John Hebert da Silva Felix

Continuous Positive Airway Pressure Effects on Regional Lung Aeration in Patients With COPD: A High-Resolution CT Scan Study

BACKGROUND The effects of nasal continuous positive airway pressure (CPAP) on the lung parenchyma of patients with COPD, to our knowledge, have never been assessed by high-resolution CT (HRCT) scanning. METHODS HRCT scans were obtained at the apex, hilum, and basis of the lungs at functional residual capacity while on spontaneous respiration and at the end of CPAP trials of 5 cm water (H(2)O), 10 cm H(2)O, and 15 cm H(2)O in 11 stable patients with COPD and eight healthy volunteers. Lung aeration was assessed by quantitative density parameters and by qualitative analysis of each CT image after processing by means of a density-based color-mask computational algorithm. The quantitative parameters were density histograms, the relative area of the lungs with attenuation values < -950 Hounsfield units (percentage of hyperaerated areas) and the 15th percentile (the density value separating the 15% voxels of least density). RESULTS A CPAP of 5 cm H(2)O caused little increase in lung aeration in both groups, but in some patients with COPD, CPAP deflated some regions of the lungs. CPAP levels of 10 cm H(2)O and 15 cm H(2)O increased the emphysematous zones in all sectors of the lungs, including dorsal and apical regions in patients with COPD compared to little hyperaeration predominantly in the ventral areas in healthy volunteers. CONCLUSIONS Nasal CPAP causes variable effects on regional lung aeration in relation to the applied pressure and the regional distribution of emphysema in patients with COPD. Low pressure levels may cause regional lung deflation in some patients. High levels increase the emphysematous areas wherever they are located inside the lungs.

pSnakes: A new radial active contour model and its application in the segmentation of the left ventricle from echocardiographic images

Active contours are image segmentation methods that minimize the total energy of the contour to be segmented. Among the active contour methods, the radial methods have lower computational complexity and can be applied in real time. This work aims to present a new radial active contour technique, called pSnakes, using the 1D Hilbert transform as external energy. The pSnakes method is based on the fact that the beams in ultrasound equipment diverge from a single point of the probe, thus enabling the use of polar coordinates in the segmentation. The control points or nodes of the active contour are obtained in pairs and are called twin nodes. The internal energies as well as the external one, Hilbertian energy, are redefined. The results showed that pSnakes can be used in image segmentation of short-axis echocardiogram images and that they were effective in image segmentation of the left ventricle. The echo-cardiologists golden standard showed that the pSnakes was the best method when compared with other methods. The main contributions of this work are the use of pSnakes and Hilbertian energy, as the external energy, in image segmentation. The Hilbertian energy is calculated by the 1D Hilbert transform. Compared with traditional methods, the pSnakes method is more suitable for ultrasound images because it is not affected by variations in image contrast, such as noise. The experimental results obtained by the left ventricle segmentation of echocardiographic images demonstrated the advantages of the proposed model. The results presented in this paper are justified due to an improved performance of the Hilbert energy in the presence of speckle noise.

Avaliação computacional de enfisema pulmonar em TC: comparação entre um sistema desenvolvido localmente e um sistema de uso livre

OBJECTIVE: To present a locally developed system of computer vision for use with HRCT images, designated SIStema para a Deteccao e a quantificacao de Enfisema Pulmonar (SISDEP, System to Detect and Quantify Pulmonary Emphysema), and to compare this system with a freeware system tool. METHODS: Thirty-three HRCT images scanned at the apex, hilum and base of the lungs of 11 patients with COPD were analyzed. The SISDEP was compared with the Osiris Medical Imaging Software Program regarding lung parenchyma segmentation, precision of the measurement of the cross-sectional area of the lungs in mm2, mean lung density (MLD), relative area (RA) of the lung occupied by voxels with attenuation values < -950 hounsfield units (ra -950), 15th percentile point (perc15) and visualization of hyperinflated areas using a color mask. RESULTS: Although both computational systems were efficient in segmenting the lungs, the SISDEP performed this task automatically and more rapidly. There were significant correlations between the two systems in terms of the results obtained for lung cross-sectional area, MLD, RA -950 and Perc15 (r2 = 0.99, 0.99, 0.99 and 1.00, respectively). The color mask tool of the SISDEP allowed excellent visualization of hyperinflated areas, discriminating them from normal areas. CONCLUSIONS: The SISDEP was efficient in segmenting the lungs and quantifying lung hyperinflation, presenting an excellent correlation with the Osiris system. The SISDEP constitutes a promising computational tool for diagnosing and assessing the progression of emphysema in HRCT images of COPD patients.

