International Journal of Engineering and Manufacturing(IJEM)
ISSN: 2305-3631 (Print), ISSN: 2306-5982 (Online)
Published By: MECS Press
IJEM Vol.13, No.2, Apr. 2023
Interpolation Method for Identification of Brain Tumor from Magnetic Resonance Images
Full Text (PDF, 762KB), PP.40-51
During the past years, it is observed from the literature that, identification of the brain tumor identification in human being is gaining popularity. Diagnosing any disease without manual interaction with great accuracy makes computer science research more demanding, therefore, the present work is related to identify the tumor clots in the affected patients. For this purpose, a well-known Safdarganj Hospital, New Delhi, India is consulted and 2165 Magnetic Resonance Images (MRI) of a single patient are collected through scanning, and interpolation technique of numerical method used to identify the accurate position of the brain tumor. A system model is developed and implemented by the use of Python programming language and MATLAB for the identification of affected areas in the form of a contour of a patient. The desired accuracy and specificity are evaluated using the computed results and also presented in the form of graphs.
Cite This Paper
Sugandha Singh, Vipin Saxena, "Interpolation Method for Identification of Brain Tumor from Magnetic Resonance Images", International Journal of Engineering and Manufacturing (IJEM), Vol.13, No.2, pp. 40-51, 2023. DOI:10.5815/ijem.2023.02.05
Anaraki, A K. (2018): Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms, Biocybernetics and Biomedical Engineering, 39, 63-74, https://doi.org/10.1016/j.bbe.2018.10.004.
Bahadure, N. B. (2017); Comparative Approach of MRI-Based Brain Tumor Segmentation and Classification Using Genetic Algorithm, International Journal of Biomedical Imaging, 477-489, DOI: 10.1007/s10278-018-0050-6.
Burje, S. (2021): Modified Image Segmentation Schemes for Detection & Identification of MRI Brain Tumor Infection, Turkish Journal of Computer and Mathematics Education, 12, 6604-6612, https://doi.org/10.17762/turcomat.v12i10.5518.
Ding, Y. (2021): ToStaGAN: An end-to-end two-stage generative adversarial network for brain tumor segmentation, Neurocomputing, (2021), 462, 141-153, https://doi.org/10.1016/j.neucom.2021.07.066.
Chen, H. (2021): A Hybrid Feature Selection based Brain Tumor Detection and Segmentation in Multiparametric Magnetic Resonance Imaging, Medical Physics, 48, 7360-7371, https://doi.org/10.1002/mp.15026.
Devkotaa, B. (2018): Image Segmentation for Early-Stage Brain Tumor Detection using Mathematical Morphological Reconstruction, Procedia Computer Science, 125, 115-123, https://doi.org/10.1016/j.procs.2017.12.017.
David, A. (2021): Automated multiclass tissue segmentation of clinical brain MRIs with lesions, NeuroImage: Clinical, DOI: 10.1016/j.nicl.2021.102769.
Deb, D.; Roy S. (2021): Brain tumor detection based on hybrid deep neural network in MRI by adaptive squirrel search optimization, Multimedia Tools and Applications, 80, 2621–2645, https://doi.org/10.1007/s11042-020-09810-9.
El-Hag, N. A. (2021): Utilization of image interpolation and fusion in brain tumor segmentation, International Journal for Numerical methods in Biomedical engineering, 37, https://doi.org/10.1002/cnm.3449.
Gunasekara, S. R. (2021): A Systematic Approach for MRI Brain Tumor Localization and Segmentation Using Deep Learning and Active Contouring, Hindawi Journal of Healthcare Engineering, https://doi.org/10.48550/arXiv.2102.03532.
Hasan, A. M. (2016): Segmentation of Brain Tumors in MRI Images Using Three-Dimensional Active Contour without Edge, Symmetry, https://doi.org/10.3390/sym8110132.
Ivana (2015): MRI Segmentation of the Human Brain: Challenges, Methods, and Applications, Hindawi Publishing Corporation Computational and Mathematical Methods in Medicine, https://doi.org/10.1155/2015/450341.
Islam, M. K. (2021): Brain tumor detection in MR image using superpixels, principal component analysis and template-based K-means clustering algorithm, Machine Learning with Applications, 5, https://doi.org/10.1016/j.mlwa.2021.100044.
Wadhwa, A. (2019): A review on brain tumor segmentation of MRI images, Magnetic Resonance Imaging, 247-259, doi: 10.1016/j.mri.2019.05.043.
Yuvaraj, D. (2021): Multi-perspective scaling convolutional neural networks for high-resolution MRI brain image segmentation, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2020.12.199.
Sasikala, E. (2021): Identification of lesion using an efficient hybrid algorithm for MRI brain image segmentation, Journal of Ambient Intelligence and Humanized Computing, DOI:10.1007/s12652-021-03060-9.
Khosravanian, A. (2021a): A level set method based on domain transformation and bias correction for MRI brain tumor segmentation, Journal of Neuroscience Methods, 352, 0165-0270, https://doi.org/10.1016/j.jneumeth.2021.109091.
