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

Mlisa: sistema de análise de imagens de tomografia computadorizadas do tórax para android / Mlisa: chest computed tomography image analysis system for android

Com o aumento de doenças e novos casos de cânceres no mundo, o grande desafio na medicina é o diagnóstico precoce de tais anomalias. Desta forma, constata-se que a etapa de segmentação é essencial para o auxilio do diagnóstico médico correto e preciso, visto que esta etapa delimita a área a ser examinada em imagens de Tomografia Computadorizada (TC) que deve ser analisada pelo sistema ou pelo médico especialista. Desta forma, o presente trabalho, apresenta uma segmentação de imagens médicas para dispositivos móveis em especial para Tablet para auxiliar os médicos. Até a conclusão deste trabalho, não foi encontrado nenhum sistema de segmentação de imagens medicas para dispositivos móveis. No contexto este trabalho, descreve-se o desenvolvimento do sistema MLISA para o sistema operacional Android que visa fazer a segmentação de um exame de TC. Para a segmentação foi utilizada a técnica de Limiarização onde pode-se destacar o pulmão. Os resultados obtidos comprovam que o sistema apresenta uma resposta bastante próxima do esperado, cerca de 98% de acerto, quando comparada com outro sistema existente para PC

Embedded real-time speed limit sign recognition using image processing and machine learning techniques

Made available in DSpace on 2018-11-29T04:54:17Z (GMT). No. of bitstreams: 0 Previous issue date: 2017-12-01Instituto Federal do Ceara (IFCE)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Programa Operacional Regional do Norte (NORTE2020) through Fundo Europeu de Desenvolvimento Regional (FEDER)The number of traffic accidents in Brazil has reached alarming levels and is currently one of the leading causes of death in the country. With the number of vehicles on the roads increasing rapidly, these problems will tend to worsen. Consequently, huge investments in resources to increase road safety will be required. The vertical R-19 system for optical character recognition of regulatory traffic signs (maximum speed limits) according to Brazilian Standards developed in this work uses a camera positioned at the front of the vehicle, facing forward. This is so that images of traffic signs can be captured, enabling the use of image processing and analysis techniques for sign detection. This paper proposes the detection and recognition of speed limit signs based on a cascade of boosted classifiers working with haar-like features. The recognition of the sign detected is achieved based on the optimum-path forest classifier (OPF), support vector machines (SVM), multilayer perceptron, k-nearest neighbor (kNN), extreme learning machine, least mean squares, and least squares machine learning techniques. The SVM, OPF and kNN classifiers had average accuracies higher than 99.5 %; the OPF classifier with a linear kernel took an average time of 87 mu s to recognize a sign, while kNN took 11,721 ls and SVM 12,595 ls. This sign detection approach found and recognized successfully 11,320 road signs from a set of 12,520 images, leading to an overall accuracy of 90.41 %. Analyzing the system globally recognition accuracy was 89.19 %, as 11,167 road signs from a database with 12,520 signs were correctly recognized. The processing speed of the embedded system varied between 20 and 30 frames per second. Therefore, based on these results, the proposed system can be considered a promising tool with high commercial potential.Inst Fed Fed Educ Ciencia & Tecnol Ceara IFCE, Lab Proc Digital Imagens & Simulacao Computac, Juazeiro Do Norte, Ceara, BrazilUniv Estadual Paulista, Dept Ciencia Comp, Bauru, SP, BrazilUniv Fortaleza, Programa Posgrad Informat Aplicada, Lab Bioinformat, Fortaleza, CE, BrazilUniv Porto, Fac Engn, Inst Ciencia & Inovacao Engn Mecan & Engn Ind, Dept Engn Mecan, Oporto, PortugalUniv Estadual Paulista, Dept Ciencia Comp, Bauru, SP, BrazilInstituto Federal do Ceara (IFCE): PROINFRA/2013Instituto Federal do Ceara (IFCE): PROAPP/2014Instituto Federal do Ceara (IFCE): PROINFRA/2015CNPq: 470501/2013-8CNPq: 301928/2014-2: NORTE-01-0145-FEDER-00002

