Thiago Moraes

InVesalius: An Interactive Rendering Framework for Health Care Support

This work presents InVesalius, an open-source software for analysis and visualization of medical images. The tool has supported several surgeries in hospitals and has been downloaded from more than a hundred countries around the world. Its main characteristics, aspects of implementation, and applications in areas such as image segmentation, mesh generation, volume rendering, and 3D printing of anatomic models are described.

Electroencephalogram signal classification based on shearlet and contourlet transforms

Detection of epilepsy patterns in EEG signals with high accuracy.Development of a novel methodology based on curvelet and shearlet transforms.Extraction of a set of discriminative characteristics from the signals.Evaluation on a public data set.Results superior/comparable to the literature. Epilepsy is a disorder that affects approximately 50 million people of all ages, according to World?Health Organization?(2016), which makes it one of the most common neurological diseases worldwide. Electroencephalogram (EEG) signals have been widely used to detect epilepsy and other brain abnormalities. In this work, we propose and evaluate a novel methodology based on shearlet and contourlet transforms to decompose the EEG signals into frequency bands. A set of features are extracted from these time-frequency coefficients and used as input to different classifiers. Experiments are conducted on a public data set to demonstrate the effectiveness of the proposed classification method. The developed system can help neurophysiologists identify EEG patterns in epilepsy diagnostic tasks.

3D Adaptive Histogram Equalization Method for Medical Volumes.

Medical imaging plays a fundamental role in the diagnosis and treatment of several diseases, enabling the visualization of internal organs and tissues for use in clinical procedures. The quality of medical images can be degraded by several factors, such as noise and poor contrast. The application of filtering and contrast enhancement techniques is usually necessary to improve the quality of images, which facilitates the segmentation and classification stages. In this paper, we develop and analyze a novel three-dimensional adaptive histogram equalization method for improving contrast in the context of medical imaging. Several data sets are used to demonstrate the effectiveness of the proposed approach.

Isosurface rendering of medical images improved by automatic texture mapping

Advances in medical imaging modalities have allowed the acquisition of large collections of volumetric images for clinical and training purposes, such as Computerized Tomography, Magnetic Resonance...

Shearlet and contourlet transforms for analysis of electrocardiogram signals

BACKGROUND AND OBJECTIVE Cardiac arrhythmia is an abnormal variation in the heart electrical activity that affects millions of people worldwide. Electrocardiogram (ECG) signals have been widely used to assess and diagnose cardiac abnormalities. METHODS A novel methodology based on shearlet and contourlet transforms for automatically classify an input ECG signal into different heart beat types is proposed and evaluated in this work. Classifiers are trained through a set of features extracted from these time-frequency coefficients. RESULTS Tests are conducted on MIT-BIH data set to demonstrate the effectiveness of the proposed classification method. The shearlet and contourlet transforms achieved high classification accuracy rates. CONCLUSIONS The developed system can help cardiologists obtain structural and functional information of the heart by means of ECG patterns, improving their diagnostic tasks.

Out-of-Core Progressive Web-Based Rendering of Triangle Meshes

The visualization of large volumes of data has been explored in several knowledge domains, such as remote sensing, medicine, meteorology, biology, among others. In traditional data visualization techniques, data is stored, processed and rendered locally on the client machine, which may require expensive computational resources in terms of storage space and processing power. This work presents and discusses a methodology for out-of-core remote rendering of large three-dimensional triangles meshes. Users are able to interact with the developed visualization tool through requests sent to a server by directly manipulating the data volumes on their own Web browser.

Adaptive Filtering Techniques for Improving Hyperspectral Image Classification

Hyperspectral imaging sensors have been used to collect data for various applications, such as medicine, physics, archaeology, remote sensing, astronomy, geosciences, surveillance, among others. This work investigates the use of several local filtering techniques for improving the classification of hyperspectral images. Spectral-spatial features are extracted from the pixel values present in the image bands. A classifier is applied to each pixel of the image to determine its class. Experiments on hyperspectral images are conducted to show the effectiveness of the proposed method.