Khosravanian, A. (2021b): Fast level set method for glioma brain tumor segmentation based on Superpixel fuzzy clustering and lattice Boltzmann method, Computer Methods and Programs in Biomedicine, 198, 0169-2607, https://doi.org/10.1016/j.cmpb.2020.105809.
Kumar, D. M. (2021): MRI brain tumor detection using optimal possibilistic fuzzy C-means clustering algorithm and adaptive k-nearest neighbor classifier, Journal of Ambient Intelligence and Humanized Computing, 2867-2880, DOI:10.1007/s12652-020-02444-7.
Krishnakumar, S.; Manivannan, K. (2021): Effective segmentation and classification of brain tumor using rough K means algorithm and multi kernel SVM in MR images, Journal of Ambient Intelligence and Humanized Computing, 6751–6760, DOI:10.1007/s12652-020-02300-8.
Khalila, M. (2018): Performance evaluation of feature extraction techniques in MR-Brain image classification system, Procedia Computer Science, 127, 218–225, https://doi.org/10.1016/j.procs.2018.01.117.
Lin, F. (2021): Path aggregation U-Net model for brain tumor segmentation, Multimedia Tools and Applications, 80, 22951-22964, https://doi.org/10.1007/s11042-020-08795-9.
Maheswari, K. (2021): Hybrid clustering algorithm for an efficient Brain Tumor Segmentation, Materials Today: Proceedings, 37, 3002-3006, https://doi.org/10.1016/j.matpr.2020.08.718.
Mamatha, S. K.; Krishnappa, H. K. (2021): Detection of Brain Tumor in MR images using hybrid Fuzzy C-mean clustering with graph cut segmentation technique, Turkish Journal of Computer and Mathematics Education, 12, 4570-4577.
Nayak, D. R. (2016): Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forest, Neurocomputing, 177, 188-197, https://doi.org/10.1016/j.neucom.2015.11.034.
Punn, N. S.; Agarwal, S. (2021): multi-modality encoded fusion with 3D inception U-net and decoder model for brain tumor segmentation, Multimedia Tools, and Applications, 80, 30305–30320, https://doi.org/10.1007/s11042-020-09271-0.
Pauliah, M. (2007): Improved T1-weighted dynamic contrast-enhanced MRI to probe microvascularity and heterogeneity of human glioma, Magnetic Resonance Imaging, 25, 1292-1299, https://doi.org/10.1016/j.mri.2007.03.027.
Pereira, S. (2016): Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images, IEEE Transactions on medical imaging, 1240-251, DOI: 10.1109/TMI.2016.2538465.
Pooja, V. (2021): Comparative analysis of segmentation techniques on MRI brain tumor images, Materials Today: Proceedings, 47, 109-114, https://doi.org/10.1016/j.matpr.2021.03.723.
Shree, N. V.; Kumar, T. N. R. (2018): Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network, Brain Informatics, 23-30, DOI:10.1007/s40708-017-0075-5.
Saleem, H. (2021): Visual interpretability in 3D brain tumor segmentation network, Computers in Biology and Medicine, (2021), 133, https://doi.org/10.1016/j.compbiomed.2021.104410.
Sun, J. (2020), Segmentation of the multimodal brain tumor image used the multi-pathway architecture method based on 3D FCN, Neurocomputing, 423, 34-45,https://doi.org/10.1016/j.neucom.2020.10.031.
Zin, S.; Khaing, A. S. (2014): Brain Tumor Detection and Segmentation using Watershed Segmentation and Morphological Operation, International Journal of Research in Engineering and Technology, eISSN: 2319-1163 | pISSN: 2321-7308.
Viji, A. K. S.; Jayakumari J. (2012): Performance Evaluation of Standard Image Segmentation Methods and Clustering Algorithms for Segmentation of MRI Brain Tumor Images, European Journal of Scientific Research, 166-179.
Vaishnavee, K. B.; Amshakala K. (2015): An Automated MRI Brain Image Segmentation and Tumor Detection using SOM-Clustering and Proximal Support Vector Machine Classifier, IEEE International Conference on Engineering and Technology (ICETECH), DOI:10.1109/ICETECH.2015.7275030.
Wu, Z. (2021), MR-UNet Commodity Semantic Segmentation Based on Transfer Learning, IEEE Access, 9, 159447-159456, DOI: 10.1109/ACCESS.2021.3130578.
Dhar, Kishore K. (2022): Edge detection of image using image divergence and down sampling method, I. J. Engineering and Manufacturing, 3, 14-24, DOI: 10.5815/ijem.2022.03.02
Omolara, A. Ogungbe (2022): Design and Implementation of Diagnosis System for Cardiomegaly from Clinical Chest X-ray Reports, International Journal of Engineering and Manufacturing, 3, 25-37, DOI: 10.5815/ijem.2022.03.02.
Prashengit, Dhar; and Sunanda, Guha (2021): Skin Lesion Detection Using Fuzzy Approach and Classification with CNN, International Journal of Engineering and Manufacturing, 1, 11-18, DOI: 10.5815/ijem.2021.01.02.
Diwakar; and Deepa, Raj (2022); Recent Object Detection Techniques: A Survey, International Journal of Image, Graphics and Signal Processing, Vol. 14, 47-60, DOI: 10.5815/ijigsp.2022.02.05