Original ResearchCOPDContinuous Positive Airway Pressure Effects on Regional Lung Aeration in Patients With COPD: A High-Resolution CT Scan Study

BACKGROUND The effects of nasal continuous positive airway pressure (CPAP) on the lung parenchyma of patients with COPD, to our knowledge, have never been assessed by high-resolution CT (HRCT) scanning. METHODS HRCT scans were obtained at the apex, hilum, and basis of the lungs at functional residual capacity while on spontaneous respiration and at the end of CPAP trials of 5 cm water (H(2)O), 10 cm H(2)O, and 15 cm H(2)O in 11 stable patients with COPD and eight healthy volunteers. Lung aeration was assessed by quantitative density parameters and by qualitative analysis of each CT image after processing by means of a density-based color-mask computational algorithm. The quantitative parameters were density histograms, the relative area of the lungs with attenuation values < -950 Hounsfield units (percentage of hyperaerated areas) and the 15th percentile (the density value separating the 15% voxels of least density). RESULTS A CPAP of 5 cm H(2)O caused little increase in lung aeration in both groups, but in some patients with COPD, CPAP deflated some regions of the lungs. CPAP levels of 10 cm H(2)O and 15 cm H(2)O increased the emphysematous zones in all sectors of the lungs, including dorsal and apical regions in patients with COPD compared to little hyperaeration predominantly in the ventral areas in healthy volunteers. CONCLUSIONS Nasal CPAP causes variable effects on regional lung aeration in relation to the applied pressure and the regional distribution of emphysema in patients with COPD. Low pressure levels may cause regional lung deflation in some patients. High levels increase the emphysematous areas wherever they are located inside the lungs.

Identification and Quantification of Pulmonary Emphysema through Pseudocolors

Chronic Obstructive Pulmonary Disease (COPD) is a world health problem with high morbidity and mortality. High-Resolution Computed Tomography (HRCT), is an excellent tool for early detection of emphysema component of COPD. Despite this fact, HRCT presents limitations inherent to the subjective analysis of the gray scale image that directly compromises the accuracy for both diagnosis and precise determination of the disease extension. The objective of this paper is present a colored mask algorithm (CMA) to identify and quantify the emphysema, enhancing its visualization through pseudocolors. We studied 21 images of 7 patients with COPD and 1 healthy volunteer. The CMA applies colors to the segmented lungs according to pre-defined ranges of Hounsfield units. CMA automatically calculates the relative area occupied by tomographic densities within the pre-defined ranges, allowing precise quantification of diseased and normal parenchyma. Future works are needed in order to validate the incorporation of the CMA in the image assessment of emphysema in COPD patients.

Automatic Segmentation and Measurement of the Lungs in healthy persons and in patients with Chronic Obstructive Pulmonary Disease in CT Images

Nowadays, Computed Tomography (CT) of the thorax is the most accurate image technique for the diagnosis of the majority of the lung and chest diseases. Despite of this fact there are still limitations of CT in diagnosing and specially quantifying lung diseases such as emphysema. The automatic segmentation and measurement of the lungs and thoracic structures can improve by image processing techniques. These techniques enhance the visualization of the lungs and the chest wall. The present paper presents a method of automatic classification capable to segment and measure the lungs and the thoracic cavity both in healthy volunteers and in patients with Chronic Obstructive Pulmonary Disease (COPD) in prone positions based on technique of region growing. With the region growing method, based on computer programs, it is possible to segment and measure the aerated lung and the thoracic cavity.

