A Noise-robust and Overshoot-free Alternative to Unsharp Masking for Enhancing the Acuity of MR Images.
Poor acutance of images (unsharpness) is one of the major concerns in magnetic resonance imaging (MRI). MRI-based diagnosis and clinical interventions become difficult due to the vague textural information and weak morphological margins on images. A novel image sharpening algorithm named as maximum...
| Publicado en: | Journal of Digital Imaging Vol. 35; no. 4; pp. 1041 - 1061 |
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| Autores principales: | , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2022
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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=159195611&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159195611 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2022 vid: 35 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 159195611 155791450 159195611 159195611 10.1007/s10278-022-00585-z 159195611 ppf: 1041 ppct: 20 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Noise-robust and Overshoot-free Alternative to Unsharp Masking for Enhancing the Acuity of MR Images. aug: au: Edla, Damodar Reddy Simi, V. R. Joseph, Justin affil: Department of Computer Science and Engineering, National Institute of Technology, 403401, Goa, India sug: subj: Algorithms Magnetic Resonance Imaging Radiographic Image Enhancement Human Signal Processing, Computer Assisted Descriptive Statistics Noise Adverse Effects ab: Poor acutance of images (unsharpness) is one of the major concerns in magnetic resonance imaging (MRI). MRI-based diagnosis and clinical interventions become difficult due to the vague textural information and weak morphological margins on images. A novel image sharpening algorithm named as maximum local variation-based unsharp masking (MLVUM) to address the issue of 'unsharpness' in MRI is proposed in this paper. In the MLVUM, the sharpened image is the algebraic sum of the input image and the product of the user-defined scale and the difference between the output of a newly designed nonlinear spatial filter named maximum local variation-controlled edge smoothing Gaussian filter (MLVESGF) and the input image, weighted by the normalised MLV. The MLVESGF is a locally adaptive 2D Gaussian edge smoothing kernel whose standard deviation is directly proportional to the local value of the normalized MLV. The values of the acutance-to-noise ratio (ANR) and absolute mean brightness error (AMBE) shown by the MLVUM on 100 MRI slices are 0.6463 ± 0.1852 and 0.3323 ± 0.2200, respectively. Compared to 17 state-of-the-art image sharpening algorithms, the MLVUM exhibited a higher ANR and lower AMBE. The MLVUM selectively enhances the sharpness of edges in the MR images without amplifying the background noise without altering the mean brightness level. 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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