Application of Improved Homogeneity Similarity-Based Denoising in Optical Coherence Tomography Retinal Images.
Image denoising is a fundamental preprocessing step of image processing in many applications developed for optical coherence tomography (OCT) retinal imaging-a high-resolution modality for evaluating disease in the eye. To make a homogeneity similarity-based image denoising method more suitable for...
| Publicado en: | Journal of Digital Imaging Vol. 28; no. 3; pp. 346 - 362 |
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| Autores principales: | , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2015
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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=103803492&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103803492 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2015 vid: 28 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 103803492 102855462 10.1007/s10278-014-9742-8 NLM25404105 PMC4441691 103803492 ppf: 346 ppct: 16 formats: fmt: @attributes: type: P tig: atl: Application of Improved Homogeneity Similarity-Based Denoising in Optical Coherence Tomography Retinal Images. aug: au: Chen, Qiang Sisternes, Luis Leng, Theodore Rubin, Daniel affil: Department of Radiology, Stanford University, Stanford 94305 USA sug: subj: Retina Radiography Tomography, Optical Coherence Image Processing, Computer Assisted Methods Algorithms Evaluation Research Multimethod Studies Human Funding Source ab: Image denoising is a fundamental preprocessing step of image processing in many applications developed for optical coherence tomography (OCT) retinal imaging-a high-resolution modality for evaluating disease in the eye. To make a homogeneity similarity-based image denoising method more suitable for OCT image removal, we improve it by considering the noise and retinal characteristics of OCT images in two respects: (1) median filtering preprocessing is used to make the noise distribution of OCT images more suitable for patch-based methods; (2) a rectangle neighborhood and region restriction are adopted to accommodate the horizontal stretching of retinal structures when observed in OCT images. As a performance measurement of the proposed technique, we tested the method on real and synthetic noisy retinal OCT images and compared the results with other well-known spatial denoising methods, including bilateral filtering, five partial differential equation (PDE)-based methods, and three patch-based methods. Our results indicate that our proposed method seems suitable for retinal OCT imaging denoising, and that, in general, patch-based methods can achieve better visual denoising results than point-based methods in this type of imaging, because the image patch can better represent the structured information in the images than a single pixel. However, the time complexity of the patch-based methods is substantially higher than that of the others. 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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