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A New Fuzzy Clustering Algorithm by Incorporating Constrained Class Uncertainty-Based Entropy for Brain MR Image Segmentation

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Computer Vision and Image Processing (CVIP 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1147))

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Abstract

We propose a new fuzzy clustering algorithm by incorporating constrained class uncertainty-based entropy for brain MR image segmentation. Due to deficiencies of MRI machines, the brain MR images are affected by noise and intensity inhomogeneity (IIH), resulting unsharp tissue boundaries with low resolution. As a result, standard fuzzy clustering algorithms fail to classify pixels properly, especially using only pixel intensity values. We mitigate this difficulty by introducing entropy that measures constrained class uncertainty for each pixel. The value of this entropy is more for the pixels in the unsharp tissue boundaries. Apart from using the fuzzy membership function, we also define the similarity as the complement of a measure characterized by a Gaussian density function in non-Euclidean space to reduce the affect of noise and IIH. By introducing a regularization parameter, the trade-off between the fuzzy membership function and class uncertainty-based measure is resolved. The proposed algorithm is assessed both in qualitatively and quantitatively on several brain MR images of a benchmark database and two clinical data. The simulation results show that the proposed algorithm outperforms some of the fuzzy-based state-of-the-art methods devised in recent past when evaluated in terms of cluster validity functions, segmentation accuracy and Dice coefficient.

This work is partially supported the SERB, Govt. of India (File No: EEQ/2016/000145).

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Acknowledgment

This work is supported the SERB, Govt. of India (File No: EEQ/2016/000145). We are also grateful to radiologists Dr. S. K. Sharma and Dr. Sumita Kundu of the EKO X-Ray & Imaging Institute, Jawaharlal Nehru Road, Kolkata, for their support and providing clinical brain MR data. Authors are also thankful to Mr. Banshadhar Nandi and Mr. Niloy Halder, Sr. Technologist (Imaging), AMRI Hospital, Dhakuria, for their support and providing the clinical data. Moreover, authors convey their profound indebtedness to radiologist Dr. Amitabha Bhattacharyya for his invaluable suggestions and support.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India

    Nabanita Mahata & Jamuna Kanta Sing

Authors
  1. Nabanita Mahata
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  2. Jamuna Kanta Sing
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Correspondence to Jamuna Kanta Sing .

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Editors and Affiliations

  1. Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Neeta Nain

  2. Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Santosh Kumar Vipparthi

  3. Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Balasubramanian Raman

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Mahata, N., Sing, J.K. (2020). A New Fuzzy Clustering Algorithm by Incorporating Constrained Class Uncertainty-Based Entropy for Brain MR Image Segmentation. In: Nain, N., Vipparthi, S., Raman, B. (eds) Computer Vision and Image Processing. CVIP 2019. Communications in Computer and Information Science, vol 1147. Springer, Singapore. https://doi.org/10.1007/978-981-15-4015-8_27

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  • DOI: https://doi.org/10.1007/978-981-15-4015-8_27

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-4014-1

  • Online ISBN: 978-981-15-4015-8

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