I. J. of Electrical & Electronics Research
Research Article | 
Efficient Brain Tumour Segmentation Using Fuzzy Level Set Method and Intensity Normalization
Author(s): Dr. Balasubramanian Prabhu Kavin1, M. Divya2, N. Rithvi3, M. Vanmathi4 and M. Keerthana5
Published In : International Journal of Electrical and Electronics Research (IJEER) Volume 10, Issue 4
Publisher : FOREX Publication
Published : 18 October 2022
e-ISSN : 2347-470X
Page(s) : 801-805
Abstract
This paper is developed to implement a fuzzy set technique with intensity normalization intended for the identification of location and tumor shape from an MRI image. Normally, the tumor can be an uncontrolled growth of tissue in any portion of the body. Here, different kinds of cancers have various conditions with the treatments. Hence, brain tumor segmentation is an essential topic in medical applications. The fuzzy level set technique is utilized to segment the tumor from the brain MRI images. Additionally, intensity normalization is utilized to enhance image quality. The proposed technique is implemented in MATLAB and the exhibitions are evaluated by performance scores and implementation scales of quality ratings. To recognize the exhibition of the proposed technique, it is compared with the different and conventional strategies, for example, mobilenetv2, resnet18, resnet50, and xception separately.
Keywords: Brain tumor, Segmentation, Intensity modulation, Fuzzy level set method, Similarity measurements
Dr. Balasubramanian Prabhu Kavin*, Department of Data Science and Business Systems, College of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur - 603203, Chengalpattu Dist. Tamil Nadu, India; Email: ceaserkavin@gmail.com
M. Divya, Sri Ramachandra Faculty of Engineering and Technology, Sri Ramachandra Institute of Higher Education and Research, Chennai, India; Email: divyamanojaran1406@gmail.com
N. Rithvi, Sri Ramachandra Faculty of Engineering and Technology, Sri Ramachandra Institute of Higher Education and Research, Chennai, India; Email: rithvinatrajan4444@gmail.com
M. Vanmathi, Sri Ramachandra Faculty of Engineering and Technology, Sri Ramachandra Institute of Higher Education and Research, Chennai, India; Email: vanmathimanimaran@gmail.com
M. Keerthana, Sri Ramachandra Faculty of Engineering and Technology, Sri Ramachandra Institute of Higher Education and Research, Chennai, India; Email: keerthanamanimaran02@gmail.com
-
[1] Havaei, Mohammad, Axel Davy, David Warde-Farley, Antoine Biard, Aaron Courville, Yoshua Bengio, Chris Pal, Pierre-Marc Jodoin, and Hugo Larochelle. "Brain tumor segmentation with deep neural networks." Medical image analysis 35 (2017): 18-31. [Cross Ref]
-
[2] Ali, Mahnoor, Syed Omer Gilani, Asim Waris, Kashan Zafar, and Mohsin Jamil. "Brain Tumour Image Segmentation Using Deep Networks." IEEE Access 8 (2020): 153589-153598.[Cross Ref]
-
[3] Pinto, Adriano, Sérgio Pereira, Deolinda Rasteiro, and Carlos A. Silva. "Hierarchical brain tumour segmentation using extremely randomized trees." Pattern Recognition 82 (2018): 105-117.[Cross Ref]
-
[4] Khan, Amjad Rehman, Siraj Khan, Majid Harouni, Rashid Abbasi, Sajid Iqbal, and Zahid Mehmood. "Brain tumor segmentation using K‐means clustering and deep learning with synthetic data augmentation for classification." Microscopy Research and Technique 84, no. 7 (2021): 1389-1399.[Cross Ref]
-
