Tehran University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share By
Segmentation of Gbm in Mri Images Using an Efficient Speed Function Based on Level Set Method Publisher



Mojtabavi A1 ; Farnia P1 ; Ahmadian A1 ; Alimohamadi M2 ; Pourrashidi A2 ; Rad HS1 ; Alirezaie J3
Authors

Source: Proceedings - 2017 10th International Congress on Image and Signal Processing# BioMedical Engineering and Informatics# CISP-BMEI 2017 Published:2017


Abstract

Accurate segmentation and characterization of abnormalities in brain tumor are challenging task, especially in the case of GBM tumors, where the ambiguities presented in the boundaries of these tumors necessitates using efficient segmentation method. Level set methods have proven to be a flexible and powerful tool for image segmentation because of being shape-driven method with a properly defined speed function to grow or shrink the boundaries to segment complex objects of interest, precisely. In this study a combined level set algorithm consists of both region and boundary terms for GBM segmentation is proposed. The modified speed function incorporates threshold based level set and the Laplacian filter to highlight the fine details for performing an accurate extraction of the tumor region using multiple seed points selected by the user. An evaluation was performed on a dataset containing 6 patients with GBM by using three measures Dice, false positive error (FPE) and false negative error (FNE). Manual segmentation of GBM is considered as gold standard. Compared to traditional method, the mean of FPE and FNE are improved by 53.5% and 53.1%, respectively. The mean of Dice coefficients between our results and gold standard measurement reached to 0.88. As the results proved, the proposed combined method improves the accuracy of GBM segmentation by 16% compared to conventional level set method with threshold based speed function. Our method is also robust to change of parameters. © 2017 IEEE.
Related Docs
View other Related Docs
1. Brain Tumor Segmentation Using Hierarchical Combination of Fuzzy Logic and Cellular Automata, Journal of Medical Signals and Sensors (2022)
2. Automated Segmentation of Meningioma From Contrast-Enhanced T1-Weighted Mri Images in a Case Series Using a Marker-Controlled Watershed Segmentation and Fuzzy C-Means Clustering Machine Learning Algorithm, International Journal of Surgery Case Reports (2023)
3. An Automatic Level Set Method for Hippocampus Segmentation in Mr Images, Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization (2020)
Experts (# of related papers)
View all Related Experts
Mohammad Ali Oghabian (2)
Alireza Ahmadian (2)
Other Related Docs
4. A Novel in Silico Platform for a Fully Automatic Personalized Brain Tumor Growth, Magnetic Resonance Imaging (2020)
5. Current Trends in Glioma Tumor Segmentation: A Survey of Deep Learning Modules, Physica Medica (2025)
6. Multi-Parametric (Adc/Pwi/T2-W) Image Fusion Approach for Accurate Semi-Automatic Segmentation of Tumorous Regions in Glioblastoma Multiforme, Magnetic Resonance Materials in Physics# Biology and Medicine (2015)
7. Enhancing Cancer Zone Diagnosis in Mri Images: A Novel Som Neural Network Approach With Block Processing in the Presence of Noise, Iranian Journal of Blood and Cancer (2025)
8. A Memory-Efficient Deep Framework for Multi-Modal Mri-Based Brain Tumor Segmentation, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society# EMBS (2022)
9. Accurate Automatic Glioma Segmentation in Brain Mri Images Based on Capsnet, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society# EMBS (2021)
10. Brain Tumor Segmentation Using Multimodal Mri and Convolutional Neural Network, 2022 30th International Conference on Electrical Engineering# ICEE 2022 (2022)
11. A Neonatal Bimodal Mr-Ct Head Template, PLoS ONE (2017)
12. The Effect of Harmonization on the Variability of Pet Radiomic Features Extracted Using Various Segmentation Methods, Annals of Nuclear Medicine (2024)
13. An Improved Capsule Network for Glioma Segmentation on Mri Images: A Curriculum Learning Approach, Computers in Biology and Medicine (2022)
14. Comparison of Wavelet Transformations to Enhance Convolutional Neural Network Performance in Brain Tumor Segmentation, BMC Medical Informatics and Decision Making (2021)
15. Segmentation of Effective Cells in Multiple Myeloma Cancer Using Deformable Models and K-Means Clustering, Journal of Isfahan Medical School (2017)
16. Findings of Dti-P Maps in Comparison With T 2 /T 2 -Flair to Assess Postoperative Hyper-Signal Abnormal Regions in Patients With Glioblastoma 08 Information and Computing Sciences 0801 Artificial Intelligence and Image Processing, Cancer Imaging (2018)
17. Fully Automated Clinical Target Volume Segmentation for Glioblastoma Radiotherapy Using a Deep Convolutional Neural Network, Polish Journal of Radiology (2023)
18. Fully Automatic 3D Segmentation of the Thoracolumbar Spinal Cord and the Vertebral Canal From T2-Weighted Mri Using K-Means Clustering Algorithm, Spinal Cord (2020)
19. Obtaining Thickness Maps of Corneal Layers Using the Optimal Algorithm for Intracorneal Layer Segmentation, International Journal of Biomedical Imaging (2016)
20. Barrett′S Mucosa Segmentation in Endoscopic Images Using a Hybrid Method: Spatial Fuzzy C-Mean and Level Set, Journal of Medical Signals and Sensors (2016)