International Journal of Pattern Recognition and Artificial Intelligence

This paper offers a definition of precise, comprehensive, robust and practical neuroanatomical segmentation in magnetic resonance brain images with the goal of performing quantitative morphometric analyses. The main types of difficulties experienced with such problems are described, including those relating to the classification of MR signal intensities and the fact that there is insufficient information in the 2D image. To illustrate the details of obtaining a morphometric description, a case study of semi-automated methods is presented for segmenting the lateral ventricles and caudate nucleus in T1 coronal MR image data. The most significant remaining difficulties are summarized and are offered as objectives for further research.

A procedure for estimating the joint probability density function (pdf) of T₁, T₂ and proton spin density (P_D) for gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) in the brain is presented. The pdf's have numerous applications, including the study of tissue parameter variability in pathology and across populations. The procedure requires a multispectral, spin echo magnetic resonance imaging (MRI) data set of the brain. It consists of five automated steps:

(i) preprocess the data to remove extracranial tissue using a sequence of image processing operators;

(ii) estimate T₁, T₂ and P_D by fitting the preprocessed data to an imaging equation;

(iii) perform a fuzzy c-means clustering on the same preprocessed data to obtain a spatial map representing the membership value of the three tissue classes at each pixel location;

(iv) reject estimates which are not from pure tissue or have poor fits in the parameter estimation, and classify the remaining estimates as either GM, WM or CSF;

(v) compute statistics on the classified estimates to obtain a probability mass function and a Gaussian joint pdf of the tissue parameters for each tissue class.

Some preliminary results are shown comparing computed pdf's of young, elderly and Alzheimer's subjects. Two brief examples applying the joint pdf's to pulse sequence optimization and generation of computational phantoms are also provided.

Magnetic resonance imaging (MRI) has become a widely used research and clinical tool in the study of the human brain. The ability to robustly and accurately quantify repeatable morphological measures from such data is aided by the ability to accurately segment the MRI data set into homogeneous regions such as gray matter, white matter, and cerebro spinal fluid. The large amount of data associated with typical MRI scans makes completely manual segmentation prohibitive on a large scale. In this paper an efficient approach to the segmentation of such MR imagery is presented. The approach uses an estimation-theoretic interpretation of the segmentation problem to develop a computationally efficient, statistically-based recursive technique for its solution. Being statistically based, the method also provides associated measures of uncertainty of the resulting estimates, which are extremely important both for evaluation of the estimates as well as their combination with other sources of information.

Segmentation of anatomic structures of the human brain from MR images is important for assessing treatment efficacy, screening for anomalies, and improving our understanding of human development. The labor intensive nature of manual segmentation, however, makes such a technique viable only in selected cases. In this paper we present a new approach to segmentation that involves only minimal human interactions. The technique utilizes a variational formulation to obtain an edge-strength function over the region of interest, and uses curve evolution and a pre-segmented atlas to guide the actual segmentation process. The approach is demonstrated via both phantoms and actual MR images, and when applied to the lateral ventricles and caudate nucleus, showed a size accuracy error of 5%–20% with respect to manual segmentation, depending on the manual segmentation method utilized.

The wide availability of high resolution magnetic resonance images (MRI) of the brain has facilitated tremendous progress in neuroscience. Accurate automated segmentation and quantification of neuroanatomical structure from such images is crucial for the advancement of the understanding of brain morphology, both in normal variation and in disease. Gradient-based deformable surface finding is a powerful technique for locating structure in three-dimensional images. However, it often suffers from poorly defined edges and noise. This paper proposes a gradient-based deformable surface finding approach that integrates region information. This makes the resulting procedure more robust to noise and improper initialization. In addition, prior shape information may be incorporated. The algorithm uses Gauss's Divergence theorem to find the surface of a homogeneous region-classified area in the image and integrates this with a gray-level gradient-based surface finder. Experimental results on synthetic and MR brain images show that a significant improvement is achieved as a consequence of the use of this extra information. Further, these improvements are achieved with little increase in computational overhead, an advantage derived from the application of Gauss's Divergence theorem.

