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- W3090369103 abstract "Lung segmentation is the most essential and significant step for computer-aided diagnosis (CAD) systems for the detection of cancer in the lung region at the prior phase. The efficacy of CAD system is highly dependent on how the lungs are properly and accurately segmented. Effective lung segmentation reduces the various challenges for the CAD system to detect the juxtapleural nodules. This paper emphasizes the segmentation of lungs in CT images using the K-means approach with automatic threshold and morphological operations. Total ten subjects have been taken from the public dataset LIDC-IDRI including two subjects of juxtapleural nodules. The proposed method achieved a performance of 97.522% overlap ratio and 0.9685 Jaccard’s similarity index values." @default.
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- W3090369103 date "2020-09-29" @default.
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- W3090369103 title "Segmentation of Lungs in Thoracic CTs Using K-means Clustering and Morphological Operations" @default.
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- W3090369103 doi "https://doi.org/10.1007/978-981-15-6329-4_28" @default.
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