Segmentation of Brain Tissue from Magnetic Resonance Images

Segmentation of medical imagery is a challenging problem due to the complexity of the images, as well as to the absence of models of the anatomy that fully capture the possible deformations in each structure. Brain tissue is a particularly complex structure, and its segmentation is an important step for studies in temporal change detection of morphology, as well as for 3D visualization in surgical planning. In this paper, we present a method for segmentation of brain tissue from magnetic resonance images that is a combination of three existing techniques from the Computer Vision literature: EM segmentation, binary morphology, and active contour models. Each of these techniques has been customized for the problem of brain tissue segmentation in a way that the resultant method is more robust than its components. Finally, we present the results of a parallel implementation of this method on IBM's supercomputer Power Visualization System for a database of 20 brain scans each with 256x256x124 voxels and validate those against segmentations generated by neuroanatomy experts.

Multiclass Classification of SRBCTs

A novel approach to multiclass tumor classification using Artificial Neural Networks (ANNs) was introduced in a recent paper cite{Khan2001}. The method successfully classified and diagnosed small, round blue cell tumors (SRBCTs) of childhood into four distinct categories, neuroblastoma (NB), rhabdomyosarcoma (RMS), non-Hodgkin lymphoma (NHL) and the Ewing family of tumors (EWS), using cDNA gene expression profiles of samples that included both tumor biopsy material and cell lines. We report that using an approach similar to the one reported by Yeang et al cite{Yeang2001}, i.e. multiclass classification by combining outputs of binary classifiers, we achieved equal accuracy with much fewer features. We report the performances of 3 binary classifiers (k-nearest neighbors (kNN), weighted-voting (WV), and support vector machines (SVM)) with 3 feature selection techniques (Golub's Signal to Noise (SN) ratios cite{Golub99}, Fisher scores (FSc) and Mukherjee's SVM feature selection (SVMFS))cite{Sayan98}.

Improving Multiclass Text Classification with the Support Vector Machine

We compare Naive Bayes and Support Vector Machines on the task of multiclass text classification. Using a variety of approaches to combine the underlying binary classifiers, we find that SVMs substantially outperform Naive Bayes. We present full multiclass results on two well-known text data sets, including the lowest error to date on both data sets. We develop a new indicator of binary performance to show that the SVM's lower multiclass error is a result of its improved binary performance. Furthermore, we demonstrate and explore the surprising result that one-vs-all classification performs favorably compared to other approaches even though it has no error-correcting properties.

Ameliorating the Overhead of Dynamic Optimization

Dynamic optimization has several key advantages. This includes the ability to work on binary code in the absence of sources and to perform optimization across module boundaries. However, it has a significant disadvantage viz-a-viz traditional static optimization: it has a significant runtime overhead. There can be performance gain only if the overhead can be amortized. In this paper, we will quantitatively analyze the runtime overhead introduced by a dynamic optimizer, DynamoRIO. We found that the major overhead does not come from the optimizer's operation. Instead, it comes from the extra code in the code cache added by DynamoRIO. After a detailed analysis, we will propose a method of trace construction that ameliorate the overhead introduced by the dynamic optimizer, thereby reducing the runtime overhead of DynamoRIO. We believe that the result of the study as well as the proposed solution is applicable to other scenarios such as dynamic code translation and managed execution that utilizes a framework similar to that of dynamic optimization.

CoDa-dendrogram: A new exploratory too

The use of orthonormal coordinates in the simplex and, particularly, balance coordinates, has suggested the use of a dendrogram for the exploratory analysis of compositional data. The dendrogram is based on a sequential binary partition of a compositional vector into groups of parts. At each step of a partition, one group of parts is divided into two new groups, and a balancing axis in the simplex between both groups is defined. The set of balancing axes constitutes an orthonormal basis, and the projections of the sample on them are orthogonal coordinates. They can be represented in a dendrogram-like graph showing: (a) the way of grouping parts of the compositional vector; (b) the explanatory role of each subcomposition generated in the partition process; (c) the decomposition of the total variance into balance components associated with each binary partition; (d) a box-plot of each balance. This representation is useful to help the interpretation of balance coordinates; to identify which are the most explanatory coordinates; and to describe the whole sample in a single diagram independently of the number of parts of the sample

Compositional amalgamations and balances: a critical approach

The amalgamation operation is frequently used to reduce the number of parts of compositional data but it is a non-linear operation in the simplex with the usual geometry, the Aitchison geometry. The concept of balances between groups, a particular coordinate system designed over binary partitions of the parts, could be an alternative to the amalgamation in some cases. In this work we discuss the proper application of both concepts using a real data set corresponding to behavioral measures of pregnant sows

One-sided jet at milliarcsecond scales in LS I +61˚ 303

We present Very Long Baseline Interferometry (VLBI) observations of the high mass X-ray binary LS I +61˚303, carried out with the European VLBI Network (EVN). Over the 11 hour observing run, performed ~10 days after a radio outburst, the radio source showed a constant flux density, which allowed sensitive imaging of the emission distribution. The structure in the map shows a clear extension to the southeast. Comparing our data with previous VLBI observations we interpret the extension as a collimated radio jet as found in several other X-ray binaries. Assuming that the structure is the result of an expansion that started at the onset of the outburst, we derive an apparent expansion velocity of 0:003 c, which, in the context of Doppler boosting, corresponds to an intrinsic velocity of at least 0:4 c for an ejection close to the line of sight. From the apparent velocity in all available epochs we are able to establish variations in the ejection angle which imply a precessing accretion disk. Finally we point out that LS I +61˚303, like SS 433 and Cygnus X-1, shows evidence for an emission region almost orthogonal to the relativistic jet

Exercise sheet 6

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Análisis de la depresión en el adulto mayor en la encuesta nacional de demografía y salud 2010

En Colombia como en otros países del mundo se está viviendo un proceso de transición epidemiológica y cada vez es más importante la población, aumentando el número de adultos mayores, en los que la depresión es una enfermedad que genera impacto en la morbililidad y mortalidad, y pocos estudios nacionales se han enfocado en analizar su comportamiento y sus factores asociados. Metodología: Estudio de corte transversal sobre los datos de la encuesta nacional de demografía y salud del año 2010 (ENDS 2010) que encuesto 50.000 hogares con 17.574 adultos mayores encuestados. Se realizó una regresión logística binaria y múltiple para establecer las variables asociadas a la depresión. Resultados: Se encontró una prevalencia global de 9,5%, siendo mayor para el sexo femenino (11,5%, OR =1,74), menor prevalencia de depresión a medida que aumenta la escolaridad (OR=0.14), mayor depresión en presencia de discapacidad (OR=14,21) y en las comorbilidades como enfermedades cerebrales (OR=2.09), cardiacas (OR=1.49), diabetes (OR=1.19), Alzheimer (OR=2,13) entre otras, no se encontró asociación con variables como edad, estado civil. Discusión: El comportamiento de la depresión en adultos mayores en la población estudiada es similar a la de otros países, se deben hacer estudios prospectivos y de intervención para establecer mejor las variables relacionadas.

Exam 2001

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