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

• Published in: EAI endorsed transactions on pervasive health and technology Vol. 7; no. 26; p. e2
• Main Authors: Kapoor, Anjali, Agarwal, Rekha
• Format: Journal Article
• Language: English
• Published: European Alliance for Innovation (EAI) 01.04.2021
• Subjects: firefly algorithm (fa); k-means; machine learning; magnetic resonance imaging (mri); particle swarm optimization (pso)
• ISSN: 2411-7145; 2411-7145
• DOI: 10.4108/eai.3-2-2021.168600

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.