A segmentation-based and partial-volume-compensated method for an accurate measurement of lateral ventricular volumes on T-1-weighted magnetic resonance images

Lateral ventricular volumes based on segmented brain MR images can be significantly underestimated if partial volume effects are not considered. This is because a group of voxels in the neighborhood of lateral ventricles is often mis-classified as gray matter voxels due to partial volume effects. This group of voxels is actually a mixture of ventricular cerebro-spinal fluid and the white matter and therefore, a portion of it should be included as part of the lateral ventricular structure. In this note, we describe an automated method for the measurement of lateral ventricular volumes on segmented brain MR images. Image segmentation was carried in combination of intensity correction and thresholding. The method is featured with a procedure for addressing mis-classified voxels in the surrounding of lateral ventricles. A detailed analysis showed that lateral ventricular volumes could be underestimated by 10 to 30% depending upon the size of the lateral ventricular structure, if mis-classified voxels were not included. Validation of the method was done through comparison with the averaged manually traced volumes. Finally, the merit of the method is demonstrated in the evaluation of the rate of lateral ventricular enlargement. (C) 2001 Elsevier Science Inc. All rights reserved.

Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images

Mixture models implemented via the expectation-maximization (EM) algorithm are being increasingly used in a wide range of problems in pattern recognition such as image segmentation. However, the EM algorithm requires considerable computational time in its application to huge data sets such as a three-dimensional magnetic resonance (MR) image of over 10 million voxels. Recently, it was shown that a sparse, incremental version of the EM algorithm could improve its rate of convergence. In this paper, we show how this modified EM algorithm can be speeded up further by adopting a multiresolution kd-tree structure in performing the E-step. The proposed algorithm outperforms some other variants of the EM algorithm for segmenting MR images of the human brain. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

The use of unwrapped phase in MR image segmentation: A preliminary study

This paper considers the problem of tissue classification in 3D MRI. More specifically, a new set of texture features, based on phase information, is used to perform the segmentation of the bones of the knee. The phase information provides a very good discrimination between the bone and the surrounding tissues, but is usually not used due to phase unwrapping problems. We present a method to extract textural information from the phase that does not require phase unwrapping. The textural information extracted from the magnitude and the phase can be combined to perform tissue classification, and used to initialise an active shape model, leading to a more precise segmentation.

Automatic initialisation of 3D deformable models for cartilage segmentation

Deformable models are a highly accurate and flexible approach to segmenting structures in medical images. The primary drawback of deformable models is that they are sensitive to initialisation, with accurate and robust results often requiring initialisation close to the true object in the image. Automatically obtaining a good initialisation is problematic for many structures in the body. The cartilages of the knee are a thin elastic material that cover the ends of the bone, absorbing shock and allowing smooth movement. The degeneration of these cartilages characterize the progression of osteoarthritis. The state of the art in the segmentation of the cartilage are 2D semi-automated algorithms. These algorithms require significant time and supervison by a clinical expert, so the development of an automatic segmentation algorithm for the cartilages is an important clinical goal. In this paper we present an approach towards this goal that allows us to automatically providing a good initialisation for deformable models of the patella cartilage, by utilising the strong spatial relationship of the cartilage to the underlying bone.

Automatic segmentation of the knee bones using 3D active shape models

This paper presents an automated segmentation approach for MR images of the knee bones. The bones are the first stage of a segmentation system for the knee, primarily aimed at the automated segmentation of the cartilages. The segmentation is performed using 3D active shape models (ASM), which are initialized using an affine registration to an atlas. The 3D ASMs of the bones are created automatically using a point distribution model optimization scheme. The accuracy and robustness of the segmentation approach was experimentally validated using an MR database of fat suppressed spoiled gradient recall images.

Random numbers from astronomical imaging

This article describes a method to turn astronomical imaging into a random number generator by using the positions of incident cosmic rays and hot pixels to generate bit streams. We subject the resultant bit streams to a battery of standard benchmark statistical tests for randomness and show that these bit streams are statistically the same as a perfect random bit stream. Strategies for improving and building upon this method are outlined.

Prefrontal cortex involvement in selective letter generation: A functional magnetic resonance imaging study

Cerebral responses to alternating periods of a control task and a selective letter generation paradigm were investigated with functional Magnetic Resonance Imaging (fMRI). Subjects selectively generated letters from four designated sets of six letters from the English language alphabet, with the instruction that they were not to produce letters in alphabetical order either forward or backward, repeat or alternate letters. Performance during this condition was compared with that of a control condition in which subjects recited the same letters in alphabetical order. Analyses revealed significant and extensive foci of activation in a number of cerebral regions including mid-dorsolateral frontal cortex, inferior frontal gyrus, precuneus, supramarginal gyrus, and cerebellum during the selective letter generation condition. These findings are discussed with respect to recent positron emission tomography (PET) and fMRI studies of verbal working memory and encoding/retrieval in episodic memory.

