phat 21(26): e2

Research Article

Enhanced Brain Tumour MRI Segmentation using K-means with machine learning based PSO and Firefly Algorithm

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  • @ARTICLE{10.4108/eai.3-2-2021.168600,
        author={Anjali Kapoor and Rekha Agarwal},
        title={Enhanced Brain Tumour MRI Segmentation using K-means   with machine learning based PSO and Firefly Algorithm},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        volume={7},
        number={26},
        publisher={EAI},
        journal_a={PHAT},
        year={2021},
        month={2},
        keywords={Magnetic Resonance Imaging (MRI), K-means, Machine Learning, Particle Swarm Optimization (PSO), Firefly Algorithm (FA)},
        doi={10.4108/eai.3-2-2021.168600}
    }
    
  • Anjali Kapoor
    Rekha Agarwal
    Year: 2021
    Enhanced Brain Tumour MRI Segmentation using K-means with machine learning based PSO and Firefly Algorithm
    PHAT
    EAI
    DOI: 10.4108/eai.3-2-2021.168600
Anjali Kapoor1,*, Rekha Agarwal2
  • 1: Research Scholar, University School of Information, Communication and Technology, Guru Gobind Singh Indraprastha University, New Delhi, India
  • 2: Professor, Department of Electronics and Communication Engineering, Amity School of Engineering and Technology, Amity Campus, Noida, India
*Contact email: kapoor12anjali@gmail.com

Abstract

INTRODUCTION: Medical image segmentation is usually integrated as a critical step in medical image analysis, often associated with numerous clinical applications. Magnetic Resonance Imaging (MRI) provides detailed visualization of the various anatomical structures decisive for interventions and surgical plans.

OBJECTIVES: The objective of this paper is to design and apply an enhanced brain tumor MRI segmentation using K-mean with K-means as machine learning based Particle Swarm Optimization (PSO) and Firefly Algorithm (FA).

METHODS: A novel fitness function of Swarm Based PSO works on velocity variation is introduced, which enhances the segmented regions. The traditional k-means algorithm is enhanced by applying PSO to the segmented part. Another extension of Swarm Intelligence named Firefly is applied to compare the results of the PSO based segmentation, and Firefly based segmentation is used.

RESULTS: The simulation results are evaluated in terms of precision (98%), recall (0.95), f-measure (0.96), accuracy (97%), and segmentation time (2.63s) to measure the image segmentation the quality of main results obtained.

CONCLUSION: Comparative studies have shown that the proposed design using k-means combined with FA exhibited high accuracy and precision in detecting brain tumor RoI.