Medical image analysis
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Medical image analysis · Jun 2009
Automated classification of fMRI data employing trial-based imagery tasks.
Automated interpretation and classification of functional MRI (fMRI) data is an emerging research field that enables the characterization of underlying cognitive processes with minimal human intervention. In this work, we present a method for the automated classification of human thoughts reflected on a trial-based paradigm using fMRI with a significantly shortened data acquisition time (less than one minute). Based on our preliminary experience with various cognitive imagery tasks, six characteristic thoughts were chosen as target tasks for the present work: right-hand motor imagery, left-hand motor imagery, right foot motor imagery, mental calculation, internal speech/word generation, and visual imagery. ⋯ Extracted feature vectors were classified using the support vector machine (SVM) algorithm. Parameter optimization, using a k-fold cross validation scheme, allowed the successful recognition of the six different categories of administered thought tasks with an accuracy of 74.5% (mean)+/-14.3% (standard deviation) across all five subjects. Our proposed study for the automated classification of fMRI data may be utilized in further investigations to monitor/identify human thought processes and their potential link to hardware/computer control.
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Medical image analysis · Jun 2009
Comparison of regularization methods for human cardiac diffusion tensor MRI.
Diffusion tensor MRI (DT-MRI) is an imaging technique that is gaining importance in clinical applications. However, there is very little work concerning the human heart. When applying DT-MRI to in vivo human hearts, the data have to be acquired rapidly to minimize artefacts due to cardiac and respiratory motion and to improve patient comfort, often at the expense of image quality. ⋯ Experiments on synthetic data show that, for high signal-to-noise ratio (SNR), the methods operating on DW images produce the best results; they substantially reduce noise error propagation throughout the diffusion calculations. However, when the SNR is low, Rician Cholesky and Log-Euclidean DT regularization methods handle the bias introduced by Rician noise and ensure symmetry and positive definiteness of the tensors. Results based on a set of sixteen ex vivo human hearts show that the different regularization methods tend to provide equivalent results.
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Medical image analysis · Jun 2009
Atlas-based segmentation of 3D cerebral structures with competitive level sets and fuzzy control.
We propose a novel approach for the simultaneous segmentation of multiple structures with competitive level sets driven by fuzzy control. To this end, several contours evolve simultaneously toward previously defined anatomical targets. A fuzzy decision system combines the a priori knowledge provided by an anatomical atlas with the intensity distribution of the image and the relative position of the contours. ⋯ This leads to a local expansion or contraction of the contours, in order to match the boundaries of their respective targets. Two applications are presented: the segmentation of the brain hemispheres and the cerebellum, and the segmentation of deep internal structures. Experimental results on real magnetic resonance (MR) images are presented, quantitatively assessed and discussed.
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Medical image analysis · Apr 2009
Phase unwrapping of MR images using Phi UN--a fast and robust region growing algorithm.
We present a fully automated phase unwrapping algorithm (Phi UN) which is optimized for high-resolution magnetic resonance imaging data. The algorithm is a region growing method and uses separate quality maps for seed finding and unwrapping which are retrieved from the full complex information of the data. ⋯ Phi UN, however, was significantly faster at low signal to noise ratio (SNR) and data with a more complex phase topography, making it particularly suitable for applications with low SNR and high spatial resolution. Phi UN is freely available to the scientific community.
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Medical image analysis · Apr 2009
A fast, model-independent method for cerebral cortical thickness estimation using MRI.
Several algorithms for measuring the cortical thickness in the human brain from MR image volumes have been described in the literature, the majority of which rely on fitting deformable models to the inner and outer cortical surfaces. However, the constraints applied during the model fitting process in order to enforce spherical topology and to fit the outer cortical surface in narrow sulci, where the cerebrospinal fluid (CSF) channel may be obscured by partial voluming, may introduce bias in some circumstances, and greatly increase the processor time required. In this paper we describe an alternative, voxel based technique that measures the cortical thickness using inversion recovery anatomical MR images. ⋯ The method was applied to 119 normal volunteers, and validated through extensive comparisons with published measurements of both cortical thickness and rate of thickness change with age. We conclude that the proposed technique is generally faster than deformable model-based alternatives, and free from the possibility of model bias, but suffers no reduction in accuracy. In particular, it will be applicable in data sets showing severe cortical atrophy, where thinning of the gyri leads to points of high curvature, and so the fitting of deformable models is problematic.