A New Generative Model for Textual Descriptions of Medical Images Using Transformers Enhanced with Convolutional Neural Networks.

The automatic generation of descriptions for medical images has sparked increasing interest in the healthcare field due to its potential to assist professionals in the interpretation and analysis of clinical exams. This study explores the development and evaluation of a generalist generative model for medical images. Gaps were identified in the literature, such as the lack of studies that explore the performance of specific models for medical description generation and the need for objective evaluation of the quality of generated descriptions. Additionally, there is a lack of model generalization to different image modalities and medical conditions. To address these issues, a methodological strategy was adopted, combining natural language processing and features extraction from medical images and feeding them into a generative model based on neural networks. The goal was to achieve model generalization across various image modalities and medical conditions. The results showed promising outcomes in the generation of descriptions, with an accuracy of 0.7628 and a BLEU-1 score of 0.5387. However, the quality of the generated descriptions may still be limited, exhibiting semantic errors or lacking relevant details. These limitations could be attributed to the availability and representativeness of the data, as well as the techniques used.

Automatic COVID-19 and Common-Acquired Pneumonia Diagnosis Using Chest CT Scans.

Even with over 80% of the population being vaccinated against COVID-19, the disease continues to claim victims. Therefore, it is crucial to have a secure Computer-Aided Diagnostic system that can assist in identifying COVID-19 and determining the necessary level of care. This is especially important in the Intensive Care Unit to monitor disease progression or regression in the fight against this epidemic. To accomplish this, we merged public datasets from the literature to train lung and lesion segmentation models with five different distributions. We then trained eight CNN models for COVID-19 and Common-Acquired Pneumonia classification. If the examination was classified as COVID-19, we quantified the lesions and assessed the severity of the full CT scan. To validate the system, we used Resnetxt101 Unet++ and Mobilenet Unet for lung and lesion segmentation, respectively, achieving accuracy of 98.05%, F1-score of 98.70%, precision of 98.7%, recall of 98.7%, and specificity of 96.05%. This was accomplished in just 19.70 s per full CT scan, with external validation on the SPGC dataset. Finally, when classifying these detected lesions, we used Densenet201 and achieved accuracy of 90.47%, F1-score of 93.85%, precision of 88.42%, recall of 100.0%, and specificity of 65.07%. The results demonstrate that our pipeline can correctly detect and segment lesions due to COVID-19 and Common-Acquired Pneumonia in CT scans. It can differentiate these two classes from normal exams, indicating that our system is efficient and effective in identifying the disease and assessing the severity of the condition.

ECG Measurement Uncertainty Based on Monte Carlo Approach: An Effective Analysis for a Successful Cardiac Health Monitoring System.

Measurement uncertainty is one of the widespread concepts applied in scientific works, particularly to estimate the accuracy of measurement results and to evaluate the conformity of products and processes. In this work, we propose a methodology to analyze the performance of measurement systems existing in the design phases, based on a probabilistic approach, by applying the Monte Carlo method (MCM). With this approach, it is feasible to identify the dominant contributing factors of imprecision in the evaluated system. In the design phase, this information can be used to identify where the most effective attention is required to improve the performance of equipment. This methodology was applied over a simulated electrocardiogram (ECG), for which a measurement uncertainty of the order of 3.54% of the measured value was estimated, with a confidence level of 95%. For this simulation, the ECG computational model was categorized into two modules: the preamplifier and the final stage. The outcomes of the analysis show that the preamplifier module had a greater influence on the measurement results over the final stage module, which indicates that interventions in the first module would promote more significant performance improvements in the system. Finally, it was identified that the main source of ECG measurement uncertainty is related to the measurand, focused towards the objective of better characterization of the metrological behavior of the measurements in the ECG.

Novel and powerful 3D adaptive crisp active contour method applied in the segmentation of CT lung images.

The World Health Organization estimates that 300 million people have asthma, 210 million people have Chronic Obstructive Pulmonary Disease (COPD), and, according to WHO, COPD will become the third major cause of death worldwide in 2030. Computational Vision systems are commonly used in pulmonology to address the task of image segmentation, which is essential for accurate medical diagnoses. Segmentation defines the regions of the lungs in CT images of the thorax that must be further analyzed by the system or by a specialist physician. This work proposes a novel and powerful technique named 3D Adaptive Crisp Active Contour Method (3D ACACM) for the segmentation of CT lung images. The method starts with a sphere within the lung to be segmented that is deformed by forces acting on it towards the lung borders. This process is performed iteratively in order to minimize an energy function associated with the 3D deformable model used. In the experimental assessment, the 3D ACACM is compared against three approaches commonly used in this field: the automatic 3D Region Growing, the level-set algorithm based on coherent propagation and the semi-automatic segmentation by an expert using the 3D OsiriX toolbox. When applied to 40 CT scans of the chest the 3D ACACM had an average F-measure of 99.22%, revealing its superiority and competency to segment lungs in CT images.

