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Performance Analysis of Supervised & Unsupervised Techniques for Brain Tumor Detection and Segmentation from MR Images

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Book cover Proceedings of the International Conference on Intelligent Systems and Signal Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 671))

Abstract

Brain tumor detection and segmentation from the magnetic resonance images (MRI) is a difficult task as in the MR brain images, various tissues such as white matter, gray matter, and cerebrospinal fluid have complicated structures that make it difficult to segment the tumor. An automated system for brain tumor detection and segmentation will help the patients for proper treatment planning. Also, it will improve the diagnosis and reduce the diagnostic time. Segmentation of brain tumor MR images is the most difficult task as the tumor varies in terms of size, shape, location, and texture. In this paper, we discuss various supervised and unsupervised techniques for brain tumor detection and segmentation such as K-nearest neighbor (K-NN), K-means clustering, and using morphological operators. We also review the results obtained.

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Authors and Affiliations

  1. Information Technology, Dharmsinh Desai University, Nadiad, India

    Brijesha D. Rao & Mukesh M. Goswami

Authors
  1. Brijesha D. Rao
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  2. Mukesh M. Goswami
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Corresponding author

Correspondence to Brijesha D. Rao .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, G.H. Patel College of Engineering and Technology, Vallabh Vidyanagar, Gujarat, India

    Rahul Kher

  2. Department of Information Technology, G.H. Patel College of Engineering and Technology, Vallabh Vidyanagar, Gujarat, India

    Dr. Nikhil Gondaliya

  3. Department of Electrical Engineering, G.H. Patel College of Engineering and Technology, Vallabh Vidyanagar, Gujarat, India

    Mukesh Bhesaniya

  4. SnT, University of Luxembourg, Esch-sur-Alzette, Luxembourg

    Latif Ladid

  5. University of Oklahoma, Norman, Oklahoma, USA

    Mohammed Atiquzzaman

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Rao, B.D., Goswami, M.M. (2018). Performance Analysis of Supervised & Unsupervised Techniques for Brain Tumor Detection and Segmentation from MR Images. In: Kher, R., Gondaliya, D., Bhesaniya, M., Ladid, L., Atiquzzaman, M. (eds) Proceedings of the International Conference on Intelligent Systems and Signal Processing . Advances in Intelligent Systems and Computing, vol 671. Springer, Singapore. https://doi.org/10.1007/978-981-10-6977-2_4

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  • DOI: https://doi.org/10.1007/978-981-10-6977-2_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6976-5

  • Online ISBN: 978-981-10-6977-2

  • eBook Packages: EngineeringEngineering (R0)

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