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An Intuitionistic Fuzzy C-Means and Local Information-Based DCT Filtering for Fast Brain MRI Segmentation

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Abstract

Structural and photometric anomalies in the brain magnetic resonance images (MRIs) affect the segmentation performance. Moreover, a sudden change in intensity between two boundaries of the brain tissues makes it prone to data uncertainty, resulting in the misclassification of the pixels lying near the cluster boundaries. The discrete cosine transform (DCT) domain-based filtering is an effective way to deal with structural and photometric anomalies, while the intuitionistic fuzzy C-means (IFCM) clustering can handle the uncertainty using the intuitionistic fuzzy set (IFS) theory. In this background, we propose two novel approaches, namely, the DCT-based intuitionistic fuzzy C-means (DCT-IFCM) and the DCT-based local information IFCM (DCT-LIFCM), which effectively deal with the Rician and Gaussian noises and also handle the data uncertainty problem to provide high segmentation accuracy. The DCT-IFCM approach performs the histogram-based segmentation, while the DCT-LIFCM uses the pixel-wise computation to include the spatial information. Although the DCT-LIFCM delivers slightly better performance than the DCT-IFCM, the latter is very fast in providing equally high segmentation accuracy. An exhaustive performance analysis is provided to demonstrate the superior performance of the proposed algorithms compared with the state-of-the-art algorithms, including those based on the DCT-based filtering approach and the IFS theory.

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Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Computer Science, Punjabi University, Patiala, 147002, India

    Chandan Singh, Sukhjeet Kaur Ranade & Dalvinder Kaur

  2. Department of Computer Science and Applications, Sharda School of Engineering & Technology, Sharda University, Greater Noida, 201310, India

    Anu Bala

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Contributions

All authors contributed to the study's conception and design. All authors read and approved the final manuscript. Conceptualization, Methodology, Software, Formal analysis, Writing-review & editing, and Supervision are performed by Chandan Singh. Sukhjeet Kaur performs conceptualization, Visualization, Validation, Writing-review & editing. Conceptualization, Software, Validation, Formal analysis, Data collection, and Writing the first original draft are performed by Dalvinder Kaur. Anu Bala performs conceptualization, Visualization, Validation, Writing-review & editing.

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Correspondence to Anu Bala.

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The research does not involve any human or animal participant, their data, or biological material. No ethical approval is required. The datasets analyzed during the current study are BrainWeb and IBSR, which are publically available at https://www.bic.mni.mcgill.ca/brainweb/ and http://www.cma.mgh.Harvard.edu/ibsr , respectively.

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Appendix

Appendix

Table 7 A summary of various categories for fuzzy C-means clustering, methods pertaining to a category, acronym, year of publication, and reference in the paper
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Singh, C., Ranade, S.K., Kaur, D. et al. An Intuitionistic Fuzzy C-Means and Local Information-Based DCT Filtering for Fast Brain MRI Segmentation. J Digit Imaging. Inform. med. (2024). https://doi.org/10.1007/s10278-023-00899-6

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