[5] Chen, Hao, Zhiguang Qin, Yi Ding, Lan Tian, and Zhen Qin. "Brain tumor segmentation with deep convolutional symmetric neural network." Neurocomputing 392 (2020): 305-313.[Cross Ref]
-
[6] Soltaninejad, Mohammadreza, Guang Yang, Tryphon Lambrou, Nigel Allinson, Timothy L. Jones, Thomas R. Barrick, Franklyn A. Howe, and Xujiong Ye. "Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels." Computer methods and programs in biomedicine 157 (2018): 69-84.[Cross Ref]
-
[7] Khan, Amjad Rehman, Siraj Khan, Majid Harouni, Rashid Abbasi, Sajid Iqbal, and Zahid Mehmood. "Brain tumor segmentation using K‐means clustering and deep learning with synthetic data augmentation for classification." Microscopy Research and Technique 84, no. 7 (2021): 1389-1399.[Cross Ref]
-
[8] Khosravanian, Asieh, Mohammad Rahmanimanesh, Parviz Keshavarzi, and Saeed Mozaffari. "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 (2021): 105809.[Cross Ref]
-
[9] Ramya, P., M. S. Thanabal, and C. Dharmaraja. "Brain tumor segmentation using cluster ensemble and deep super learner for classification of MRI." Journal of Ambient Intelligence and Humanized Computing 12, no. 10 (2021): 9939-9952.[Cross Ref]
-
[10] Chahal, Prabhjot Kaur, and Shreelekha Pandey. "A hybrid weighted fuzzy approach for brain tumor segmentation using MR images." Neural Computing and Applications (2021): 1-15.[Cross Ref]
-
[11] Sachdeva, Jainy, Vinod Kumar, Indra Gupta, Niranjan Khandelwal, and Chirag Kamal Ahuja. "Segmentation, feature extraction, and multiclass brain tumor classification." Journal of digital imaging 26, no. 6 (2013): 1141-1150.[Cross Ref]
-
[12] Khosravanian, Asieh, Mohammad Rahmanimanesh, Parviz Keshavarzi, and Saeed Mozaffari. "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 (2021): 105809.[Cross Ref]
-
[13] https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection[Cross Ref]
-
[14] Rehman, Zaka Ur, Syed S. Naqvi, Tariq M. Khan, Muhammad A. Khan, and Tariq Bashir. "Fully automated multi-parametric brain tumour segmentation using superpixel based classification." Expert systems with applications 118 (2019): 598-613.[Cross Ref]
-
[15] Sriramakrishnan, Padmanaban, Thiruvenkadam Kalaiselvi, and Rangasami Rajeswaran. "Modified local ternary patterns technique for brain tumour segmentation and volume estimation from MRI multi-sequence scans with GPU CUDA machine." Biocybernetics and Biomedical Engineering 39, no. 2 (2019): 470-487.[Cross Ref]
-
[16] Ali, Mahnoor, Syed Omer Gilani, Asim Waris, Kashan Zafar, and Mohsin Jamil. "Brain tumour image segmentation using deep networks." IEEE Access 8 (2020): 153589-153598.[Cross Ref]
-
[17] Avadhesh Kumar Dixit, Rakesh Kumar Yadav and Ramapati Mishra (2021), Contrast Enhancement of Colour Images by Optimized Fuzzy Intensification. IJEER 9(4), 143-149. DOI: 10.37391/IJEER.090408. [Cross Ref]
-
[18] Balaji, V. N., Srinivas, P. B., & Singh, M. K. (2021). Neuromorphic advancements architecture design and its implementations technique. Materials Today: Proceedings.[Cross Ref]
-
[19] V. Sanjay and P. Swarnalatha (2022), A Survey on Various Machine Learning Techniques for an Efficient Brain Tumor Detection from MRI Images. IJEER 10(2), 177-182. DOI: 10.37391/IJEER.100222.[Cross Ref]
Dr. Balasubramanian Prabhu Kavin, M. Divya, N. Rithvi, M. Vanmathi and M. Keerthana (2022), Efficient Brain Tumour Segmentation Using Fuzzy Level Set Method and Intensity Normalization. IJEER 10(4), 801-805. DOI: 10.37391/IJEER.100406.