Ground glass opacity (GGO) nodules detection from lung CT scans

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dc.contributor.authorPaing, May Phu
dc.contributor.authorChoomchuay, Somsak
dc.date.accessioned2026-08-06T10:16:50Z
dc.date.available2026-08-06T10:16:50Z
dc.date.issued2017-07-01
dc.description.abstractGround glass opacity (GGO) nodules have a higher possibility of malignancy compared to other types of nodules appeared in the lung cancer. They are very effortful to detect due to their hazy structures and unclear margins. 65% of lung cancers are missed by the radiologists with the faint appearance of the GGO. Consequently, the detection of GGO is a critical issue and a striving task for the radiologists. This research proposes an automatic detection of GGO remained after the detection of solid opacity. Simple thresholding based on grey levels and mathematical image subtraction are applied for segmentation. Possible false after segmentation are reduced by the support vector machine (SVM). In total 37 GGOs, only 2 are missed by proposed segmentation and the false reduction performance is 94%.
dc.identifier.citation2017 International Symposium on Electronics and Smart Devices Isesd 2017, 2018-January, 230-235, 2017
dc.identifier.doi10.1109/ISESD.2017.8253338
dc.identifier.other2-s2.0-85047531364
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/7702
dc.source2017 International Symposium on Electronics and Smart Devices Isesd 2017
dc.subjectCT scan
dc.subjectground glass opacity
dc.subjectlung cancer
dc.subjectnodules
dc.subjectSVM
dc.titleGround glass opacity (GGO) nodules detection from lung CT scans
dc.typeConference Paper

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