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

References

Sheela CJJ, Suganthi G. Brain tumor segmentation with radius contraction and expansion based initial contour detection for active contour model. Multimedia Tools and Applications. 2020;79(33-34):23793–23819. Available from: https://dx.doi.org/10.1007/s11042-020-09006-1
Veeramalla SK, Hindumathi V, Reddy TV, Pattan AB, Nandan TPK. Segmentation of MRI Images Using A Combination of Active Contour Modelling and Morphological Processing. Journal of Mechanics in Medicine and Biology. 2023;23(04):1–24. Available from: https://dx.doi.org/10.1142/s021951942340002x
Shekari M, Rostamian M. Brain tumor segmentation from MRI using FCM clustering, morphological reconstruction, and active contour. Multimedia Tools and Applications. 2024;83(14):42973–42998. Available from: https://dx.doi.org/10.1007/s11042-023-17233-5
Shahvaran Z, Kazemi K, Fouladivanda M, Helfroush MS, Godefroy O, Aarabi A. Morphological active contour model for automatic brain tumor extraction from multimodal magnetic resonance images. Journal of Neuroscience Methods. 2021;362. Available from: https://dx.doi.org/10.1016/j.jneumeth.2021.109296
Soleymanifard M, Hamghalam M. Segmentation of Whole Tumor Using Localized Active Contour and Trained Neural Network in Boundaries. In: 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI). IEEE. 2019.
Kaifi R. A Review of Recent Advances in Brain Tumor Diagnosis Based on AI-Based Classification. Diagnostics. 2023;13(18):1–32. Available from: https://dx.doi.org/10.3390/diagnostics13183007
Abbasi S, Tajeripour F. Detection of brain tumor in 3D MRI images using local binary patterns and histogram orientation gradient. Neurocomputing. 2017;219:526–535. Available from: https://doi.org/10.1016/j.neucom.2016.09.051
Pereira S, Pinto A, Alves V, Silva CA. Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images. IEEE Transactions on Medical Imaging. 2016;35(5):1240–1251. Available from: https://dx.doi.org/10.1109/tmi.2016.2538465
Rad AE, Rahim MSM, Kolivand H, Amin IBM. Morphological region-based initial contour algorithm for level set methods in image segmentation. Multimedia Tools and Applications. 2017;76(2):2185–2201. Available from: https://dx.doi.org/10.1007/s11042-015-3196-y
Kazerooni AF, Ahmadian A, Serej ND, Rad HS, Saberi H, Yousefi H, et al. Segmentation of brain tumors in MRI images using multi-scale gradient vector flow. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. (pp. 7973-7976) IEEE. 2011.
Wadhwa A, Bhardwaj A, Verma VS. A review on brain tumor segmentation of MRI images. Magnetic Resonance Imaging. 2019;61:247–259. Available from: https://dx.doi.org/10.1016/j.mri.2019.05.043
Ullah N, Khan JA, Khan MS, Khan W, Hassan I, Obayya M, et al. An Effective Approach to Detect and Identify Brain Tumors Using Transfer Learning. Applied Sciences. 2022;12(11):1–17. Available from: https://dx.doi.org/10.3390/app12115645
Mane V, Chivate A, Ambekar P, Chavan A, AP. Brain Tumor Detection using Convolutional Neural Network. In: Proceedings of International Joint Conference on Advances in Computational Intelligence, Algorithms for Intelligent Systems ((AIS)). (Vol. 2019, pp. 449-461) Singapore. Springer. 2024.
Abd-Ellah MK, Awad AI, Khalaf AAM, Hamed HFA. A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned. Magnetic Resonance Imaging. 2019;61:300–318. Available from: https://dx.doi.org/10.1016/j.mri.2019.05.028
Rezazadeh R. The Concept of Centralization and Decentralization : An Analysis and Evaluation. International Review of Administrative Sciences. 1961;27(4):425–430. Available from: https://dx.doi.org/10.1177/002085236102700407
Kulshreshtha A, Nagpal A. IFAS: improved fully automatic segmentation convolutional neural network model along with morphological segmentation for brain tumor detection. International Journal of Information Technology. 2024;16(3):1517–1525. Available from: https://dx.doi.org/10.1007/s41870-023-01572-5
Ayomide KS, Aris TNM, Zolkepli M. Improving Brain Tumor Segmentation in MRI Images through Enhanced Convolutional Neural Networks. International Journal of Advanced Computer Science and Applications. 2023;14(4):670–678. Available from: https://dx.doi.org/10.14569/ijacsa.2023.0140473
