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Fuzzy Logic Based Edge Detection in Smooth and Noisy Clinical Images

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  • Izhar Haq
  • Shahzad Anwar
  • Kamran Shah
  • Muhammad Tahir Khan
  • Shaukat Ali Shah

Abstract

Edge detection has beneficial applications in the fields such as machine vision, pattern recognition and biomedical imaging etc. Edge detection highlights high frequency components in the image. Edge detection is a challenging task. It becomes more arduous when it comes to noisy images. This study focuses on fuzzy logic based edge detection in smooth and noisy clinical images. The proposed method (in noisy images) employs a 3×3 mask guided by fuzzy rule set. Moreover, in case of smooth clinical images, an extra mask of contrast adjustment is integrated with edge detection mask to intensify the smooth images. The developed method was tested on noise-free, smooth and noisy images. The results were compared with other established edge detection techniques like Sobel, Prewitt, Laplacian of Gaussian (LOG), Roberts and Canny. When the developed edge detection technique was applied to a smooth clinical image of size 270×290 pixels having 24 dB ‘salt and pepper’ noise, it detected very few (22) false edge pixels, compared to Sobel (1931), Prewitt (2741), LOG (3102), Roberts (1451) and Canny (1045) false edge pixels. Therefore it is evident that the developed method offers improved solution to the edge detection problem in smooth and noisy clinical images.

Suggested Citation

  • Izhar Haq & Shahzad Anwar & Kamran Shah & Muhammad Tahir Khan & Shaukat Ali Shah, 2015. "Fuzzy Logic Based Edge Detection in Smooth and Noisy Clinical Images," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-17, September.
    • Handle: RePEc:plo:pone00:0138712
    DOI: 10.1371/journal.pone.0138712
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    Cited by:

    1. Ebrahim Navid Sadjadi & Danial Sadrian Zadeh & Behzad Moshiri & Jesús García Herrero & Jose Manuel Molina López & Roemi Fernández, 2022. "Application of Smooth Fuzzy Model in Image Denoising and Edge Detection," Mathematics, MDPI, vol. 10(14), pages 1-25, July.
    2. V S Bharath Kurukuru & Ahteshamul Haque & Arun Kumar Tripathy & Mohammed Ali Khan, 2022. "Machine learning framework for photovoltaic module defect detection with infrared images," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(4), pages 1771-1787, August.
    3. Abdullah-Al Nahid & Tariq M. Khan & Yinan Kong, 2017. "Hardware Implementation of Bone Fracture Detector Using Fuzzy Method Along with Local Normalization Technique," Annals of Data Science, Springer, vol. 4(4), pages 533-546, December.

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