Automatic Labeling of MR Brain Images Through the Hashing Retrieval Based Atlas Forest.
The multi-atlas method is one of the efficient and common automatic labeling method, which uses the prior information provided by expert-labeled images to guide the labeling of the target. However, most multi-atlas-based methods depend on the registration that may not give the correct information du...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 8 |
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| Autores principales: | , , , , |
| Formato: | algorithm computer program diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2019
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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=137490044&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137490044 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Aug2019 vid: 43 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137490044 137490044 137490044 10.1007/s10916-019-1385-3 137490044 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automatic Labeling of MR Brain Images Through the Hashing Retrieval Based Atlas Forest. aug: au: Liu, Hong Xu, Lijun Song, Enmin Jin, Renchao Hung, Chih-Cheng affil: School of Computer Science and Technology, Huazhong University of Science and Technology, 430074, Wuhan, Hubei, China sug: subj: Signal Processing, Computer Assisted Magnetic Resonance Imaging Methods Image Processing, Computer Assisted Radiographic Image Interpretation, Computer-Assisted Automation Methods Image Retrieval Image Retrieval Systems Algorithms Decision Trees Software Design Human Funding Source ab: The multi-atlas method is one of the efficient and common automatic labeling method, which uses the prior information provided by expert-labeled images to guide the labeling of the target. However, most multi-atlas-based methods depend on the registration that may not give the correct information during the label propagation. To address the issue, we designed a new automatic labeling method through the hashing retrieval based atlas forest. The proposed method propagates labels without registration to reduce the errors, and constructs a target-oriented learning model to integrate information among the atlases. This method innovates a coarse classification strategy to preprocess the dataset, which retains the integrity of dataset and reduces computing time. Furthermore, the method considers each voxel in the atlas as a sample and encodes these samples with hashing for the fast sample retrieval. In the stage of labeling, the method selects suitable samples through hashing learning and trains atlas forests by integrating the information from the dataset. Then, the trained model is used to predict the labels of the target. Experimental results on two datasets illustrated that the proposed method is promising in the automatic labeling of MR brain images. pubtype: Academic Journal doctype: algorithm computer program diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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