Ilunga–Mbuyamba E, Avina–Cervantes JG, Cepeda–Negrete J, Ibarra–Manzano MA, Chalopin C. Automatic selection of localized region-based active contour models using image content analysis applied to brain tumor segmentation. Computers in Biology and Medicine. 2017;91:69–79. Available from: https://dx.doi.org/10.1016/j.compbiomed.2017.10.003
Sun K, Qin L, Zeng S, Li Y. Automatic Smoothing Parameter Adjustment of Region-Based Active Contour Using Edge Information. In: 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). IEEE. 2016.
Al-Fahdawi S, Al-Waisy AS, Zeebaree DQ, Qahwaji R, Natiq H, Mohammed MA, et al. Fundus-DeepNet: Multi-label deep learning classification system for enhanced detection of multiple ocular diseases through data fusion of fundus images. Information Fusion. 2024;102:1–11. Available from: https://dx.doi.org/10.1016/j.inffus.2023.102059
Ye X, Ji B, Chen X, Qian D, Zhao Z. Probability Boltzmann Machine Network for Face Detection on Video. In: 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). (pp. 138-147) IEEE. 2020.
Pramanandaperumal T, Srivaishnavi KR, Rani DL, Murthy KPN. Boltzmann and Non-Boltzmann sampling for image processing. Indian Journal of Economics and Development. 2017;5(9):1–8. Available from: https://www.i-scholar.in/index.php/ijed/article/view/176178
Pai PF, Hong WC. Support vector machines with simulated annealing algorithms in electricity load forecasting. Energy Conversion and Management. 2005;46(17):2669–2688. Available from: https://dx.doi.org/10.1016/j.enconman.2005.02.004
Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E. Equation of State Calculations by Fast Computing Machines. The Journal of Chemical Physics. 1953;21(6):1087–1092. Available from: https://dx.doi.org/10.1063/1.1699114
Patil RC, Bhalchandra AS. Brain tumor extraction from MRI images using MATLAB. International Journal of Electronics, Communication and Soft Computing Science & Engineering (IJECSCSE). 2012;1(4):1–4. Available from: http://www.hep.upatras.gr/class/download/psi_epe_iko/5-Brain-Tumour-Extraction-from-MRI-Images-Using-MATLAB.pdf
Tomar N. Brain Tumor Segmentation. kaggle. Available from: https://www.kaggle.com/datasets/nikhilroxtomar/brain-tumor-segmentation/data
Nickparvar M. Brain Tumor MRI Dataset. Kaggle. Available from: https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset
Kumar A, Sodhi SS. Comparative Analysis of Gaussian Filter, Median Filter and Denoise Autoenocoder. In: 2020 7th International Conference on Computing for Sustainable Global Development (INDIACom). (Vol. 2020, pp. 45-51) IEEE. 2020.
Tenghongsakul K, Kanjanasurat I, Archevapanich T, Purahong B, Lasakul A. Deep transfer learning for brain tumor detection based on MRI images. In: 2022 The 4th International Conference on Electronics Communication Technologies (ICECT 2022) , Journal of Physics: Conference Series. (Vol. 2497, pp. 1-9) IOP Publishing. 2023.
Pandey SK, Bhandari AK. Morphological active contour based SVM model for lung cancer image segmentation. Multimedia Tools and Applications. 2024. Available from: https://dx.doi.org/10.1007/s11042-023-18092-w
Gunn SR, Nixon MS. A robust snake implementation; a dual active contour. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1997;19(1):63–68. Available from: https://dx.doi.org/10.1109/34.566812
Das B, Banerjee S. Parametric Contour Model In Medical Image Segmentation. In: Deformable Models, Topics in Biomedical Engineering. International Book Series ((ITBE)). (pp. 31-74) New York, NY, USA. Springer. 2007.
Shenbagarajan A, Ramalingam V, Balasubramanian C, Palanivel S. Tumor Diagnosis in MRI Brain Image using ACM Segmentation and ANN-LM Classification Techniques. Indian Journal of Science and Technology. 2016;9(1):1–12. Available from: https://dx.doi.org/10.17485/ijst/2016/v9i1/78766
Singh S, Saxena V. Classification and Segmentation of MRI Images of Brain Tumors Using Deep Learning and Hybrid Approach. International journal of electrical and computer engineering systems. 2024;15(2):163–172. Available from: https://dx.doi.org/10.32985/ijeces.15.2.5
Saxena V, Singh S, Singh KV. MRI Brain Tumor Prediction using Azure Streamlit Framework and Analysis of CNN Activation Functions. Indian Journal Of Science And Technology. 2023;16(37):3129–3138. Available from: https://dx.doi.org/10.17485/ijst/v16i37.427

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© 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)