Rician Denoising Based on Correlated Local Features LMMSE Approach.
In this study we propose a novel correction scheme that filters Magnetic Resonance Images data, by using a modified Linear Minimum Mean Square Error (LMMSE) estimator which takes into account the joint information of the local features. A closed-form analytical solution for our estimator is presente...
| Publicado en: | Journal of Medical Systems Vol. 45; no. 4; pp. 1 - 13 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2021
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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=149631216&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149631216 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Apr2021 vid: 45 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 149631216 149631216 149631216 10.1007/s10916-020-01696-2 149631216 ppf: 1 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Rician Denoising Based on Correlated Local Features LMMSE Approach. aug: au: Kinani, Jean Marie Vianney Silva, Alberto Rosales Mújica-Vargas, Dante Funes, Francisco Gallegos Díaz, Eduardo Ramos affil: Instituto Politécnico Nacional-UPIIH, San Agustín Tlaxiaca-Hidalgo, México sug: subj: Noise Prevention and Control Magnetic Resonance Imaging Methods Signal Processing, Computer Assisted Methods Models, Statistical Equipment Reliability Neural Networks (Computer) Descriptive Statistics Algorithms Computer Simulation Image Interpretation, Computer Assisted Image Processing, Computer Assisted Noise Evaluation Sensitivity and Specificity Experimental Studies Quantitative Studies ab: In this study we propose a novel correction scheme that filters Magnetic Resonance Images data, by using a modified Linear Minimum Mean Square Error (LMMSE) estimator which takes into account the joint information of the local features. A closed-form analytical solution for our estimator is presented and it proves to make the filtering process far simpler and faster than other estimation techniques that rely on iterative optimization scheme and require multiple data samples. An experimental validation of our correction scheme was carried out through large scale experiments using both clinical and synthetic MR images, artificially corrupted with rician noise of σ varying from 1 to 40. These noisy images were filtered using our proposed method against the classical LMMSE, the Non-Local Means filter and the Nonlocality-Reinforced Convolutional Neural Networks (NRCNN) techniques. The results show an outstanding performance of our proposed method, given the fact that from σ ≈ 12 onwards, the proposed method outperforms all other methods. Another attention-grabbing feature of our method is that its Structural Similarity does not vary sharply [0.87, 0.95] across the σ spectrum as the other three techniques, which implies that this method can work on a wider range of deteriorated images than the rest of the techniques. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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