Computer-aided diagnosis system for the Acute Respiratory Distress Syndrome from chest radiographs

Zaglam, Nesrine; Jouvet, Philippe; Fléchelles, Olivier; Émériaud, Guillaume; Cheriet, Foued

doi:10.1016/j.compbiomed.2014.06.006

Cited by 24 publications

(27 citation statements)

References 20 publications

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“…Our group developed the first CAD system for detection of ARDS in chest radiographs from children. 63 Identification of ROI: Our method consists of automatically extracting intercostal patches from chest radiographs belonging to the test database using a semiautomatic segmentation method of the ribs.…”

Section: Acute Respiratory Distress Syndromementioning

confidence: 99%

“…In the literature, various databases have been used for validation of CAD systems. Some databases were created using chest X-ray interpretation obtained from a consensus among several radiologists or physicians which subsequently had no diagnosis verification 54,62,63 or were verified by CT scans or chest radiograph follow-up. 6,[48][49][50][51]59 For detection of lung nodules, databases were created by identifying all the nodules on each chest radiograph.…”

Section: Chest X-ray Databases Construction For the Development And Vmentioning

confidence: 99%

“…60 Some databases only included lungs without ribs areas to avoid interpretation bias. 52,63 Others used the global diagnosis and created a database for the right and left lungs. 61,62 For external validation (outside the center that developed the CAD system), the CAD system can be tested using a public database.…”

Section: Chest X-ray Databases Construction For the Development And Vmentioning

confidence: 99%

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Computer-Aided Diagnosis for Chest Radiographs in Intensive Care

Zaglam

Cheriet

Jouvet

2015

J Pediatr Intensive Care

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The chest radiograph is an essential tool for the diagnosis of several lung diseases in intensive care units (ICU). However, several factors make the interpretation of the chest radiograph difficult including the number of X-rays done daily in ICU, the quality of the chest radiograph, and the lack of a standardized interpretation. To overcome these limitations in the interpretation of chest radiographs, researchers have developed computer-aided diagnosis (CAD) systems. In this review, the authors report the methodology used to develop CAD systems including identification of the region of interest, analysis of these regions, and classification. Currently, only a few CAD systems for chest X-ray interpretation are commercially available. Some promising research is ongoing, but the involvement of the pediatric research community is needed for the development and validation of such CAD systems dedicated to pediatric intensive care.

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Section: Acute Respiratory Distress Syndromementioning

confidence: 99%

Section: Chest X-ray Databases Construction For the Development And Vmentioning

confidence: 99%

Section: Chest X-ray Databases Construction For the Development And Vmentioning

confidence: 99%

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Computer-Aided Diagnosis for Chest Radiographs in Intensive Care

Zaglam

Cheriet

Jouvet

2015

J Pediatr Intensive Care

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“…Texture is an important visual attribute in computer vision with many areas of applications. Recently, texture analysis has been widely applied to remote sensing [1], industrial inspection [2], medical image analysis [3], face recognition [4], among many others. Although the human visual system can easily discriminate textural patterns, the description by automatic methods has been a great challenge.…”

Section: Introductionmentioning

confidence: 99%

Texture analysis using complex system models: fractal dimension, swarm systems and non-linear diffusion

Machado¹,

Rodrigues²

2017

Anais Do XXX Concurso De Teses E Dissertações (CTD 2017)

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Texture is one of the primary visual features used to computationally describe the patterns found in nature. Existing computational methods, however, do not successfully discriminate the complexity of texture patterns. Such methods disregard the possibility of describing images by benefiting from the complex systems properties that are characteristic to textures. To do so, we created approaches based on the Bouligand-Minkowski fractal dimension, swarm-system Artificial Crawlers, and non-linear diffusion of Perona-Malik, techniques that led to methodologies with efficacy and efficiency comparable to the state-ofthe-art. The results achieved in the four methodologies described in this work demonstrated the validity and the potential of our hypothesis in tasks of pattern recognition. The contributions of our methodologies shall support advances in materials engineering, computer vision, and agriculture.

