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

Anjali Kapoor; Rekha Agarwal

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.

Keywords: Magnetic Resonance Imaging (MRI), K-means, Machine Learning, Particle Swarm Optimization (PSO), Firefly Algorithm (FA)

Received: 2020-04-08
Accepted: 2021-01-19
Published: 2021-02-03
Publisher: EAI

: http://dx.doi.org/10.4108/eai.3-2-2021.168600

Copyright © 2021 Anjali Kapoor et al., licensed to EAI. This is an open-access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.