Débora Christina Muchaluat Saade

A Bayesian network decision model for supporting the diagnosis of dementia, Alzheimer's disease and mild cognitive impairment

Population aging has been occurring as a global phenomenon with heterogeneous consequences in both developed and developing countries. Neurodegenerative diseases, such as Alzheimer׳s Disease (AD), have high prevalence in the elderly population. Early diagnosis of this type of disease allows early treatment and improves patient quality of life. This paper proposes a Bayesian network decision model for supporting diagnosis of dementia, AD and Mild Cognitive Impairment (MCI). Bayesian networks are well-suited for representing uncertainty and causality, which are both present in clinical domains. The proposed Bayesian network was modeled using a combination of expert knowledge and data-oriented modeling. The network structure was built based on current diagnostic criteria and input from physicians who are experts in this domain. The network parameters were estimated using a supervised learning algorithm from a dataset of real clinical cases. The dataset contains data from patients and normal controls from the Duke University Medical Center (Washington, USA) and the Center for Alzheimer׳s Disease and Related Disorders (at the Institute of Psychiatry of the Federal University of Rio de Janeiro, Brazil). The dataset attributes consist of predisposal factors, neuropsychological test results, patient demographic data, symptoms and signs. The decision model was evaluated using quantitative methods and a sensitivity analysis. In conclusion, the proposed Bayesian network showed better results for diagnosis of dementia, AD and MCI when compared to most of the other well-known classifiers. Moreover, it provides additional useful information to physicians, such as the contribution of certain factors to diagnosis.

Image registration using genetic algorithms

This paper addresses the image registration problem applying genetic algorithms. The image registrations objective is the definition of a mapping that best match two set of points or images. In this work the point matching problem was addressed employing a method based on nearest-neighbor. The mapping was handled by affine transformations. Experiments were conducted using three 2D synthetic point-sets with different affine transformations and noise. The results were compared against other optimization techniques. The similarity of two point-sets is measured using the Euclidean distance between matched points.

A new measure for comparing biomedical regions of interest in segmentation of digital images

The segmentation of the region of interest (ROI) of digital images is generally the first step in the pattern recognition (PR) procedure. Automatic segmentation of biomedical images is desirable and comparisons among new approaches, by using available databases, are important. We present a new approach to compute the Hausdorff distance (HD) between digital images. Although HD is the most used distance estimator among sets, we show why it is not suitable for biomedical applications. In this paper, a new technique to define the degree of correction of the ROI is developed to serve as a basis for the comparisons used to validate works on segmentation of biomedical images. As for online diagnosis, the comparison among possible techniques must be efficient enough to: (1) be done in real time (i.e. during the examination), (2) allow the inclusion of priority aspects, and (3) be intuitive and simple enough to be easily followed by people with no computational or mathematical background. We develop a new index by considering the expectations of the medical doctors who are using computer systems for diagnostic aids, and take into consideration how these systems use ROIs to extract feature properties from the examinations. We discuss conditions for empirically defining a measure for calculating similarities and differences between ROIs. The proposed method is applied to both real and simulated data examples.

XTemplate 3.0: adding semantics to hypermedia compositions and providing document structure reuse

Hypermedia composite templates define generic structures of nodes and links that can be reused in different document compositions. The XTemplate language is an XML-based solution for defining composite templates for hypermedia documents in order to embed semantics into a composition that does not have it in prior. The use of templates intend to facilitate the authoring of interactive applications in Digital TV systems, as long as IPTV systems. XTemplate 3.0 extends the previous XTemplate versions, incorporating new features to the language and increasing its expressiveness. As an application of XTemplate, this work extends NCL (Nested Context Language) with XTemplate, adding semantics to NCL contexts and providing document structure reuse.

Automated Segmentation of the Corpus Callosum Midsagittal Surface Area

The non-invasive in vivo nature of magnetic resonance imaging (MRI) makes it the modality of choice of many neuroanatomical imaging studies. This paper discusses automatic brain structure segmentation based on previous knowledge on statistical models. The method is validated by an experiment involving magnetic resonance images acquired from 20 healthy adult individuals (10 men and 10 women). The results provide normative data of the midsagittal surface area of the corpus callosum from a 46-55 years old range group, splitting results by gender. Our results were also compared with data obtained from other authors, validating the correlation between brain volume and the area of this structure. The final goal of this work is computer-aided diagnosis for brain diseases.

Automatic Segmentation of Brain Structures Based on Anatomic Atlas

The non-invasive in vivo nature of magnetic resonance imaging (MRI) makes it the modality of choice of many neuroanatomical imaging studies. This paper discusses automatic brain structure segmentation based on anatomic atlas. Our goal is to use image-processing algorithms and previous knowledge statistical models for segmentation and labeling of brain regions in order to support radiologists to make clinical diagnosis. Practical experiments show the results of brain tissue classification process and automatic region labeling in order to segment accurately the hippocampus and measure its volume. Hippocampus volumetric information can be useful to evaluate patients with Alzheimers disease. The final goal of this work is computer-aided diagnosis for brain diseases.

STEVE: Spatial-Temporal View Editor for Authoring Hypermedia Documents

Interactive multimedia applications are available in many platforms such as smartphones, computers and digital TVs. In addition, the production of multimedia content has been growing increasingly and facilitated due to easier access to these devices. In this scenario, the creation of multimedia applications has gained importance. There are several commercial tools that allow building multimedia presentations using the timeline paradigm for users with no programming knowledge. However, these tools inherit the timeline authoring paradigm limitations. In order to facilitate hypermedia document authoring for users with no knowledge of program- ming and avoid the timeline paradigm limitations, this paper proposes an event-based hypermedia document model and a graphical editor, which is based on this model, for spatio-temporal view editing of a document. The proposed tool is called STEVE, Spatio-Temporal View Editor, which sup- ports the definition of viewer interactions. Besides, STEVE exports hypermedia applications to NCL and HTML5 documents to accomplish different execution platforms.

XTemPlate 3.0 language: easing the authoring of NCL programs for interactive digital TV

This paper presents the XTemplate 3.0 language as a solution for defining composite templates to NCL 3.0 documents. Hypermedia composite templates define nodes and links structures that can be reused in different NCL 3.0 document contexts, facilitating the authoring of hypermedia documents used in the Brazilian digital TV system interactive content creation. The XTemplate 3.0 language specification was made using a modular approach in XML Schema according to the W3C standard. With this modular approach, the specification becomes simpler to maintain and more flexible to implement, besides the possibility to create XTemplate 3.0 profiles. XTemplate 3.0 extends the previous XTemplate versions, incorporating new features to the language.

Clinical dementia rating score prediction based on MR segmentation

This work aims at predicting the clinical dementia rating (CDR) score with a fully automated human brain volumetric segmentation method based on anatomical atlas using magnetic resonance (MR) images. The CDR prediction method uses a Bayesian classifier considering 371 individuals. Practical results were assessed using the classifier true-positive rate. CDR score prediction can indicate an underlying neurodegenerative disorder, such as Alzheimerpsilas disease. Its early detection allows precocious therapeutic intervention and better clinical results.

Analysis of energy efficient OLSR extensions and OLSR-EXT energetic optimization proposal

Energy efficiency is a key issue for ad-hoc networks and several proposals have been explored in order to maximize network lifetime. This work analyzes the most relevant modifications proposed in the literature for OLSR in order to make it energy efficient and compares their performance results in terms of throughput, network lifetime and node energy level. Additionally, this paper proposes an extension to OLSR-ETX, named ETX-EMPR, which provides a longer network lifetime.