Automated System for the Detection of Lung Nodules

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dc.contributor.authorMartinez Machado, Elizabeth
dc.contributor.authorPerez Diaz, Marlen
dc.contributor.authorOrozco Morales, Rubén
dc.contributor.departmentUniversidad Central "Marta Abreu"de las Villas. Facultad de Eléctrica. Departamento de automática.en_US
dc.coverage.spatialHabanaen_US
dc.date.accessioned2022-02-17T15:30:25Z
dc.date.available2022-02-17T15:30:25Z
dc.date.issued2021-11
dc.description.abstractLung cancer is the most frequent cause of cancer mortality in the world. The diagnostic procedure usually begins with a chest X-ray; however, it is difficult to interpret due to the set of anatomical structures overlapped. Computer-aided detection (CAD) systems are a diagnostic aid tool for radiologists. In the present work a CAD system is proposed for the detection of lung nodules on chest radiographs. Methods such as convolution, local normalization and homomorphic filters are used to pre-process images, using a multi-level threshold method supported by morphological operations for anatomical segmentation. This is followed by a candidate nodule detector using the local slidingband convergence filter. The candidate nodules are segmented using an adaptive threshold based on distance. A set of characteristics for each candidate are calculated based on the segmentation. The system was tested by a free available database (DB) of 247 images, of which 154 are pulmonary nodules (100 malignant and 54 benign cases and 93 nodules). The results obtained indicate that the system is able of detecting 98.7% of the nodules of the DB with an average of 56.08 detections per image. Two false positive were obtained due to lung segmentation.en_US
dc.identifier.urihttps://dspace.uclv.edu.cu/handle/123456789/13430
dc.language.isoesen_US
dc.relation.journalLecture Notes in Computer Scienceen_US
dc.rights.holderSpringeren_US
dc.source.endpage348en_US
dc.source.initialpage337en_US
dc.source.volume13055en_US
dc.subjectCAD Systemen_US
dc.subjectLung nodulesen_US
dc.subjectImage processingen_US
dc.titleAutomated System for the Detection of Lung Nodulesen_US
dc.typeArticleen_US
dc.type.article1en_US

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