An OCR System for Numerals Applied to Energy Meters

This work describes a prototype of an OCR (Optical Character Recognition) system designed for reading digits of power measurement devices, using Artificial Neural Networks. The motivation for this work is the implementation of an alternative automatic measurement system to be used in a prepaid power system - SEPPRA. Prototype software using Computer Vision techniques and pattern recognition through Neural Networks is implemented in the C++/Windows platform. Considering this application, adaptive threshold methods are compared in order to choose the more appropriated algorithm of binarization. Several algorithms are implemented and evaluated under different conditions of zoom and camera focus. The system works satisfactorily and can be carried to other platforms, making possible its production in commercial scale.

Radial snakes

To perform a comparative study of methods based on radial active contour methods.To evaluate the new method pSnakes.To apply at the segmentation of noisy image with different levels of noise.To apply Hilbert transform to calculate the external energy used on radial snakes. There has been a growing use of digital image processing since the 90s. To process an image it must be transformed successively in order to extract information more easily. The first steps in an image analysis are the acquisition followed by the preprocessing to prepare the image for the next step. This step is called image segmentation which is the process of separating different regions of the image according to their properties. The segmentation process is fundamental for all image analyses, as the final result is essentially dependent on the quality of the segmentation. Highlighted among these techniques are the active contour systems, known as snakes. The active contour methods can be subdivided in two main groups: two-dimensional search (traditional) and one-dimensional search (radial). The radial active contours were developed in order to obtain a smaller computational cost. The aim of this work was to study, evaluate and compare algorithms of radial active contours in synthetic noisy images and thus identify the advantages and disadvantages of each method in order to point out the most appropriate method for a given application. This work makes a quantitative and qualitative comparison of three methods: Traditional Radial Snakes, Hilbert Radial Snakes and pSnakes. The results of this research are suitable for academic research as they show that the recently developed pSnakes method is effective in image segmentation with noise. This paper also considered the processing time of the different methods.

AUTOIN: Method of Automatic Initialization of Active Contours Applied to Lungs in CT Images

In this work we present a new method of automatic initialization for the active contours method denominated AUTOIN. This method is applied in 72 computed tomography images of the lungs of healthy volunteers and ill patients. AUTOIN is compared to the method of object center localization. The accuracy obtained is calculated using the average and standard deviation of the distance (mm) between the points localized in each method. The results of AUTOIN for healthy volunteers are for the right lung mean of 12.3mm and standard deviation of 17.9mm, and for the left one mean of 11.7mm and 16.7mm of standard deviation. For the ill patients, the results are mean of 15.8mm and standard deviation of 20.4 mm for the right lung and mean of 14.6mm and 21.3mm of standard deviation for the left one. We conclude that AUTOIN enables to perform automatic definition of the initial contour inside the lungs.

Evaluating Gaussian and Rayleigh-Based Mathematical Models for T and P-waves in ECG

This paper presents a comparative study of modelling and segmentation of P and T waves in electrocardiograms, using three different mathematical models: Gaussian function, a composition of two Gaussian functions and Rayleigh probability density function (Rayleigh pdf). In order to evaluate the adaptability and the matching degree between each model and each characteristic wave, we compute the normalized root mean square (RMS) error, as well as the evolution of the fitting parameters related to each kernel throughout ECG records from the well-known QT database. Concerning the delineation of P and T-waves, we apply Wavelet Transform for estimating T-wave and P-wave peak locations and combine each developed model with an approach based on the computation of Trapeziums area to locate the end point of each T-wave and the beginning and end point of each P-wave. The composition of two Gaussian functions has produced the most accurate results concerning wave modelling, providing average values of normalized RMS errors equal to 9,15% and 18,70%, respectively for T-wave and P-wave. Rayleigh pdf provided the most stable fitting parameters. For T-wave end location, the most accurate results were computed when using the kernel composition of two Gaussian functions, for which the average time error was 4,49 ± 14,32 ms. For P-wave begin and P-wave end locations, the most accurate results were computed when using kernel Rayleigh pdf, for which the average time errors were, respectively, -4,23 ± 14,84 ms and 2,26 ± 13,14 ms.