An Automatic Parameter Decision System of Bilateral Filtering with GPU-Based Acceleration for Brain MR Images.
Bilateral filters have been extensively utilized in a number of image denoising applications such as segmentation, registration, and tissue classification. However, it requires burdensome adjustments of the filter parameters to achieve the best performance for each individual image. To address this...
| Published in: | Journal of Digital Imaging Vol. 32; no. 1; pp. 148 - 162 |
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| Main Authors: | , , |
| Format: | diagnostic images equations & formulas research tables/charts Journal Article |
| Published: |
Springer Nature
Feb2019
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=134830552&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 134830552 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2019 vid: 32 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 134830552 134830552 134830552 10.1007/s10278-018-0110-y 134830552 ppf: 148 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An Automatic Parameter Decision System of Bilateral Filtering with GPU-Based Acceleration for Brain MR Images. aug: au: Chang, Herng-Hua Lin, Yu-Ju Zhuang, Audrey Haihong affil: Computational Biomedical Engineering Laboratory (CBEL), Department of Engineering Science and Ocean Engineering, National Taiwan University, 1 Sec. 4 Roosevelt Road, Daan, 10617, Taipei, Taiwan sug: subj: Magnetic Resonance Imaging Methods Diagnosis, Brain Methods Automation Computer Graphics Decision Support Systems, Clinical Computer-Aided Design Neural Networks (Computer) Sensitivity and Specificity Human Conceptual Framework Computer Processor Decision Making, Computer Assisted Image Processing, Computer Assisted ab: Bilateral filters have been extensively utilized in a number of image denoising applications such as segmentation, registration, and tissue classification. However, it requires burdensome adjustments of the filter parameters to achieve the best performance for each individual image. To address this problem, this paper proposes a computer-aided parameter decision system based on image texture features associated with neural networks. In our approach, parallel computing with the GPU architecture is first developed to accelerate the computation of the conventional bilateral filter. Subsequently, a back propagation network (BPN) scheme using significant image texture features as the input is established to estimate the GPU-based bilateral filter parameters and its denoising process. The k-fold cross validation method is exploited to evaluate the performance of the proposed automatic restoration framework. A wide variety of T1-weighted brain MR images were employed to train and evaluate this parameter-free decision system with GPU-based bilateral filtering, which resulted in a speed-up factor of 208 comparing to the CPU-based computation. The proposed filter parameter prediction system achieved a mean absolute percentage error (MAPE) of 6% and was classified as "high accuracy". Our automatic denoising framework dramatically removed noise in numerous brain MR images and outperformed several state-of-the-art methods based on the peak signal-to-noise ratio (PSNR). The usage of image texture features associated with the BPN to estimate the GPU-based bilateral filter parameters and to automate the denoising process is feasible and validated. It is suggested that this automatic restoration system is advantageous to various brain MR image-processing applications. 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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