MRI segmentation using dialectical optimization.

Biology, Psychology and Social Sciences are intrinsically connected to the very roots of the development of algorithms and methods in Computational Intelligence, as it is easily seen in approaches like genetic algorithms, evolutionary programming and particle swarm optimization. In this work we propose a new optimization method based on dialectics using fuzzy membership functions to model the influence of interactions between integrating poles in the status of each pole. Poles are the basic units composing dialectical systems. In order to validate our proposal we designed a segmentation method based on the optimization of k-means using dialectics for the segmentation of MR images. As a case study we used 181 MR synthetic multispectral images composed by proton density, T(1)- and T(2)-weighted synthetic brain images of 181 slices with 1 mm, resolution of 1 mm(3), for a normal brain and a noiseless MR tomographic system without field inhomogeneities, amounting a total of 543 images, generated by the simulator BrainWeb [2]. Our principal target here is comparing our proposal to k-means, fuzzy c-means, and Kohonen's self-organized maps, concerning the quantization error, we proved that our method can improved results obtained using k-means.

Evaluation of Alzheimer's disease by analysis of MR images using Objective Dialectical Classifiers as an alternative to ADC maps.

Alzheimer's disease is the most common cause of dementia, yet hard to diagnose precisely without invasive techniques, particularly at the onset of the disease. This work approaches image analysis and classification of synthetic multispectral images composed by diffusion-weighted (DW) magnetic resonance (MR) cerebral images for the evaluation of cerebrospinal fluid area and measuring the advance of Alzheimer's disease. A clinical 1.5 T MR imaging system was used to acquire all images presented. The classification methods are based on Objective Dialectical Classifiers, a new method based on Dialectics as defined in the Philosophy of Praxis. A 2-degree polynomial network with supervised training is used to generate the ground truth image. The classification results are used to improve the usual analysis of the apparent diffusion coefficient map.

Evaluation of Alzheimer's disease by analysis of MR images using multilayer perceptrons and Kohonen SOM classifiers as an alternative to the ADC maps.

Alzheimer's disease is the most common cause of dementia, yet hard to diagnose precisely without invasive techniques, particularly at the onset of the disease. This work approaches image analysis and classification of synthetic multispectral images composed by diffusion-weighted magnetic resonance (MR) cerebral images for the evaluation of cerebrospinal fluid area and measuring the advance of Alzheimer's disease. A clinical 1.5 T MR imaging system was used to acquire all images presented. The classification methods are based on multilayer perceptrons and Kohonen Self-Organized Map classifiers. We assume the classes of interest can be separated by hyperquadrics. Therefore, a 2-degree polynomial network is used to classify the original image, generating the ground truth image. The classification results are used to improve the usual analysis of the apparent diffusion coefficient map.

Manganese-enhanced magnetic resonance imaging (MEMRI) of rat brain after systemic administration of MnCl2: changes in T1 relaxation times during postnatal development.

PURPOSE: To measure regional T(1) changes in the postnatal rat brain following systemic administration of the contrast agent manganese chloride (MnCl(2)). MATERIALS AND METHODS: MnCl(2) (120 mM) was administered intravenously (i.v.) at 1.25 mL/hour to a dose of 175 mg/kg body weight. MRI experiments were performed on anaesthetized animals (32 male Wistar rats, postnatal days (PDs) 11, 16, 21, and 31) at 2.0 T. Regions of interest (ROIs) were drawn in sagittal slices and placed over five brain regions: olfactory bulb, cerebellum, cortex, thalamus, and hypothalamus. The signal intensities of each ROI were measured and fitted to a three-parameter function to estimate T(1) values. RESULTS: In the brains of animals who did not receive the contrast agent (control group), we observed a consistent age-dependent decrease in T(1) values. In the brains of manganese-infused animals (manganese group), however, T(1) values were significantly lower than in the control group, indicating the uptake of manganese, but no dependence of T(1) on age was found. CONCLUSION: Our T(1) measurements indicate that the relative Mn(2+) concentrations are higher in neonates and decrease with brain development. An estimate of the relative cortical concentration of manganese shows a two-fold drop from PD 11 to PD 31.

Concentration-dependent diffusivity and anomalous diffusion: a magnetic resonance imaging study of water ingress in porous zeolite.

Magnetic resonance imaging is employed to study water ingress in fine zeolite powders compacted by high pressure. The experimental conditions are chosen such that the applicability of Boltzmann's transformation of the one-dimensional diffusion equation is approximately satisfied. The measured moisture profiles indicate subdiffusive behavior with a spatiotemporal scaling variable eta=x/t(gamma/2) (0