A Study on the Feasibility of Active Contours on Automatic CT Bone Segmentation.
utomatic bone segmentation of computed tomography (CT) images is an important step in image-guided surgery that requires both high accuracy and minimal user interaction. Previous attempts include global thresholding, region growing, region competition, watershed segmentation, and parametric active c...
| Publicado en: | Journal of Digital Imaging Vol. 23; no. 6; pp. 793 - 806 |
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| Autores principales: | , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2010
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105017026&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105017026 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2010 vid: 23 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 105017026 55128920 10.1007/s10278-009-9210-z NLM19495880 105017026 ppf: 793 ppct: 13 formats: fmt: @attributes: type: P tig: atl: A Study on the Feasibility of Active Contours on Automatic CT Bone Segmentation. aug: au: Truc PTH Kim T Lee S Lee Y affil: Department of Computer Engineering, Kyung Hee University, Gyeonggi-do Republic of Korea sug: subj: Tomography, X-Ray Computed Therapy, Computer Assisted Bone and Bones Radiography Radiographic Image Enhancement Human Orthopedic Surgery Computer Simulation Evaluation Research Radiographic Image Interpretation, Computer-Assisted Information Science Funding Source ab: utomatic bone segmentation of computed tomography (CT) images is an important step in image-guided surgery that requires both high accuracy and minimal user interaction. Previous attempts include global thresholding, region growing, region competition, watershed segmentation, and parametric active contour (AC) approaches, but none claim fully satisfactory performance. Recently, geometric or level-set-based AC models have been developed and appear to have characteristics suitable for automatic bone segmentation such as initialization insensitivity and topology adaptability. In this study, we have tested the feasibility of five level-set-based AC approaches for automatic CT bone segmentation with both synthetic and real CT images: namely, the geometric AC, geodesic AC, gradient vector flow fast geometric AC, Chan-Vese (CV) AC, and our proposed density distance augmented CV AC (Aug. CV AC). Qualitative and quantitative evaluations have been made in comparison with the segmentation results from standard commercial software and a medical expert. The first three models showed their robustness to various image contrasts, but their performances decreased much when noise level increased. On the contrary, the CV AC's performance was more robust to noise, yet dependent on image contrast. On the other hand, the Aug. CV AC demonstrated its robustness to both noise and contrast levels and yielded improved performances on a set of real CT data compared with the commercial software, proving its suitability for automatic bone segmentation from CT images. 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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