Magnetic resonance imaging (MRI) has become the modality of choice for neuro-anatomical imaging. Quantitative analysis requires the accurate and reproducible labeling of all voxels in any given structure within the brain. Since manual labeling is prohibitively time-consuming and error-prone we have designed an automated procedure called ANIMAL (Automatic Nonlinear Image Matching and Anatomical Labeling) to objectively segment gross anatomical structures from 3D MRIs of normal brains. The procedure is based on nonlinear registration with a previously labeled target brain, followed by numerical inverse transformation of the labels to the native MRI space. Besides segmentation, ANIMAL has been applied to non-rigid registration and to the analysis of morphometric variability.

In this paper, the nonlinear registration approach is validated on five test volumes, produced with simulated deformations. Experiments show that the ANIMAL recovers 64% of the nonlinear residual variability remaining after linear registration. Segmentations of the same test data are presented as well.

The paper concludes with two applications of ANIMAL using real data. In the first, one MRI volume is nonlinearly matched to a second and is automatically segmented using labels, predefined on the second MRI volume. The automatic segmentation compares well with manual labeling of the same structures. In the second application, ANIMAL is applied to seventeen MRI data sets, and a 3D map of anatomical variability estimates is produced. The automatic variability estimates correlate well (r =0.867, p = 0.01) with manual estimates of inter-subject variability.

We propose a method for the 3D segmentation and representation of cortical folds with a special emphasis on the cortical sulci. These cortical structures are represented using "active ribbons". Active ribbons are built from active surfaces, which represent the median surface of a particular sulcus filled by CSF. Sulci modeling is obtained from MRI acquisitions (usually T1 images). The segmentation is performed using an automatic labeling procedure to separate gyri from sulci based on curvature analysis of the different iso-intensity surfaces of the original MRI volume. The outer parts of the sulci are used to initialize the convergence of the active ribbon from the outer parts of the brain to the interior. This procedure has two advantages: first, it permits the labeling of voxels belonging to sulci on the external part of the brain as well as on the inside (which is often the hardest point) and secondly, this segmentation allows 3D visualization of the sulci in the MRI volumetric environment as well as showing the sophisticated shapes of the cortical structures by means of isolated surfaces. Active ribbons can be used to study the complicated shape of the cortical anatomy, to model the variability of these structures in shape and position, to assist nonlinear registrations of human brains by locally controlling the warping procedure, to map brain neurophysiological functions into morphology or even to select the trajectory of an intra-sulci (virtual) endoscope.

This paper develops mathematical representations for neuro-anatomically significant substructures of the brain and their variability in a population. The focus of the paper is on the neuro-anatomical variation of the geometry and the "shape" of two-dimensional surfaces in the brain. As examples, we focus on the cortical and hippocampal surfaces in an ensemble of Macaque monkeys and human MRI brains. The "shapes" of the substructures are quantified via the construction of templates; the variations are represented by defining probabilistic deformations of the template. Methods for empirically estimating probability measures on these deformations are developed by representing the deformations as Gaussian random vector fields on the embedded sub-manifolds. The Gaussian random vector fields are constructed as quadratic mean limits using complete orthonormal bases on the sub-manifolds. The complete orthonormal bases are generated using modes of vibrations of the geometries of the brain sub-manifolds. The covariances are empirically estimated from an ensemble of brain data. Principal component analysis is presented for characterizing the "eigen-shape" of the hippocampus in an ensemble of MRI-MPRAGE whole brain images. Clustering based on eigen-shape is presented for two sub-populations of normal and schizophrenic.

The design of representative models of the human body is of great interest to medical doctors. Qualitative information about the characteristics of the brain is widely available, but due to the volume of information that needs to be analyzed and the complexity of its structure, rarely is there quantification according to a standard model. To address this problem, we propose in this paper an automatic method to retrieve corresponding structures from a database of medical images. This procedure being local and fast, will permit navigation through large databases in a practical amount of time. We present as examples of applications the building of an average volume of interest and preliminary results of classification according to morphology.

While the role and utility of Magnetic Resonance Images as a diagnostic tool are well established in current clinical practice, there are a number of emerging medical arenas in which MRI can play an equally important role. In this article, we consider the problem of image-guided surgery, and provide an overview of a series of techniques that we have recently developed in order to automatically utilize MRI-based anatomical reconstructions for surgical guidance and navigation.