Automatic 3D pulmonary nodule detection in CT images: A survey.

This work presents a systematic review of techniques for the 3D automatic detection of pulmonary nodules in computerized-tomography (CT) images. Its main goals are to analyze the latest technology being used for the development of computational diagnostic tools to assist in the acquisition, storage and, mainly, processing and analysis of the biomedical data. Also, this work identifies the progress made, so far, evaluates the challenges to be overcome and provides an analysis of future prospects. As far as the authors know, this is the first time that a review is devoted exclusively to automated 3D techniques for the detection of pulmonary nodules from lung CT images, which makes this work of noteworthy value. The research covered the published works in the Web of Science, PubMed, Science Direct and IEEEXplore up to December 2014. Each work found that referred to automated 3D segmentation of the lungs was individually analyzed to identify its objective, methodology and results. Based on the analysis of the selected works, several studies were seen to be useful for the construction of medical diagnostic aid tools. However, there are certain aspects that still require attention such as increasing algorithm sensitivity, reducing the number of false positives, improving and optimizing the algorithm detection of different kinds of nodules with different sizes and shapes and, finally, the ability to integrate with the Electronic Medical Record Systems and Picture Archiving and Communication Systems. Based on this analysis, we can say that further research is needed to develop current techniques and that new algorithms are needed to overcome the identified drawbacks.

Lung disease detection using feature extraction and extreme learning machine

INTRODUCTION: The World Health Organization estimates that by 2030 the Chronic Obstructive Pulmonary Disease (COPD) will be the third leading cause of death worldwide. Computerized Tomography (CT) images of lungs comprise a number of structures that are relevant for pulmonary disease diagnosis and analysis. METHODS: In this paper, we employ the Adaptive Crisp Active Contour Models (ACACM) for lung structure segmentation. And we propose a novel method for lung disease detection based on feature extraction of ACACM segmented images within the cooccurrence statistics framework. The spatial interdependence matrix (SIM) synthesizes the structural information of lung image structures in terms of three attributes. Finally, we perform a classification experiment on this set of attributes to discriminate two types of lung diseases and health lungs. We evaluate the discrimination ability of the proposed lung image descriptors using an extreme learning machine neural network (ELMNN) comprising 4-10 neurons in the hidden layer and 3 neurons in the output layer to map each pulmonary condition. This network was trained and validated by applying a holdout procedure. RESULTS: The experimental results achieved 96% accuracy demonstrating the effectiveness of the proposed method on identifying normal lungs and diseases as COPD and fibrosis. CONCLUSION: Our results lead to conclude that the method is suitable to integrate clinical decision support systems for pulmonary screening and diagnosis.

Color texture classification using shortest paths in graphs.

Color textures are among the most important visual attributes in image analysis. This paper presents a novel method to analyze color textures by modeling a color image as a graph in two different and complementary manners (each color channel separately and the three color channels altogether) and by obtaining statistical moments from the shortest paths between specific vertices of this graph. Such an approach allows to create a set of feature vectors, which were extracted from VisTex, USPTex, and TC00013 color texture databases. The best classification results were 99.07%, 96.85%, and 91.54% (LDA with leave-one-out), 87.62%, 66.71%, and 88.06% (1NN with holdout), and 98.62%, 96.16%, and 91.34% (LDA with holdout) of success rate (percentage of samples correctly classified) for these three databases, respectively. These results prove that the proposed approach is a powerful tool for color texture analysis to be explored.

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-cardiologist's 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.

