Brain Tissue Classification using PCA with Hybrid Clustering Algorithms

  • Authors

    • Yepuganti Karuna
    • Saritha Saladi
    • Budhaditya Bhattacharyya
    2018-04-25
    https://doi.org/10.14419/ijet.v7i2.24.12155
  • ABC algorithm, FCM clustering, MRI, PCA, T1W-Brain images.

  • Abstract

    Distinct algorithms were developed to segment the MRI images, to satisfy the accuracy in segmenting the regions of the brain. In this paper, we proposed a novel methodology for segmenting the MRI brain images using the clustering techniques. The Modified Fuzzy C-Means (MFCM) algorithm is pooled with the Artificial Bee Colony (ABC) algorithm after denoising images, features are extracted using Principal Component Analysis (PCA) for better results of segmentation. This improves the ability to extract the regions (cluster centres) and cells in the normal and abnormal brain MRI images. The comparative analysis of proposed methodology with existing FCM, ABC algorithms is evaluated in terms of Minkowski score. The proposed MFCM-ABC method is more robust and efficient to hostile noise in images when compared to existing FCM and ABC methods.

                                                                                                                                    

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  • How to Cite

    Karuna, Y., Saladi, S., & Bhattacharyya, B. (2018). Brain Tissue Classification using PCA with Hybrid Clustering Algorithms. International Journal of Engineering & Technology, 7(2.24), 536-540. https://doi.org/10.14419/ijet.v7i2.24.12155

    Received date: 2018-04-25

    Accepted date: 2018-04-25

    Published date: 2018-04-25