Brain Tumor Prediction and Segmentation with Morphological Region-based Active Contour Model and Refinement using Boltzmann Monte Carlo Method in MRI Images

K R Srivaishnavi; T Pramananda Perumal; P Anishiya

doi:10.17485/IJST/v17i20.1231

Article

Brain Tumor Prediction and Segmentation with Morphological Region-based Active Contour Model and Refinement using Boltzmann Monte Carlo Method in MRI Images

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v17i20.1231

Year: 2024, Volume: 17, Issue: 20, Pages: 2088-2100

Original Article

Brain Tumor Prediction and Segmentation with Morphological Region-based Active Contour Model and Refinement using Boltzmann Monte Carlo Method in MRI Images

K R Srivaishnavi^1*, T Pramananda Perumal¹, P Anishiya²

¹Presidency College, Chennai, 600 005, Tamil Nadu, India
²Anna University, CEG Campus, Chennai, 600 025, Tamil Nadu, India

*Corresponding Author
Email: [email protected]

Received Date:15 April 2024, Accepted Date:15 May 2024, Published Date:18 May 2024

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: The primary goal of the research work is to accurately detect the precise location of the brain tumor in the radiological Magnetic Resonance Imaging (MRI) images of human brain using segmentation method. Methods: In this research work, we introduce mainly the Morphological Region-based Active Contour model and Boltzmann Monte Carlo method (MACB model), involving a comprehensive three-step methodology for the segmentation of the brain, MRI images in order to detect brain tumor. The initial step involves pre-processing which includes Gaussian filtering for noise reduction and Contrast Limited Adaptive Histogram Equalization (CLAHE) technique to enhance image features. In the second step, we identify tumor-related clusters using morphological operations and delineate the tumor regions using Active Contour (Snake) model to get a segmented image. In the final step, the Boltzmann Monte Carlo method is used to refine the edges of the segmented image. To evaluate the effectiveness of this approach, the 2D brain tumor datasets, available in the public domain, are used. The first dataset is taken from Kaggle website and has 3064 MRI human brain images and its respective ground truth images which is used for segmentation. The second dataset is used for visualization of segmented tumor, available in the same Kaggle website. Findings: The Performance metrics for finding similarity between the segmented images generated using the proposed MACB model and the ground truth images, available in the first dataset, exhibit higher values. That is, the proposed method has achieved higher values of Dice Similarity Coefficient (DSC): 93.26%, Jaccard Co-efficient: 86.44%, Sensitivity: 97.27%, Specificity: 99.43% and Pixel accuracy: 98.95%. Novelty: In this research work, MACB model is proposed for the detection, segmentation, and refinement process of brain tumor by incorporating Boltzmann Monte Carlo method with Morphological Region-Based Active Contour model. This novel approach has resulted in enhanced precision and efficiency in the brain tumor segmentation process.

Keywords: Brain Tumor Segmentation, Morphological Operation, Active Contour, Boltzmann Monte Carlo Method, Magnetic Resonance Imaging

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Copyright

© 2024 Srivaishnavi et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)