Adaptive segmentation of cerebrovascular tree in time-of-flight magnetic resonance angiography.
Accurate segmentation of the human vasculature is an important prerequisite for a number of clinical procedures, such as diagnosis, image-guided neurosurgery and pre-surgical planning. In this paper, an improved statistical approach to extracting whole cerebrovascular tree in time-of-flight magnetic...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 46; no. 1; pp. 75 - 84 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Jan2008
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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=105217999&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105217999 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jan2008 vid: 46 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 105217999 NLM17846808 2010070445 10.1007/s11517-007-0244-4 NLM17846808 105217999 ppf: 75 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Adaptive segmentation of cerebrovascular tree in time-of-flight magnetic resonance angiography. aug: au: Hao JT Li ML Tang FL Hao, J T Li, M L Tang, F L affil: Department of Computer Science and Engineering Shanghai, Jiaotong University, Min Hang, Shanghai, People's Republic of China sug: subj: Brain Blood Supply Magnetic Resonance Angiography Methods Algorithms Blood Vessels Anatomy and Histology Human Image Interpretation, Computer Assisted Methods Phantoms, Imaging ab: Accurate segmentation of the human vasculature is an important prerequisite for a number of clinical procedures, such as diagnosis, image-guided neurosurgery and pre-surgical planning. In this paper, an improved statistical approach to extracting whole cerebrovascular tree in time-of-flight magnetic resonance angiography is proposed. Firstly, in order to get a more accurate segmentation result, a localized observation model is proposed instead of defining the observation model over the entire dataset. Secondly, for the binary segmentation, an improved Iterative Conditional Model (ICM) algorithm is presented to accelerate the segmentation process. The experimental results showed that the proposed algorithm can obtain more satisfactory segmentation results and save more processing time than conventional approaches, simultaneously. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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