Modelo de Contorno Ativo Crisp Adaptativo 2D aplicado na segmentação dos pulmões em imagens de TC do tórax de voluntários sadios e pacientes com enfisema pulmonar / Adaptive 2D Crisp Active Contour Model applied to lung segmentation in CT images of the thorax of healthy volunteers and patients with pulmonary emphysema

INTRODUÇÃO: Dentre as doenças que afetam a população mundial, destaca-se a preocupação com a Doença Pulmonar Obstrutiva Crônica (DPOC), que, segundo a Organização Mundial de Saúde, pode se constituir na terceira causa de morte mais importante em todo mundo no ano de 2030. Visando contribuir com o auxílio ao diagnóstico médico, esta pesquisa centraliza seus esforços na etapa de segmentação dos pulmões, visto que esta é a etapa básica de sistema de Visão Computacional na area de pneumologia. MÉTODOS: Este trabalho propõe um novo método de segmentação dos pulmões em imagens de Tomografia Computadorizada (TC) do tórax chamado de Método de Contorno Ativo (MCA) Crisp Adaptativo 2D. Este MCA consiste em traçar automaticamente uma curva inicial dentro dos pulmões, que se deforma por iterações sucessivas, minimizando energias que atuam sobre a mesma, deslocando-a até as bordas do objeto. O MCA proposto é o resultado do aperfeiçoamento do MCA Crisp desenvolvido previamente, visando aumentar a sua exatidão, diminuindo o tempo de análise e reduzindo a subjetividade na segmentação e análise dos pulmões dessas imagens pelos médicos especialistas. Este método por iterações sucessivas de minimização de sua energia, segmenta de forma automática os pulmões em imagens de TC do tórax. RESULTADOS: Para sua validação, o MCA Crisp Adaptativo é comparado com os MCAs THRMulti, THRMod, GVF, VFC, Crisp e também com o sistema SISDEP, sendo esta avaliação realizada utilizando como referência 24 imagens, sendo 12 de pacientes com DPOC e 12 de voluntários sadios, segmentadas manualmente por um pneumologista. Os resultados obtidos demonstram que o método proposto é superior aos demais. CONCLUSÃO: Diante dos resultados obtidos, pode-se concluir que este método pode integrar sistemas de auxílio ao diagnóstico médico na área de Pneumologia.

INTRODUCTION: Among the diseases that affect the world's population, there is concern about Chronic Obstructive Pulmonary Disease (COPD), that, according to the World Health Organization, could be the leading cause of death worldwide by the year 2030. Aiming to contribute to aid medical diagnosis, this research focuses its efforts on the segmentation of the lungs, since this is the basic step system in the area of Computer Vision pulmonology. METHODS: This paper proposes a new method for segmentation of lung images in Computed Tomography (CT) of the chest called Active Contour Method (MCA) Crisp Adaptive 2D. This MCA is to draw a curve starting inside an object of interest. This curve is deformed by successive iterations, minimizing energies that act on it, moving it to the edges of the object. The MCA is the improvement of the proposed MCA Crisp previously developed, aiming to increase the accuracy, decreasing analysis time and reducing the subjectivity in the segmentation and analysis of the lungs of these images by pulmonologists. This method is automatically initialized in the lungs and on successive iterations to minimize this energy, this MCA automatically targets the lungs in chest CT images. RESULTS: To evaluate the proposed method, the MCA Adaptive Crisp is compared with MCAs THRMulti, THRMod, GVF, VFC, Crisp and also with the system SISDEP, this assessment is performed using reference images 24, 12 COPD patients and 12 volunteers healthy, manually segmented by a pulmonologist. The results show that the proposed method is superior to the others. CONCLUSION: Based on the results, it can be concluded that this method can integrate systems aid in the medical diagnosis of Pulmonology.

Segmentação automática 2D de vias aéreas em imagens de tomografia computadorizada do tórax / Automatic 2D segmentation of airways in thorax computed tomography images

