Automated segmentation of brain tumors from MRI images can be essential for behavior planning, monitoring in addition to a diagnosis of a different kind of disease. Hence, in this paper Squirrel Search Algorithm-based Fuzzy Level Set Method (SSA-FLS) is designed aimed at brain tumor segmentation. The proposed segmentation process is designed with the combination of the Fuzzy Level Set method (FLS) and Squirrel Search Algorithm (SSA). In the fuzzy level set method, the efficient cluster center is chosen with the assistance of the Squirrel Search Algorithm. Initially, the fuzzy level set method objective function is considered by tumor portion extraction from the MRI images. After that, SSA is utilized to optimize the cluster center and fuzzifier from the clustering method. The projected technique is applied in MATLAB and performances have been assessed. The projected technique is authenticated by performance metrics like Dice similarity coefficient (DSC), Jaccard Similarity Index (JSI), accuracy, sensitivity, and specificity. The projected technique is contrasted with the conventional techniques like Seagull Optimization Algorithm Based Super Pixel Fuzzy Clustering (SOA-SFC) and Chimp Optimization Algorithm Based Type-2 Intuitionistic Fuzzy C-Means Clustering (COA-T2FCM).
Keywords:
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brain tumor segmentation, squirrel search algorithm, fuzzy level set method, cluster center, and medical image segmentation.
References
References:
[1]. A.Selvapandian, and K. Manivannan. "Fusion based glioma brain tumor detection and segmentation using ANFIS classification." Computer methods and programs in biomedicine 166 (2018): 33-38.
[2]. Jijun Tong, Yingjie Zhao, Peng Zhang, Lingyu Chen, and Lurong Jiang. "MRI brain tumor segmentation based on texture features and kernel sparse coding." Biomedical Signal Processing and Control 47 (2019): 387-392.
[3]. EserSert, FatihÖzyurt, and Akif Doğantekin. "A new approach for brain tumor diagnosis system: Single image super resolution based maximum fuzzy entropy segmentation and convolutional neural network." Medical hypotheses 133 (2019): 109413.
[4]. Geethu Mohan, and M. Monica Subashini. "MRI based medical image analysis: Survey on brain tumor grade classification." Biomedical Signal Processing and Control 39 (2018): 139-161.
[5]. Shengcong Chen, Changxing Ding, and Minfeng Liu. "Dual-force convolutional neural networks for accurate brain tumor segmentation." Pattern Recognition 88 (2019): 90-100.
[6]. FatihÖzyurt, EserSert, EnginAvci, and EsinDogantekin. "Brain tumor detection based on Convolutional Neural Network with neutrosophic expert maximum fuzzy sure entropy." Measurement 147 (2019): 106830.
[7]. Mohamed Akil, RachidaSaouli, and Rostom Kachouri. "Fully automatic brain tumor segmentation with deep learning-based selective attention using overlapping patches and multi-class weighted cross-entropy." Medical image analysis 63 (2020): 101692.
[8]. Abdelaziz Essadike, ElhoussaineOuabida, and AbdenbiBouzid. "Brain tumor segmentation with Vander Lugt correlator based active contour." Computer methods and programs in biomedicine 160 (2018): 103-117.
[9]. Khai Yin Lim, and Rajeswari Mandava. "A multi-phase semi-automatic approach for multisequence brain tumor image segmentation." Expert Systems with Applications 112 (2018): 288-300.
[10]. Ilunga–Mbuyamba, Elisee, Juan Gabriel Avina–Jonathan Cepeda–Negrete Cervantes, Mario Alberto Ibarra–Manzano, and Claire Chalopin. "Automatic selection of localized region-based active contour models using image content analysis applied to brain tumor segmentation." Computers in biology and medicine 91 (2017): 69-79.
[11]. MohammadrezaSoltaninejad, Guang Yang, TryphonLambrou, 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.
[12]. Adel Kermi, Khaled Andjouh, and Ferhat Zidane. "Fully automated brain tumour segmentation system in 3D-MRI using symmetry analysis of brain and level sets." IET Image Processing 12, no. 11 (2018): 1964-1971.
[13]. Marco Domenico Cirillo, David Abramian, and Anders Eklund. "Vox2Vox: 3D-GAN for brain tumour segmentation." arXiv preprint arXiv:2003.13653 (2020).
[14]. Mahnoor Ali, Syed Omer Gilani, AsimWaris, Kashan Zafar, and Mohsin Jamil. "Brain Tumour Image Segmentation Using Deep Networks." IEEE Access 8 (2020): 153589-153598.
[15]. Abhisha Mano, and Swaminathan Anand. "Method of multi-region tumour segmentation in brain MRI images using grid-based segmentation and weighted bee swarm optimisation." IET Image Processing 14, no. 12 (2020): 2901-2910.
[16]. Rehman, Zaka Ur, Syed S. Naqvi, Tariq M. Khan, Khan Muhammad A, and Tariq Bashir. "Fully automated multi-parametric brain tumour segmentation using superpixel based classification." Expert systems with applications 118 (2019): 598-613.
[17]. R.Radha, and R. Gopalakrishnan. "A medical analytical system using intelligent fuzzy level set brain image segmentation based on improved quantum particle swarm optimization." Microprocessors and Microsystems 79 (2020): 103283.
[18]. Bing Nan Li, Chee Kong Chui, Stephen Chang, and Sim Heng Ong. "Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation." Computers in biology and medicine 41, no. 1 (2011): 1-10.
[19]. Mohit Jain, Vijander Singh, and Asha Rani. "A novel nature-inspired algorithm for optimization: Squirrel search algorithm." Swarm and evolutionary computation 44 (2019): 148-175.
[20]. Tongyi Zheng, and Weili Luo. "An improved squirrel search algorithm for optimization." Complexity 2019 (2019).
[21]. https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection
