Optical Flow Methods for Lung Nodule Segmentation on LIDC-IDRI Images.
Lung nodule segmentation is an essential step in any CAD system for lung cancer detection and diagnosis. Traditional approaches for image segmentation are mainly morphology based or intensity based. Motion-based segmentation techniques tend to use the temporal information along with the morphology a...
| Published in: | Journal of Digital Imaging Vol. 33; no. 5; pp. 1306 - 1325 |
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| Main Authors: | , , , |
| Format: | diagnostic images equations & formulas research tables/charts Journal Article |
| Published: |
Springer Nature
Oct2020
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=146532212&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146532212 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2020 vid: 33 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 146532212 144442656 146532212 146532212 10.1007/s10278-020-00346-w 146532212 ppf: 1306 ppct: 19 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Optical Flow Methods for Lung Nodule Segmentation on LIDC-IDRI Images. aug: au: Suji, R. Jenkin Bhadouria, Sarita Singh Dhar, Joydip Godfrey, W. Wilfred affil: ABV-IIITM Gwalior, ABV-IIITM Campus, Morena Link Road, 474010, Gwalior, MadhyaPradesh, India sug: subj: Lung Neoplasms Radiography Tomography, X-Ray Computed Diagnosis, Computer Assisted Methods Human Radiography, Thoracic Cancer Patients Radiographic Image Enhancement Early Detection of Cancer Computer-Aided Design ab: Lung nodule segmentation is an essential step in any CAD system for lung cancer detection and diagnosis. Traditional approaches for image segmentation are mainly morphology based or intensity based. Motion-based segmentation techniques tend to use the temporal information along with the morphology and intensity information to perform segmentation of regions of interest in videos. CT scans comprise of a sequence of dicom 2-D image slices similar to videos which also comprise of a sequence of image frames ordered on a timeline. In this work, Farneback, Horn-Schunck and Lucas-Kanade optical flow methods have been used for processing the dicom slices. The novelty of this work lies in the usage of optical flow methods, generally used in motion-based segmentation tasks, for the segmentation of nodules from CT images. Since thin-sliced CT scans are the imaging modality considered, they closely approximate the motion videos and are the primary motivation for using optical flow for lung nodule segmentation. This paper also provides a detailed comparative analysis and validates the effectiveness of using optical flow methods for segmentation. Finally, we propose methods to further improve the efficiency of segmentation using optical flow methods on CT scans. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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