and a deformable surface model, the method reconstructs the entire cortex with the correct topology, including deep convoluted sulci and gyri. The method is largely automated and its results are robust to imaging noise, partial volume averaging, and image intensity inhomogeneities. The performance of this method is demonstrated, both qualitatively and quantitatively, and the results of its application to six subjects and one simulated MR brain volume are presented.