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Efficient Denoising Technique for CT images to Enhance Brain Hemorrhage Segmentation.

This paper presents an adaptive denoising approach aiming to improve the visibility and detectability of hemorrhage from brain computed tomography (CT) images. The suggested approach fuses the images denoised by total variation (TV) method, denoised by curvelet-based method, and edge information ext...

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Published in:Journal of Digital Imaging Vol. 25; no. 6; pp. 782 - 792
Main Authors: Bhadauria, H., Dewal, M.
Format: diagnostic images research tables/charts Journal Article
Published: Springer Nature Dec2012
Online Access:View this record in EBSCOhost
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      dt: Dec2012
      vid: 25
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s10278-012-9453-y
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        atl: Efficient Denoising Technique for CT images to Enhance Brain Hemorrhage Segmentation.
      aug:
        au:
          Bhadauria, H.
          Dewal, M.
        affil: Department of Electrical Engineering, Indian Institute of Technology, Roorkee Roorkee-247667 India
      sug:
        subj:
          Radiographic Image Enhancement
          Radiographic Image Interpretation, Computer-Assisted
          Intracranial Hemorrhage Radiography
          Intracranial Hemorrhage Diagnosis
          Tomography, X-Ray Computed
          Evaluation Research
          Sensitivity and Specificity
          False Positive Results
          Human
      ab: This paper presents an adaptive denoising approach aiming to improve the visibility and detectability of hemorrhage from brain computed tomography (CT) images. The suggested approach fuses the images denoised by total variation (TV) method, denoised by curvelet-based method, and edge information extracted from the noise residue of TV method. The edge information is extracted from the noise residue of TV method by processing it through curvelet transform. The visual interpretation shows that the proposed approach not only reduces the staircase effect caused by total variation method but also reduces visual distortion induced by curvelet transform in the homogeneous areas of the CT images. The denoising abilities of the proposed method are further evaluated by segmenting the hemorrhagic brain area using region-growing method. The sensitivity, specificity, Jaccard index, and Dice coefficients were calculated for different noise levels. The comparative results show that the significant improvement has yielded in the brain hemorrhage detection from CT images after denoising it with the proposed approach.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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