Spectral clustering independent component analysis for tissue classification from brain MRI

A spectral angle based feature extraction method, Spectral Clustering Independent Component Analysis (SC-ICA), is proposed in this work to improve the brain tissue classification from Magnetic Resonance Images (MRI). SC-ICA provides equal priority to global and local features; thereby it tries to resolve the inefficiency of conventional approaches in abnormal tissue extraction. First, input multispectral MRI is divided into different clusters by a spectral distance based clustering. Then, Independent Component Analysis (ICA) is applied on the clustered data, in conjunction with Support Vector Machines (SVM) for brain tissue analysis. Normal and abnormal datasets, consisting of real and synthetic T1-weighted, T2-weighted and proton density/fluid-attenuated inversion recovery images, were used to evaluate the performance of the new method. Comparative analysis with ICA based SVM and other conventional classifiers established the stability and efficiency of SC-ICA based classification, especially in reproduction of small abnormalities. Clinical abnormal case analysis demonstrated it through the highest Tanimoto Index/accuracy values, 0.75/98.8%, observed against ICA based SVM results, 0.17/96.1%, for reproduced lesions. Experimental results recommend the proposed method as a promising approach in clinical and pathological studies of brain diseases

Brain Tissue Classification from Multispectral MRI by Wavelet based Principal Component Analysis

In this paper, we propose a multispectral analysis system using wavelet based Principal Component Analysis (PCA), to improve the brain tissue classification from MRI images. Global transforms like PCA often neglects significant small abnormality details, while dealing with a massive amount of multispectral data. In order to resolve this issue, input dataset is expanded by detail coefficients from multisignal wavelet analysis. Then, PCA is applied on the new dataset to perform feature analysis. Finally, an unsupervised classification with Fuzzy C-Means clustering algorithm is used to measure the improvement in reproducibility and accuracy of the results. A detailed comparative analysis of classified tissues with those from conventional PCA is also carried out. Proposed method yielded good improvement in classification of small abnormalities with high sensitivity/accuracy values, 98.9/98.3, for clinical analysis. Experimental results from synthetic and clinical data recommend the new method as a promising approach in brain tissue analysis.

A semi-automatic method for segmentation and 3D modeling of glioma tumors from brain MRI

This work presents an efficient method for volume rendering of glioma tumors from segmented 2D MRI Datasets with user interactive control, by replacing manual segmentation required in the state of art methods. The most common primary brain tumors are gliomas, evolving from the cerebral supportive cells. For clinical follow-up, the evaluation of the pre- operative tumor volume is essential. Tumor portions were automatically segmented from 2D MR images using morphological filtering techniques. These seg- mented tumor slices were propagated and modeled with the software package. The 3D modeled tumor consists of gray level values of the original image with exact tumor boundary. Axial slices of FLAIR and T2 weighted images were used for extracting tumors. Volumetric assessment of tumor volume with manual segmentation of its outlines is a time-consuming proc- ess and is prone to error. These defects are overcome in this method. Authors verified the performance of our method on several sets of MRI scans. The 3D modeling was also done using segmented 2D slices with the help of a medical software package called 3D DOCTOR for verification purposes. The results were validated with the ground truth models by the Radi- ologist.

Molecular mechanisms controlling the precision of chemokine guided cell migration

By the end of the first day of embryonic development, zebrafish primordial germ cells (PGCs) arrive at the site where the gonad develops. In our study we investigated the mechanisms controlling the precision of primordial germ cell arrival at their target. We found that in contrast with our expectations which were based on findings in Drosophila and mouse, the endoderm does not constitute a preferred migration substrate for the PGCs. Rather, endoderm derivatives are important for later stages of organogenesis keeping the PGC clusters separated. It would be interesting to investigate the precise mechanism by which endoderm controls germ cell position in the gonad. In their migration towards the gonad, zebrafish germ cells follow the gradient of chemokine SDF-1a, which they detect using the receptor CXCR4b that is expressed on their membrane. Here we show that the C-terminal region of CXCR4b is responsible for down-regulation of receptor activity as well as for receptor internalization. We demonstrate that receptor molecules unable to internalize are less potent in guiding germ cells to the site where the gonad develops, thereby implicating chemokine receptor internalization in facilitating precision of migration during chemotaxis in vivo. We demonstrate that while CXCR4b activity positively regulates the duration of the active migration phases, the down-regulation of CXCR4b signalling by internalization limits the duration of this phase. This way, receptor signalling contributes to the persistence of germ cell migration, whereas receptor down-regulation enables the cells to stop and correct their migration path close to the target where germ cells encounter the highest chemokine signal. Chemokine receptors are involved in directing cell migration in different processes such as lymphocyte trafficking, cancer and in the development of the vascular system. The C-terminal domain of many chemokine receptors was shown to be essential for controlling receptor signalling and internalization. It would therefore be important to determine whether the role for receptor internalization in vivo as described here (allowing periodical corrections to the migration route) and the mechanisms involved (reducing the level of signalling) apply for those other events, too.