INTRODUÇÃO: Grande parte da população mundial é afetada por doenças pulmonares, como é o caso das broncopatias constituídas pela asma, bronquiectasia e a bronquite. O diagnóstico de broncopatias é baseado no estado das vias aéreas. Neste sentido, a segmentação automática das vias aéreas em imagens de Tomografia Computadorizada (TC) do tórax é uma etapa fundamental para auxílio ao diagnóstico dessas doenças. MÉTODOS: O presente trabalho avalia algoritmos e desenvolve métodos de segmentação automática das vias aéreas 2D. Tais métodos são compostos por algoritmos de detecção de vias aéreas, sendo estes rede neural Multilayer Perceptron (MLP) e Análise de Densidades Pulmonares (ADP), e por algoritmos de segmentação de vias aéreas, sendo estes Crescimento de Região (CR), Método de Contornos Ativos (MCA) Balão e Topológico Adaptativo. RESULTADOS: Os resultados foram obtidos em três etapas: análise comparativa entre os algoritmos de detecção MLP e ADP, com um padrão-ouro adquirido por três médicos com expertise em imagens de TC do tórax; análise comparativa entre algoritmos de segmentação MCA balão, MCA topológico adaptativo, MLP e CR; e avaliação das possíveis combinações entre os algoritmos de detecção e segmentação, resultando no método completo para segmentação automática das vias aéreas em 2D. CONCLUSÃO: A baixa incidência de falso-negativo e a redução significativa de falso-positivo, resulta em coeficiente de similaridade e sensibilidade superior a 91% e 87% respectivamente, para uma combinação dos algoritmos, com qualidade de segmentação satisfatória.

INTRODUCTION: Much of the world population is affected by pulmonary diseases, such as the bronchial asthma, bronchitis and bronchiectasis. The bronchial diagnosis is based on the airways state. In this sense, the automatic segmentation of the airways in Computed Tomography (CT) scans is a critical step in the aid to diagnosis of these diseases. METHODS: This paper evaluates algorithms for airway automatic segmentation, using Neural Network Multilayer Perceptron (MLP) and Lung Densities Analysis (LDA) for detecting airways, along with Region Growing (RG), Active Contour Method (ACM) Balloon and Topology Adaptive to segment them. RESULTS: We obtained results in three stages: comparative analysis of the detection algorithms MLP and LDA, with a gold standard acquired by three physicians with expertise in CT imaging of the chest; comparative analysis of segmentation algorithms ACM Balloon, ACM Topology Adaptive, MLP and RG; and evaluation of possible combinations between segmentation and detection algorithms, resulting in the complete method for automatic segmentation of the airways in 2D. CONCLUSION: The low incidence of false negative and the significant reduction of false positive, results in similarity coefficient and sensitivity exceeding 91% and 87% respectively, for a combination of algorithms with satisfactory segmentation quality.

Modelo de contorno ativo crisp: nova técnica de segmentação dos pulmões em imagens de TC / Active contour modes crisp: new technique for segmentation the lungs in CT images

Este trabalho propõe um novo método de contornos ativos (MCA), chamado de MCA Crisp, e o avalia na segmentação dos pulmões em imagens de Tomografia Computadorizada (TC). O MCA consiste em traçar uma curva inicial em torno ou dentro de um objeto de interesse. Esta curva se deforma, conforme algumas energias que atuam sobre a mesma, deslocando-a até as bordas do objeto. Este processo é realizado por iterações sucessivas de minimização de uma dada energia, associada à curva. Aplicando MCAs descritos na literatura na segmentação dos pulmões em imagens de TC, constatam-se limitações. Neste contexto, propõe‑-se o MCA Crisp para suprir tais limitações, propondo uma inicialização automática e uma nova energia externa baseada em regras e nas densidades radiológicas pulmonares. Realiza-se uma comparação entre os MCAs Tradicional, Balão, GVF e o método proposto para demonstrar a superioridade do novo método. Em seguida, para validar o MCA Crisp realiza-se uma análise qualitativa junto a um médico especialista na área de Pneumologia do Hospital Universitário Walter Cantídio da Universidade Federal do Ceará. Nesta análise, são utilizados 100  pulmões em imagens de TC. A eficiência da segmentação foi avaliada em  5 categorias, obtendo os seguintes resultados:   73% ótimas, sem falhas, 20% aceitáveis, com pequenos erros, 7% razoáveis, com erros grosseiros e  0% ruim, segmentando apenas uma pequena parte do pulmão, e  0% péssima, obtendo uma segmentação totalmente errada. Conclui-se que o MCA Crisp é um método útil para segmentar os pulmões em imagens de TC e com potencial para integrar sistemas que auxiliem o diagnóstico médico.

