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Title: Methodology for automatic detection of lung nodules in computerized tomography images
Authors: Sousa, João Rodrigo Ferreira da Silva
Silva, Aristófanes Corrêa
Paiva, Anselmo Cardoso de
Nunes, Rodolfo Acatauassú
Keywords: Medical image
Computer-aided detection (CAD)
Lung nodules
Image processing
Computer tomography (CT)
Issue Date: 2010
Publisher: ELSEVIER
Citation: Sousa, João Rodrigo Ferreira da Silva ; Silva, Aristófanes Corrêa ; Silva, Anselmo Cardoso de; Nunes, Rodolfo Acatauassú. Methodology for automatic detection of lung nodules in computerized tomography images. Computer Methods and Programs in Biomedicine (Print), v. 98, p. 1-14, 2010.
Abstract: Lung cancer is a disease with significant prevalence in several countries around the world. Its difficult treatment and rapid progression make the mortality rates among people affected by this illness to be very high. Aiming to offer a computational alternative for helping in detection of nodules, serving as a second opinion to the specialists, this work proposes a totally automatic methodology based on successive detection refining stages. The automated lung nodules detection scheme consists of six stages: thorax extraction, lung extraction, lung reconstruction, structures extraction, tubular structures elimination, and false positive reduction. In the thorax extraction stage all the artifacts external to the patient’s body are discarded. Lung extraction stage is responsible for the identification of the lung parenchyma. The objective of the lung reconstruction stage is to prevent incorrect elimination of portions belonging to the parenchyma. Structures extraction stage comprises the selection of dense structures from inside the lung parenchyma. The next stage, tubular structures elimination eliminates a great part of the pulmonary trees. Finally, the false positive stage selects only structures with great probability to be nodule. Each of the several stages has very specific objectives in detection of particular cases of lung nodules, ensuring good matching rates even in difficult detection situations. We use 33 exams with diversified diagnosis and slices numbers for validating the methodology. We obtained a false positive per exam rate of 0.42 and false negative rate of 0.15. The total classification sensitivity obtained, measured out of the nodule candidates,was 84.84%. The specificity achieved was 96.15% and the total accuracy of the method was 95.21%.
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Appears in Collections:Mestrado em Engenharia de Eletricidade/Artigos Cientificos

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