Rationale, study design, and analysis plan of the Alveolar Recruitment for ARDS Trial (ART): Study protocol for a randomized controlled trial

Background: Acute respiratory distress syndrome (ARDS) is associated with high in-hospital mortality. Alveolar recruitment followed by ventilation at optimal titrated PEEP may reduce ventilator-induced lung injury and improve oxygenation in patients with ARDS, but the effects on mortality and other clinical outcomes remain unknown. This article reports the rationale, study design, and analysis plan of the Alveolar Recruitment for ARDS Trial (ART). Methods/Design: ART is a pragmatic, multicenter, randomized (concealed), controlled trial, which aims to determine if maximum stepwise alveolar recruitment associated with PEEP titration is able to increase 28-day survival in patients with ARDS compared to conventional treatment (ARDSNet strategy). We will enroll adult patients with ARDS of less than 72 h duration. The intervention group will receive an alveolar recruitment maneuver, with stepwise increases of PEEP achieving 45 cmH(2)O and peak pressure of 60 cmH2O, followed by ventilation with optimal PEEP titrated according to the static compliance of the respiratory system. In the control group, mechanical ventilation will follow a conventional protocol (ARDSNet). In both groups, we will use controlled volume mode with low tidal volumes (4 to 6 mL/kg of predicted body weight) and targeting plateau pressure <= 30 cmH2O. The primary outcome is 28-day survival, and the secondary outcomes are: length of ICU stay; length of hospital stay; pneumothorax requiring chest tube during first 7 days; barotrauma during first 7 days; mechanical ventilation-free days from days 1 to 28; ICU, in-hospital, and 6-month survival. ART is an event-guided trial planned to last until 520 events (deaths within 28 days) are observed. These events allow detection of a hazard ratio of 0.75, with 90% power and two-tailed type I error of 5%. All analysis will follow the intention-to-treat principle. Discussion: If the ART strategy with maximum recruitment and PEEP titration improves 28-day survival, this will represent a notable advance to the care of ARDS patients. Conversely, if the ART strategy is similar or inferior to the current evidence-based strategy (ARDSNet), this should also change current practice as many institutions routinely employ recruitment maneuvers and set PEEP levels according to some titration method.Hospital do Coracao (HCor) as part of the Program 'Hospitais de Excelencia a Servico do SUS (PROADI-SUS)'Brazilian Ministry of Healt

SISTEMA DE AQUISIÇÃO DE DADOS PARA A MÁQUINA DE IMPACTO CHARPY

Este trabalho tem o objetivo de implementar e desenvolver um sistema de aquisição de dados para a máquina de impacto Charpy. Assim, é realizado um estudo da máquina de impacto Charpy, do ensaio de impacto e das ferramentas necessárias para desenvolvimento do projeto. Utiliza-se um acelerômetro para determinar a aceleração nos eixos x e y do pêndulo Charpy durante a realização do ensaio. Para leitura e interpretação dos dados enviados pelo acelerômetro utiliza-se a plataforma de hardware Arduino UNO com software específico. Os dados enviados ao Arduino são apresentados em uma interface gráfica desenvolvida no Matlab. Nesta interface é possível inserir os dados iniciais de ensaio Charpy e apresentar ao usuário final os resultados finais de ensaio, como a energia de impacto, a resistência de impacto e a força necessária para romper o corpo de prova. Além disso, é apresentado ao usuário um gráfico da aceleração ao longo da realização do ensaio e o gráfico de força ao longo do tempo. Desta forma, registram-se os dados em um arquivo específico para análise e estudo posterior. A porcentagem de erro entre o valor medidor no mostrador da máquina e o resultado automatizado não ultrapassa o limite de 8 %