Tumor Detection and Analysis Using Improved Fuzzy C-Means Algorithm

  • R. Swathika
  • T. Sree Sharmila
  • M. Janani Bharathi
  • S. Jacindha
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


Formation of abnormal cells in brain serves the major cause of tumor. With estimated deaths of 229,000 as of 2015, it has become an issue to be dealt. The less awareness of brain tumor owes to lots of unaccounted deaths. Thus, we aim in developing an app which could serve the purpose of detecting the tumor and giving additional information related to the detected tumor. This app takes in an MRI image and does preprocessing followed by clustering, segmentation, and binarization. The preprocessing involves the conversion of the image into grayscale and noise filtering. We aim at using improved Fuzzy c-means algorithm for clustering and segmentation. Binarization mainly aims at calculating the tumor size useful for further analysis. The improved fuzzy c-means algorithm overcomes the various constraints of k-means algorithm such as time complexity, processing of noisy images, and memory space.


IFCM (Improved fuzzy c-means clustering) Gray scale Noise filtering Binarization Segmentation 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • R. Swathika
    • 1
  • T. Sree Sharmila
    • 1
  • M. Janani Bharathi
    • 1
  • S. Jacindha
    • 1
  1. 1.Department of Information TechnologySSN College of EngineeringChennaiIndia

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