This paper proposes a new Active Contour Model (ACM), called ACM Crisp, and evaluates the segmentation of lungs in computed tomography (CT) images. An ACM draws a curve around or within the object of interest. This curve changes its shape, when some energy acts on it and moves towards the edges of the object. This process is performed by successive iterations of minimization of a given energy, associated with the curve. The ACMs described in the literature have limitations when used for segmentations of CT lung images. The ACM Crisp model overcomes these limitations, since it proposes automatic initiation and new external energy based on rules and radiological pulmonary densities. The paper compares other ACMs with the proposed method, which is shown to be superior. In order to validate the algorithm a medical expert in the field of Pulmonology of the Walter Cantídio University Hospital from the Federal University of Ceará carried out a qualitative analysis. In these analyses  100 CT lung images were used. The segmentation efficiency was evaluated into  5 categories with the following results for the ACM Crisp: 73% excellent, without errors, 20% acceptable, with small errors, and  7% reasonable, with large errors, 0% poor, covering only a small part of the lung, and  0% very bad, making a totally incorrect segmentation. In conclusion the ACM Crisp is considered a useful algorithm to segment CT lung images, and with potential to integrate medical diagnosis systems.

Sistema automático para quantificação e visualização da aeração pulmonar em imagens de tomografia computadorizada de tórax: Sistema de Análise de Imagens Pulmonares – SAIP / Automatic system for quantification and visualization of lung aeration on chest computed tomography images: the Lung Image System Analysis – LISA

A tomografia computadorizada de alta resolução (TCAR) é o exame de escolha na avaliação diagnóstica de afecções do parênquima pulmonar. Neste particular, há um interesse crescente por sistemas computacionais capazes de analisar automaticamente a densidade radiológica dos pulmões. O principal objetivo deste trabalho é apresentar um sistema automático para quantificação e visualização do grau de aeração pulmonar (SAIP), em imagens de TCAR de pulmões com diferentes graus de alterações da aeração pulmonar. Como objetivo secundário comparar o SAIP ao sistema Osiris e a um algoritmo específico de segmentação pul­monar (SP), quanto à acurácia na segmentação do parênquima pulmonar. O SAIP disponibiliza atributos quantitativos extraídos automaticamente, tais como perímetro, área e volume da secção pulmonar, bem como o histograma de faixa de densidades ra­diológicas e acumulado, densidade pulmonar média (Dpm) em unidades Hounsfield (UH), área relativa dos voxels com densi­dade menor que –950 UH (RA950) e os valores de 15° percentil de baixa atenuação (PERC15). Além disso, é capaz de processar imagens por meio de uma ferramenta de máscara colorida, que aplica pseudo­cores no parênquima pulmonar, conforme faixas de densidade radiológicas pré­determinadas. Os resultados da segmentação pulmonar são comparados para um conjunto de 102 imagens de 8 voluntários saudáveis e 141 imagens de 11 pacientes com doença pulmonar obstrutiva crônica (DPOC). Quanto à segmentação, o SAIP se apresenta mais efetivo do que os outros dois métodos. O SAIP constitui uma ferramenta pro­missora no auxílio ao diagnóstico de enfisema em pacientes com DPOC, com grande potencial de aplicação nesta área e em outras doenças pulmonares.

High Resolution Computed Tomography (HRCT) is the exam of choice for the diagnostic evaluation of lung parenchyma diseases. There is an increasing interest for computational systems able to automatically analyze the radiological densities of the lungs in CT images. The main objective of this study is to present a system for the automatic quantification and visualization of the lung aeration in HRCT images of different degrees of aeration, called Lung Image System Analysis (LISA). The secondary objective is to compare LISA to the Osiris system and also to specific algorithm lung segmentation (ALS), on the accuracy of the lungs segmentation. The LISA system automatically extracts the following image attributes: lungs perimeter, cross sectional area, volume, the radiological densities histograms, the mean lung density (MLD) in Hounsfield units (HU), the relative area of the lungs with voxels with density values lower than –950 HU (RA950) and the 15th percentile of the least density voxels (PERC15). Furthermore, LISA has a colored mask algorithm that applies pseudo-colors to the lung parenchyma according to the pre-defined radiological density chosen by the system user. The lungs segmentations of 102 images of 8 healthy volunteers and 141 images of 11 patients with Chronic Obstructive Pulmonary Disease (COPD) were compared on the accuracy and concordance among the three methods. The LISA was more effective on lungs segmentation than the other two methods. LISA’s color mask tool improves the spatial visualization of the degrees of lung aeration and the various attributes of the image that can be extracted may help physicians and researchers to better assess lung aeration both quantitatively and qualitatively. LISA may have important clinical and research applications on the assessment of global and regional lung aeration and therefore deserves further developments and validation studies.