A concurrent region growing algorithm guided by circumscribed contours

Image segmentation of natural scenes constitutes a major problem in machine vision. This paper presents a new proposal for the image segmentation problem which has been based on the integration of edge and region information. This approach begins by detecting the main contours of the scene which are later used to guide a concurrent set of growing processes. A previous analysis of the seed pixels permits adjustment of the homogeneity criterion to the region's characteristics during the growing process. Since the high variability of regions representing outdoor scenes makes the classical homogeneity criteria useless, a new homogeneity criterion based on clustering analysis and convex hull construction is proposed. Experimental results have proven the reliability of the proposed approach

Segmentació de teixit i lesions en imatges de MRI de malalts d'esclerosi múltiple

Actualment, en l'àmbit mèdic, la ressonància magnètica, MRI Magnetic Resonance Imaging, és un dels sistemes més utilitzats per a la realització de diagnòstics i el seguiment de l'evolució de malalties com l'esclerosi múltiple (EM). No obstant, la gran quantitat d'informació que proporciona aquesta modalitat té com a conseqüència una tasca feixuga d'anàlisi i d'interpretació per part dels radiòlegs i neuròlegs. L'objectiu general d'aquest projecte és desenvolupar un sistema per ajudar als metges a segmentar les imatges de MRI del cervell. S'ha implementat amb MATLAB. Durant tot el procés s'han utilitzat dades sintètiques, de la base de dades simulada BrainWeb, i reals, proporcionades pels grup de metges col•laboradors amb el grup VICOROB. El projecte s'emmarca dins d'un projecte de recerca del grup de Visió per Computador i Robòtica de la Universitat de Girona

How to assess, plan and teach shared and guided reading and writing 1 : key stage one, primary 1-3.

El desarrollo de la alfabetización infantil se inicia desde el momento en que los padres, hablan, cantan y leen a sus bebés. Estas buenas experiencias son las bases sobre las que seguir, pues, después, aprenden a jugar con los libros, a disfrutar con sus imágenes y sus páginas, a imitar a los adultos en la lectura, a garabatear y a escribir como ellos. Aunque, algunos niños hayan carecido de estas experiencias en su hogar, al incorporarse a la escuela, es necesario darles todas las oportunidades posibles para observar a sus compañeros lectores y escritores, interactuar con los libros y experimentar ellos mismos con la lectura y la escritura.

How to assess, plan and teach shared and guided reading and writing 2 : key stage two, primary 4-7.

En esta etapa del desarrollo de la alfabetización, la mayoría de los niños han comprendido sus conceptos fundamentales y ya saben hablar, leer y escribir para distintos tipos de oyentes y para distintos propósitos. Para la consolidación de estos conocimientos y la adquisición de nuevas habilidades necesitan contar con una amplia gama de textos y contextos, aprender a decodificar palabras desconocidas, deletrear palabras difíciles. El modelo de lectura y escritura compartida y guiada es la estrategia de enseñanza más idónea.

Guided reading : teacher's workbook.

Es un módulo, de carácter interactivo, en el que se recomienda que no haya más de doce participantes, para que cada uno tenga la oportunidad de expresar sus pensamientos, preguntas e investigaciones en el aula. Lectura guiada es una técnica de instrucción y evaluación que apoya y fomenta el desarrollo de estrategias de lectura independiente. El grupo debe estar formado por cuatro o seis niños, cada uno de los cuales tiene una copia del libro a leer y su actividad debe de ser la de dar sentido de forma independiente al texto, con el apoyo de los demás miembros del grupo y del profesor. El apoyo del docente es breve, y su actitud es observar, afirmar y responder a las necesidades de los niños e invitarles a utilizar los recursos y estrategias de los que disponen.

Guided reading : course co-ordinator's book.

Es un módulo, de carácter interactivo, sobre 'lectura guiada' en el que se recomienda que no haya más de doce participantes, uno de los cuales es el responsable de la organización de las sesiones. Lectura guiada es una técnica de instrucción y evaluación que apoya y fomenta el desarrollo de estrategias de lectura independiente. El responsable del módulo propone las actividades que se realizan en las sesiones para las diferentes etapas en los primeros años de escolarización, los materiales y equipos que serán necesarios para apoyar cada período de sesiones,las sugerencias de textos de ficción y no ficción que los participantes apoyan para las actividades de lectura guiada, y por último,cómo llevar a cabo las sesiones de apoyo y las tareas y actividades.

Book bands for guided reading : organising key stage one texts for the literacy hour.

Ha sido pensado para ayudar a los profesores a revisar, organizar y completar en las escuelas, las existencias de materiales de lectura para la etapa uno (Key Stage uno). También, proporciona las bases para establecer los procedimientos de lectura guiada en la etapa de alfabetización infantil, y para indicar cómo la lectura guiada encaja dentro de la National Literacy Strategy Framework for Teaching (Estrategia Nacional de Alfabetización Marco para la Enseñanza). Se reunieron casi cuatro mil libros para los niños, que se han agrupado teniendo en cuenta la gradación de dificultad de los textos en diez bloques, desde los más sencillos para los primeros lectores a áquellos utilizados al final de la etapa uno (Key stage uno) para lectores con más fluidez.

A Monte Carlo-Based Fiber Tracking Algorithm using Diffusion Tensor MRI

Diffusion tensor magnetic resonance imaging, which measures directional information of water diffusion in the brain, has emerged as a powerful tool for human brain studies. In this paper, we introduce a new Monte Carlo-based fiber tracking approach to estimate brain connectivity. One of the main characteristics of this approach is that all parameters of the algorithm are automatically determined at each point using the entropy of the eigenvalues of the diffusion tensor. Experimental results show the good performance of the proposed approach

Visualitzation and processing of diffusion tensor MRI

Diffusion Tensor Imaging (DTI) is a new magnetic resonance imaging modality capable of producing quantitative maps of microscopic natural displacements of water molecules that occur in brain tissues as part of the physical diffusion process. This technique has become a powerful tool in the investigation of brain structure and function because it allows for in vivo measurements of white matter fiber orientation. The application of DTI in clinical practice requires specialized processing and visualization techniques to extract and represent acquired information in a comprehensible manner. Tracking techniques are used to infer patterns of continuity in the brain by following in a step-wise mode the path of a set of particles dropped into a vector field. In this way, white matter fiber maps can be obtained.