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Fuzzy Edge Detection for the Identification of Arachnoid Cysts in Brain Images

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Advances in Mathematical Modelling, Applied Analysis and Computation (ICMMAAC 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 953))

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Abstract

Arachnoid cysts, prevalent yet challenging to accurately delineate in neuroimaging, can cause symptoms including headaches, seizures, and neurological deficits. Edge detection methods have been employed to identify these cysts; however, they often suffer from limitations such as sensitivity to noise and an inability to detect weak edges. This article proposes a novel application using fuzzy edge detection methodology for identifying Arachnoid cysts in brain images, leveraging the robustness and efficiency of fuzzy logic to handle uncertainty and noise. A comparison is drawn between the fuzzy edge detection method and traditional methods i.e., Robert, Sobel, Laplacian, Prewitt, and Canny, hypothesizing superior results with the fuzzy approach. The proposed method employs a Fuzzy Control System (FCS), adaptive thresholding, and inversion of the binary edge map. Implemented in Python due to its adeptness in handling large complex datasets and extensive library support, the method demonstrates clearer and more defined edges, enhancing visualization and interpretation of the cysts. With its robustness against noise and variability in image quality and its adaptability to different imaging conditions and patient cases, this approach shows significant potential for improving the accuracy of arachnoid cyst detection in clinical practice.

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Authors and Affiliations

  1. Department of Mathematics, Statistics and Computer Science, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India

    Sourav Pandey, Ravindra Kumar Singh Rajput,  Dibyanshu, Bharti Kunwar & Tanuja Mathpal

Authors
  1. Sourav Pandey
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  2. Ravindra Kumar Singh Rajput
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  3. Dibyanshu
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  4. Bharti Kunwar
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  5. Tanuja Mathpal
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Corresponding author

Correspondence to Sourav Pandey .

Editor information

Editors and Affiliations

  1. Department of Mathematics, JECRC University, Jaipur, Rajasthan, India

    Jagdev Singh

  2. Department of Mathematics, University of Memphis, Memphis, TN, USA

    George A. Anastassiou

  3. Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon

    Dumitru Baleanu

  4. Department of Mathematics, University of Rajasthan, Jaipur, Rajasthan, India

    Devendra Kumar

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Pandey, S., Rajput, R.K.S., Dibyanshu, Kunwar, B., Mathpal, T. (2024). Fuzzy Edge Detection for the Identification of Arachnoid Cysts in Brain Images. In: Singh, J., Anastassiou, G.A., Baleanu, D., Kumar, D. (eds) Advances in Mathematical Modelling, Applied Analysis and Computation . ICMMAAC 2023. Lecture Notes in Networks and Systems, vol 953. Springer, Cham. https://doi.org/10.1007/978-3-031-56304-1_6

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  • DOI: https://doi.org/10.1007/978-3-031-56304-1_6

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