Análise comparativa de desempenho das transformadas Wavelet e Hilbert na detecção do QRS em ECG / Performance comparison analysis of Wavelet and Hilbert transforms for QRS detection in ECG

O processo de detecção do complexo QRS é o primeiro passo de um processo de extração de parâmetros do sinal eletrocardiograma (ECG) em sistemas de auxílio ao diagnóstico médico. O presente trabalho apresenta resultados detalhados de comparação da aplicação de duas transformadas matemáticas, Wavelet e Hilbert, em um algoritmo de detecção de QRS em termos de taxas de detecções corretas (sensibilidade e preditividade positiva) e de uma medida de frequência de recorrência a processos de filtragem (pré-processamento). Uma abordagem inovadora é implementada, na qual as rotinas de filtragem são inseridas dentro do estágio de decisão, ou seja, é realizada a supressão da etapa de pré-processamento. As transformadas são aplicadas no algoritmo, que é baseado em um limiar adaptativo, com o objetivo de realçar, apenas quando necessário, os picos (pontos fiduciais)do QRS. Em uma primeira abordagem, apenas a transformada Wavelet é utilizada neste realce e, numa segunda abordagem, a transformada de Hilbert é inserida em série à aplicação da Wavelet em dois possíveis arranjos. São realizados experimentos dos algoritmos sobre os exames da base de dados Arrhythmia Database, pertencente ao conjunto de bases de dados do MIT-BIH. É composta por 48 gravações de ECG com duração de trinta minutos, amostrados a uma frequência de 360 Hz com resolução de 4,88 μV sobre uma faixa de variação de 10 mV. Ao todo, contabilizam-se 109.662 complexos QRS. Taxas de 98,85% de sensibilidade e 95,10% de preditividade positiva são obtidas com a aplicação exclusiva da transformada Wavelet, enquanto que 98,89% de sensibilidade e 98,52% de preditividade positiva são obtidas com aaplicação em série das transformadas Wavelet e de Hilbert.

The process of QRS detection is the first stage of a greater process: the feature extraction in the electrocardiogram (ECG). This work presents detailed results on the performance of two mathematical transforms, Hilbert and Wavelet, which are applied in QRS detection. The evaluation parameters are the detection rates and a measure of frequency of recurrence to filtering processes. An innovative approach is implemented: the filtering routines are inserted in the decision stage, i.e. the preprocessing stage is removed. The algorithm is based on adaptive threshold technique and the two transforms are applied in order to emphasize, only when necessary, the QRS fiducial points. In a first approach, only the Wavelet transform is applied, and in a second approach, the Hilbert transform is inserted before the Wavelet transform or after it. We evaluate these approaches on the well-known MIT-BIH Arrhythmia Database. It contains 48 half-hour recordings of annotated ECG with a sampling rate of 360 Hz and 4.88 μV resolution over a 10 mV range, totalizing 109,662 QRS complexes. Sensitivity rates of 98.85% and 98.89% are respectively attained when the Wavelet transform is applied in the filtering processes and both Hilbert and Wavelet transforms are applied. Predictability rates of 95.10% and 98.52% are also attained respectively using Wavelet transform and the simultaneous application of Hilbert and Wavelet transforms in the filtering processes.

Computer-assisted evaluation of pulmonary emphysema in CT scans: comparison between a locally developed system and a freeware system.

OBJECTIVE: To present a locally developed system of computer vision for use with HRCT images, designated SIStema para a Detecção e a quantificação 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 mm(2), 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 (r(2) = 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.

Avaliação computacional de enfisema pulmonar em TC: comparação entre um sistema desenvolvido localmente e um sistema de uso livre / Computer-assisted evaluation of pulmonary emphysema in CT scans: comparison between a locally developed system and a freeware system

