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A novel fuzzy approach for segmenting medical images

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Abstract

In various medical imaging studies, the segmentation of the medical image is a crucial step. Due to the inherent behavior of the images, the computerized tomography (CT) scan/magnetic resonance imaging (MRI) images are not clear. As the medical images are captured using electronic devices, these images are ambiguous and unclear. This ambiguity in the images is due to factors related to noise and the environment while capturing images. A fuzzy set is a valuable method to deal with data uncertainty. In this paper, a novel clustering approach based upon a fuzzy concept is proposed, which enhances the vague MRI/CT scan image before segmentation. Initially, the image noise is minimized by processing eight immediate neighborhood pixels around each pixel in an image. After noise processing, upper and lower membership levels of the image are computed. Hamacher T-conorm is used as an aggregation operator to construct a new membership function using upper and lower membership levels. The enhanced image, created using the new membership function, is then segmented using Gaussian kernel-based fuzzy c means clustering. The experiment is performed on MRI/CT scan brain tumor images. Real data experiments demonstrate that the proposed algorithm has better performance when compared with existing methods both quantitatively and qualitatively. It is observed that even in the presence of noise, the proposed method exhibits better efficiency.

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Authors and Affiliations

  1. Maharaja Surajmal Institute of Technology, GGSIP University, New Delhi, India

    Prabhjot Kaur

  2. Aravali Pharma and Lifesciences, Dwarka, New Delhi, India

    Tamalika Chaira

Authors
  1. Prabhjot Kaur
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  2. Tamalika Chaira
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Correspondence to Prabhjot Kaur.

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Communicated by V. Loia.

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Kaur, P., Chaira, T. A novel fuzzy approach for segmenting medical images. Soft Comput 25, 3565–3575 (2021). https://doi.org/10.1007/s00500-020-05386-6

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