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A Wavelet-Based Mammographic Image Denoising and Enhancement with Homomorphic Filtering

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Abstract

Breast cancer continues to be a significant public health problem in the world. The diagnosing mammography method is the most effective technology for early detection of the breast cancer. However, in some cases, it is difficult for radiologists to detect the typical diagnostic signs, such as masses and microcalcifications on the mammograms. This paper describes a new method for mammographic image enhancement and denoising based on wavelet transform and homomorphic filtering. The mammograms are acquired from the Faculty of Medicine of the University of Akdeniz and the University of Istanbul in Turkey. Firstly wavelet transform of the mammograms is obtained and the approximation coefficients are filtered by homomorphic filter. Then the detail coefficients of the wavelet associated with noise and edges are modeled by Gaussian and Laplacian variables, respectively. The considered coefficients are compressed and enhanced using these variables with a shrinkage function. Finally using a proposed adaptive thresholding the fine details of the mammograms are retained and the noise is suppressed. The preliminary results of our work indicate that this method provides much more visibility for the suspicious regions.

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Acknowledgement

This research is supported by Istanbul University, Research Fund., with Project No: 2477.

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Authors and Affiliations

  1. Computer Engineering Department, Engineering Faculty, Istanbul University, 34320, Avcilar, Istanbul, Turkey

    Pelin Gorgel & Ahmet Sertbas

  2. Electrical and Electronics Engineering Department, Engineering Faculty, Istanbul University, 34320, Avcilar, Istanbul, Turkey

    Osman N. Ucan

Authors
  1. Pelin Gorgel
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  2. Ahmet Sertbas
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  3. Osman N. Ucan
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Correspondence to Pelin Gorgel.

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Gorgel, P., Sertbas, A. & Ucan, O.N. A Wavelet-Based Mammographic Image Denoising and Enhancement with Homomorphic Filtering. J Med Syst 34, 993–1002 (2010). https://doi.org/10.1007/s10916-009-9316-3

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  • DOI: https://doi.org/10.1007/s10916-009-9316-3

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