OBJETIVO: Apresentar um sistema de visão computacional em imagens de TCAR, desenvolvido localmente e denominado SIStema para a Detecção e a quantificação de Enfisema Pulmonar (SISDEP). Comparar esse sistema a um sistema computacional de acesso livre. MÉTODOS: Foram utilizadas 33 imagens de TCAR de 11 pacientes com DPOC com cortes tomográficos em ápice, hilo e base. O SISDEP foi comparado a Osiris Medical Imaging Software Program quanto à segmentação do parênquima pulmonar; precisão das medidas da área de secção transversal dos pulmões em mm², densidade pulmonar média (DPM), porcentagem da relative area (RA, área relativa) dos voxels com densidade < -950 unidades hounsfield (ra -950), valores de 15º percentil de baixa atenuação (perc15) e visualização das áreas hiperaeradas por máscara colorida. RESULTADOS: Os dois sistemas realizaram uma segmentação pulmonar eficiente; porém o SISDEP o fez de modo automático e mais rápido. Na comparação das medidas de área de secção dos pulmões, DPM, Perc15 e RA -950, houve alta correlação (r² = 0,99; 0,99; 0,99; e 1,00, respectivamente) entre os dois sistemas. A ferramenta de máscara colorida do SISDEP permitiu excelente visualização das áreas hiperaeradas, discriminado-as das áreas normais. CONCLUSÕES: O SISDEP se mostrou eficiente na segmentação dos pulmões e na extração de dados quantitativos da aeração pulmonar com excelente correlação com o sistema Osiris. O SISDEP constitui uma promissora ferramenta computacional para a avaliação diagnóstica e o acompanhamento da evolução do enfisema em imagens de TCAR de pacientes com DPOC.

OBJECTIVE: To present a locally developed system of computer vision for use with HRCT images, designated SIStema para a Detecção e a quantificação 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.

Sistema inteligente para auxílio ao diagnóstico no monitoramento fetal eletrônico por análise de cardiotocografias / A computer aided diagnostic intelligent system for electronic fetal monitoring based on cardiotocographies

A análise acurada da frequência cardíaca fetal (FCF) correlacionada com as contrações uterinas permite diagnosticar, e consequentemente antecipar, diversos problemas relativos ao bem estar fetal e à preservação de sua vida. O presente trabalho apresenta os resultados de um sistema híbrido, baseado em regras determinísticas e em um módulo de inferência nebuloso do tipo Mamdani, para análise de sinais coletados através de exames denominados cardiotocografias (CTG). As variáveis analisadas são: o valor basal da FCF, suas variabilidades de curto e de longo prazo, acelerações transitórias e desacelerações, sendo estas classificadas por seu tipo e número de ocorrências. São utilizados dois modelos de classificação. A saída do sistema, em qualquer dos modelos, é um diagnóstico de primeiro nível baseado nestas variáveis de entrada. O sistema inteligente para auxílio ao diagnóstico no monitoramento fetal eletrônico por análise de cardiotocografias (SISCTG) foi desenvolvido na linguagem de scripts do programa MATLAB® v.7. O projeto conta também com uma parceria multi-institucional entre o Brasil e a Alemanha, envolvendo o Departamento de Engenharia de Teleinformática (DETI) da Universidade Federal do Ceará (UFC), a Maternidade-Escola Assis Chateaubriand (MEAC), a Technische Universitãt München e a empresa alemã Trium GmbH, que fornece a base de dados utilizada neste trabalho. Os resultados apresentados pelo SISCTG mostram-se promissores, com um índice de acertos (comparando-se os dois modelos utilizados) variando de 83% a 100%, de acordo com o tipo de diagnóstico. Isto permite projetar o aprimoramento deste sistema com novas variáveis de entrada (como a entropia aproximada da FCF e da sua variabilidade). A validação do sistema contou com especialistas brasileiros e alemães na área obstétrica.

The accurate analysis of the fetal heart rate (FHR) and its correlation with uterine contractions (UC) allow the diagnostic and the anticipation of many problems related to fetal distress and the preservation of its life. This paper presents the results of a hybrid system based on a set of deterministic rules and fuzzy inference system developed to analyze FHR and UC signals collected by cardiotocography (CTG) exams. The studied variables are basal FHR, short and long-term FHR variability, transitory accelerations and decelerations, these lasts classified by their type and number of occurrences. Two classification models are used. For both models, the system output is a first level diagnostic based on those input variables. The system is developed using the MATLAB® v.7 script language. The project is also supported by a multi-institutional agreement between Brazil and Germany, among the DETI (Departamento de Engenharia de Teleinformática of the Universidade Federal do Ceará), the MEAC (Maternidade-Escola Assis Chateaubriand), the TUM (Technische Universitãt München), and the Trium GmbH, a German company who supplied the database used in this project. The results are very promising with a diagnostic accuracy (considering the two models used) varying from 83% to 100%, according to the type of diagnostic. These results allow the projection of refinements of the proposed system, inserting new input variables (such as the approximate entropy of the FHR and its variability). The system validation methodology was based on the knowledge of Brazilian and German obstetricians.