Automatic detection of solitary lung nodules using quality threshold clustering, genetic algorithm and diversity index

The present work has the objective of developing an automatic methodology for the detection of lung nodules. The proposed methodology is based on image processing and pattern recognition techniques and can be summarized in three stages. In the first stage, the extraction and reconstruction of the pu...

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Veröffentlicht in:	Artificial intelligence in medicine Jg. 60; H. 3; S. 165 - 177
Hauptverfasser:	de Carvalho Filho, Antonio Oseas, de Sampaio, Wener Borges, Silva, Aristófanes Corrêa, de Paiva, Anselmo Cardoso, Nunes, Rodolfo Acatauassú, Gattass, Marcelo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 01.03.2014 Elsevier
Schlagworte:	Algorithms Animal tumors. Experimental tumors Applied sciences Artificial Intelligence Biological and medical sciences Cluster Analysis Computer science; control theory; systems Computer-aided detection Data processing. List processing. Character string processing Exact sciences and technology Experimental respiratory system tumors Genetic algorithm Humans Image Processing, Computer-Assisted Information systems. Data bases Internal Medicine Lung - diagnostic imaging Lung cancer Lung Neoplasms - diagnosis Medical image Medical sciences Memory organisation. Data processing Nodule detection Other Pattern Recognition, Automated Pattern recognition. Digital image processing. Computational geometry Quality threshold Radiographic Image Interpretation, Computer-Assisted - methods Sensitivity and Specificity Software Solitary Pulmonary Nodule - diagnosis Support vector machine Tomography, X-Ray Computed - methods Tumors Genetic algorithm Lung cancer Medical image Quality threshold Support vector machine Nodule detection Computer-aided detection Cluster analysis Image recognition Image processing Lung Respiratory system Vector support machine Diagnosis Pattern extraction Computer vision Data analysis Texture analysis Mortality Image databank Genetic diversity Pattern recognition Failure rate Survival Automatic measurement Medical imagery
ISSN:	0933-3657, 1873-2860, 1873-2860
Online-Zugang:	Volltext
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