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Denoising of Computed Tomography Images for Improved Performance of Medical Devices in Biomedical Engineering

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Advances in Computing and Data Sciences (ICACDS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1441))

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Abstract

Image preprocessing is the most significant step for improved segmentation and classification in the Computer Aided Diagnosis (CAD) system. Certain noises such as Gaussian and Poisson destroy the performance of the CAD system. There are various types of medical images such as Computed tomography (CT), Magnetic resonance imaging (MRI), Positron emission tomography (PET) and Ultrasound. CT is the most commonly used imaging modality used in the detection of pancreatic cancer responsible for an increased rate of deaths globally. These images are attenuated during compression, transmission and acquisition which cannot be removed completely. The paper presents the comparative analysis of the filters used for denoising the CT images in order to improve the accuracy of the segmentation and classification for the improved performance of the CAD system. The Peak-signal-to-noise (PSNR), Mean square error (MSE) and Structural similarity index (SSIM) have been calculated for the images denoised using the anisotropic diffusion filter, wavelet filter, bilateral filter, Non-local mean filter (NLM), wiener filter, total variation filter (TV) BM3D filter, median and Gaussian filter. The wavelet filter outperformed the other filters with 28.43, 103.12 and 0.59 values of PSNR, MSE and SSIM respectively. The suggested approach can be embedded in CT scanners to filter out the noises present in images with inbuilt technology and improve the performance of diagnostic devices.

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Authors and Affiliations

  1. Computer Science and Engineering, UIET, Panjab University, Chandigarh, India

    Harjinder Kaur, Deepti Gupta & Mamta Juneja

Authors
  1. Harjinder Kaur
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  2. Deepti Gupta
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  3. Mamta Juneja
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Corresponding author

Correspondence to Mamta Juneja .

Editor information

Editors and Affiliations

  1. Consilio Research Lab, Tallinn, Estonia

    Mayank Singh

  2. Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India

    Vipin Tyagi

  3. Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India

    P. K. Gupta

  4. Institute of Information Theory and Automation, Prague, Czech Republic

    Jan Flusser

  5. University of Ottawa, Ottawa, ON, Canada

    Tuncer Ören

  6. MVPS's Karmaveer Adv. Baburao Ganpatrao Thakare College of Engineering, Nashik, Maharashtra, India

    V. R. Sonawane

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Kaur, H., Gupta, D., Juneja, M. (2021). Denoising of Computed Tomography Images for Improved Performance of Medical Devices in Biomedical Engineering. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T., Sonawane, V.R. (eds) Advances in Computing and Data Sciences. ICACDS 2021. Communications in Computer and Information Science, vol 1441. Springer, Cham. https://doi.org/10.1007/978-3-030-88244-0_14

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  • DOI: https://doi.org/10.1007/978-3-030-88244-0_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88243-3

  • Online ISBN: 978-3-030-88244-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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