Unsupervised cell nucleus segmentation with active contours

The task of segmenting cell nuclei from cytoplasm in conventional Papanicolaou (Pap) stained cervical cell images is a classical image analysis problem which may prove to be crucial to the development of successful systems which automate the analysis of Pap smears for detection of cancer of the cervix. Although simple thresholding techniques will extract the nucleus in some cases, accurate unsupervised segmentation of very large image databases is elusive. Conventional active contour models as introduced by Kass, Witkin and Terzopoulos (1988) offer a number of advantages in this application, but suffer from the well-known drawbacks of initialisation and minimisation. Here we show that a Viterbi search-based dual active contour algorithm is able to overcome many of these problems and achieve over 99% accurate segmentation on a database of 20 130 Pap stained cell images. (C) 1998 Elsevier Science B.V. All rights reserved.

An evaluation of the time dependence of the anisotropy of the water diffusion tensor in acute human ischemia

We have performed MRI examinations to determine the water diffusion tensor in the brain of six patients who were admitted to the hospital within 12 h after the onset of cerebral ischemic symptoms. The examinations have been carried out immediately after admission, and thereafter at varying intervals up to 90 days post admission. Maps of the trace of the diffusion tensor, the fractional anisotropy and the lattice index, as well as maps of cerebral blood perfusion parameters, were generated to quantitatively assess the character of the water diffusion tensor in the infarcted area. In patients with significant perfusion deficits and substantial lesion volume changes, four of six cases, our measurements show a monotonic and significant decrease in the diffusion anisotropy within the ischemic lesion as a function of time. We propose that retrospective analysis of this quantity, in combination with brain tissue segmentation and cerebral perfusion maps, may be used in future studies to assess the severity of the ischemic event. (C) 1999 Elsevier Science Inc.

A reproducible method for automated extraction of brain volumes from 3D human head MR images

An automated method for extracting brain volumes from three commonly acquired three-dimensional (3D) MR images (proton density, T1 weighted, and T2-weighted) of the human head is described. The procedure is divided into four levels: preprocessing, segmentation, scalp removal, and postprocessing. A user-provided reference point is the sole operator-dependent input required, The method's parameters were first optimized and then fixed and applied to 30 repeat data sets from 15 normal older adult subjects to investigate its reproducibility. Percent differences between total brain volumes (TBVs) for the subjects' repeated data sets ranged from .5% to 2.2%. We conclude that the method is both robust and reproducible and has the potential for wide application.

Predicting composition of leg sections with anthropometry and bioelectrical impedance analysis using magnetic resonance imaging as reference

Magnetic resonance imaging (MRI) was used to evaluate and compare with anthropometry a fundamental bioelectrical impedance analysis (BIA) method for predicting muscle and adipose tissue composition in the lower limb. Healthy volunteers (eight men and eight women), aged 41 to 62 years, with mean (S.D.) body mass indices of 28.6 (5.4) kg/m(2) and 25.1 (5.4) kg/m(2) respectively, were subjected to MRI leg scans, from which 20-cm sections of thigh and IO-cm sections of lower leg (calf) were analysed for muscle and adipose tissue content, using specifically developed software. Muscle and adipose tissue were also predicted from anthropometric measurements of circumferences and skinfold thicknesses, and by use of fundamental BIA equations involving section impedance at 50 kHz and tissue-specific resistivities. Anthropometric assessments of circumferences, cross-sectional areas and volumes for total constituent tissues matched closely MRI estimates. Muscle volume was substantially overestimated (bias: thigh, -40%; calf, -18%) and adipose tissue underestimated (bias: thigh, 43%; calf, 8%) by anthropometry, in contrast to generally better predictions by the fundamental BIA approach for muscle (bias:thigh, -12%; calf, 5%) and adipose tissue (bias:thigh, 17%; calf, -28%). However, both methods demonstrated considerable individual variability (95% limits of agreement 20-77%). In general, there was similar reproducibility for anthropometric and fundamental BIA methods in the thigh (inter-observer residual coefficient of variation for muscle 3.5% versus 3.8%), but the latter was better in the calf (inter-observer residual coefficient of variation for muscle 8.2% versus 4.5%). This study suggests that the fundamental BIA method has advantages over anthropometry for measuring lower limb tissue composition in healthy individuals.

Designs for an asymmetric gradient set and a compact superconducting magnet for neural magnetic resonance imaging

Imaging of the head and neck is the most commonly performed clinical magnetic resonance imaging (MRI) examination [R. G. Evans and J. R. G. Evans, AJR 157, 603 (1991)]. This is usually undertaken in a generalist MRI instrument containing superconducting magnet system capable of imaging all organs. These generalist instruments are large, typically having a bore of 0.9-1.0 m and a length of 1.7-2.5 m and therefore are expensive to site, somewhat claustrophobic to the patient, and offer little access by attending physicians. In this article, we present the design of a compact, superconducting MRI magnet for head and neck imaging that is less than 0.8 m in length and discuss in detail the design of an asymmetric gradient coil set, tailored to the magnet profile. In particular, the introduction of a radio-frequency FM modulation scheme in concert with a gradient sequence allows the epoch of the linear region of the gradient set to be much closer to the end of the gradient structure than was previously possible. Images from a prototype gradient set demonstrate the effectiveness of the designs. (C) 1999 American Institute of Physics. [S0034-6748(99)04910-2].