Region-Based Nasopharyngeal Carcinoma Lesion Segmentation from MRI Using Clustering- and Classification-Based Methods with Learning.
In clinical diagnosis of nasopharyngeal carcinoma (NPC) lesion, clinicians are often required to delineate boundaries of NPC on a number of tumor-bearing magnetic resonance images, which is a tedious and time-consuming procedure highly depending on expertise and experience of clinicians. Computer-ai...
| Publicado en: | Journal of Digital Imaging Vol. 26; no. 3; pp. 472 - 483 |
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| Autores principales: | , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2013
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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=104285070&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104285070 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2013 vid: 26 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104285070 87498138 10.1007/s10278-012-9520-4 NLM22854973 PMC3649041 104285070 ppf: 472 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Region-Based Nasopharyngeal Carcinoma Lesion Segmentation from MRI Using Clustering- and Classification-Based Methods with Learning. aug: au: Huang, Wei Chan, Kap Zhou, Jiayin affil: Information Engineering School, Nanchang University, China, No. 999, New Xuefu Road, Honggutan Nanchang 330031 China sug: subj: Nasopharyngeal Neoplasms Diagnosis Magnetic Resonance Imaging Radiographic Image Interpretation, Computer-Assisted Diagnosis, Computer Assisted Radiographic Magnification Algorithms User-Computer Interface Artificial Intelligence Evaluation Research One-Way Analysis of Variance Confidence Intervals Stratified Random Sample Human ab: In clinical diagnosis of nasopharyngeal carcinoma (NPC) lesion, clinicians are often required to delineate boundaries of NPC on a number of tumor-bearing magnetic resonance images, which is a tedious and time-consuming procedure highly depending on expertise and experience of clinicians. Computer-aided tumor segmentation methods (either contour-based or region-based) are necessary to alleviate clinicians' workload. For contour-based methods, a minimal user interaction to draw an initial contour inside or outside the tumor lesion for further curve evolution to match the tumor boundary is preferred, but parameters within most of these methods require manual adjustment, which is technically burdensome for clinicians without specific knowledge. Therefore, segmentation methods with a minimal user interaction as well as automatic parameters adjustment are often favored in clinical practice. In this paper, two region-based methods with parameters learning are introduced for NPC segmentation. Two hundred fifty-three MRI slices containing NPC lesion are utilized for evaluating the performance of the two methods, as well as being compared with other similar region-based tumor segmentation methods. Experimental results demonstrate the superiority of adopting learning in the two introduced methods. Also, they achieve comparable segmentation performance from a statistical point of view. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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