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“…• MACHADO, B.B., ORUE, J., ARRUDA, M.S., SANTOS, C. PLANCHON, 2011;GONG et al, 2014), industrial inspection (KIM; LIU; TSANG;NGAN;PANG, 2016), medical image analysis (SERRANO; ACHA, 2009;ERGIN;KILINC, 2014;ZAGLAM et al, 2014), face recognition (FU et al, 2010;MEHTA;YUAN;EGIAZARIAN, 2014), among many others. Although the human visual system can easily discriminate textural patterns, the description by automatic methods has been a great challenge.…”

Section: List Of Figuresmentioning

confidence: 99%

Texture analysis using complex system models: fractal dimension, swarm systems and non-linear diffusion

Machado¹

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" Happiness is not something you postpone for the future, it is something you design for the present; if it does not knock, build a door." A textura é um dos principais atributos visuais para a descrição de padrões encontrados na natureza. Diversosmétodos de análise de textura têm sido usadoscomo uma poderosa ferramenta para aplicações reais que envolvem análise de imagens e visão computacional. Entretanto, os métodos existentes não conseguem discriminar com sucesso a complexidade dos padrões de textura. Tais métodos desconsideram a possibilidade de se descrever estruturas de imagens por meio de medidas como a dimensão fractal. Medidas baseadas em fractalidade permitem uma interpretação geométrica não-inteira que possui aplicações encontradas em áreas como matemática, física, e biologia. Sobre esta lacuna metodológica, a hipótese central desta tese é que texturas presentes na natureza podem ser medidas como superfícies fractais irregulares devido à sua geometria complexa, o que pode ser explorado para fins de análise de imagens e visão computacional. Para superar tais limitações, avançando o estado da arte, esta tese se inicia com uma análise das características de texturas baseada em caminhadas aleatórias de agentes sobre superfícies de imagens. Esta primeira análise leva a um método que combina dimensão fractal com caminhadas de agentes sobre a superfície de imagens. Em uma segunda abordagem, usa-se a difusão não-linear para representar imagens de texturas em diferentes escalas, as quais são descritas via dimensão fractal para fins de classificação de imagens. Em uma terceira proposta, emprega-se a dimensão fractal sobre múltiplas escalas derivadas de uma mesma imagem com o propósito de se realizar a descrição multi-escala de texturas. Um dos propósitos específicos foi a detecção automática de doenças em folhas de soja. Por último, as características de textura foram exploradas segundo uma metodologia baseada em redes complexas para análise de aglomeração de partículas em imagens de nanotecnologia. Os resultados alcançados nesta tese demonstraram o potencial do uso de características de textura. Para tanto foram usadas técnicasde dimensão fractal de Bouligand-Minkowski, multiagentesArtificial Crawlerse difusão não-linear de Perona-Malik, os quais alcançaram eficácia e eficiência comparáveis ao do estado da arte. As contribuições obtidas devem suportar avanços significativos nas áreas de engenharia de materiais, visão computacional, e agricultura.Palavr as-chave: análise de textura, dimensão fractal, sistemas multi-agentes, difusão anisotró-pica, redes complexas. Texture is one of the primary visual attributes used to describe patterns found in nature. Several texture analysis methods have been used as powerful tools for real applications involving analysis and computer vision. However, existing methods do not successfully discriminate the complexity of texture patterns. Such methods disregard the possibility of describing image structures by means of measures such as the fractal dimension. Fractality-based mea...

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Computer-aided diagnosis system for the Acute Respiratory Distress Syndrome from chest radiographs

Cited by 24 publications

References 20 publications

Computer-Aided Diagnosis for Chest Radiographs in Intensive Care

Computer-Aided Diagnosis for Chest Radiographs in Intensive Care

Texture analysis using complex system models: fractal dimension, swarm systems and non-linear diffusion

Texture analysis using complex system models: fractal dimension, swarm systems and non-linear diffusion

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