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Improving Noisy T1-Weighted MRI Spatial Fuzzy Segmentation Based on a Hybrid of Stationary Wavelet Thresholding and Filtering Preprocess

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Current Approaches in Applied Artificial Intelligence (IEA/AIE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9101))

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Abstract

This paper proposes an improved spatial fuzzy segmentation of a noisy image, based on a hybrid of stationary wavelet thresholding and filtering preprocess. The proposed methods aim to improve the segmentation by reducing the effect of additive noise during preprocess. Noise filtering as well as wavelet thresholding are carried out in each stationary wavelet subbands. Thus, noise distributed across any subband coefficients can be examined. This would lead to image denoising improvement. Afterwards, fuzzy c-means incorporated with spatial information (sFCM) is utilized for segmenting the denoised image. The denoising preprocess and segmentation measurements rely on peak signal-to-noise ratio (PSNR) and Xie-Beni (XB) validity index respectively. T1-weighted MRI is tested with salt-and-pepper and Gaussian additive noise. Based on experimental results, the proposed hybrid methods improve the segmentation more efficiently than comparative traditional denoising methods.

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Authors and Affiliations

  1. School of Information Technology, Rangsit University, Pathumthani, Thailand

    Papangkorn Pidchayathanakorn & Siriporn Supratid

Authors
  1. Papangkorn Pidchayathanakorn
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  2. Siriporn Supratid
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Correspondence to Siriporn Supratid .

Editor information

Editors and Affiliations

  1. Texas State University, San Marcos, Texas, USA

    Moonis Ali

  2. Dongguk University, Seoul, Korea, Republic of (South Korea)

    Young Sig Kwon

  3. Dongguk University, Seoul, Korea, Republic of (South Korea)

    Chang-Hwan Lee

  4. Dongguk University, Seoul, Korea, Republic of (South Korea)

    Juntae Kim

  5. Seoul National University, Seoul, Korea, Republic of (South Korea)

    Yongdai Kim

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Pidchayathanakorn, P., Supratid, S. (2015). Improving Noisy T1-Weighted MRI Spatial Fuzzy Segmentation Based on a Hybrid of Stationary Wavelet Thresholding and Filtering Preprocess. In: Ali, M., Kwon, Y., Lee, CH., Kim, J., Kim, Y. (eds) Current Approaches in Applied Artificial Intelligence. IEA/AIE 2015. Lecture Notes in Computer Science(), vol 9101. Springer, Cham. https://doi.org/10.1007/978-3-319-19066-2_57

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  • DOI: https://doi.org/10.1007/978-3-319-19066-2_57

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19065-5

  • Online ISBN: 978-3-319-19066-2

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