Mammographic Image Denoising and Enhancement Using the Anscombe Transformation, Adaptive Wiener Filtering, and the Modulation Transfer Function.
A new restoration methodology is proposed to enhance mammographic images through the improvement of contrast features and the simultaneous suppression of noise. Denoising is performed in the first step using the Anscombe transformation to convert the signal-dependent quantum noise into an approximat...
| Publicado en: | Journal of Digital Imaging Vol. 26; no. 2; pp. 183 - 198 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2013
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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=104249468&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104249468 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2013 vid: 26 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104249468 86060000 10.1007/s10278-012-9507-1 NLM22806627 PMC3597965 104249468 ppf: 183 ppct: 15 formats: fmt: @attributes: type: P tig: atl: Mammographic Image Denoising and Enhancement Using the Anscombe Transformation, Adaptive Wiener Filtering, and the Modulation Transfer Function. aug: au: Romualdo, Larissa Vieira, Marcelo Schiabel, Homero Mascarenhas, Nelson Borges, Lucas affil: Electrical Engineering Department, University of São Paulo, USP, Av. Trabalhador São-Carlense, 400 São Carlos Brazil sug: subj: Mammography Breast Neoplasms Diagnosis Breast Radiography Breast Neoplasms Radiography Radiographic Magnification Methods Diagnosis, Computer Assisted Algorithms Radiography, Computed Calcinosis Diagnosis Evaluation Research Paired T-Tests P-Value Sensitivity and Specificity ROC Curve Human Funding Source ab: A new restoration methodology is proposed to enhance mammographic images through the improvement of contrast features and the simultaneous suppression of noise. Denoising is performed in the first step using the Anscombe transformation to convert the signal-dependent quantum noise into an approximately signal-independent Gaussian additive noise. In the Anscombe domain, noise is filtered through an adaptive Wiener filter, whose parameters are obtained by considering local image statistics. In the second step, a filter based on the modulation transfer function of the imaging system in the whole radiation field is applied for image enhancement. This methodology can be used as a preprocessing module for computer-aided detection (CAD) systems to improve the performance of breast cancer screening. A preliminary assessment of the restoration algorithm was performed using synthetic images with different levels of quantum noise. Afterward, we evaluated the effect of the preprocessing on the performance of a previously developed CAD system for clustered microcalcification detection in mammographic images. The results from the synthetic images showed an increase of up to 11.5 dB ( p = 0.002) in the peak signal-to-noise ratio. Moreover, the mean structural similarity index increased up to 8.3 % ( p < 0.001). Regarding CAD performance, the results suggested that the preprocessing increased the detectability of microcalcifications in mammographic images without increasing the false-positive rates. Receiver operating characteristic analysis revealed an average increase of 14.1 % ( p = 0.01) in overall CAD performance when restored image sets were used. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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