Tehran University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share By
Using Marker-Controlled Watershed Transform to Detect Baker's Cyst in Magnetic Resonance Imaging Images: A Pilot Study Publisher



Ghaderi S1 ; Ghaderi K2 ; Ghaznavi H3
Authors

Source: Journal of Medical Signals and Sensors Published:2022


Abstract

Nowadays, magnetic resonance imaging (MRI) has a high ability to distinguish between soft tissues because of high spatial resolution. Image processing is extensively used to extract clinical data from imaging modalities. In the medical image processing field, the knee's cyst (especially Baker) segmentation is one of the novel research areas. There are different methods for image segmentation. In this paper, the mathematical operation of the watershed algorithm is utilized by MATLAB software based on marker-controlled watershed segmentation for the detection of Baker's cyst in the knee's joint MRI sagittal and axial T2-weighted images. The performance of this algorithm was investigated, and the results showed that in a short time Baker's cyst can be clearly extracted from original images in axial and sagittal planes. The marker-controlled watershed segmentation was able to detect Baker's cyst reliable and can save time and current cost, especially in the absence of specialists it can help us for the easier diagnosis of MRI pathologies. © 2021 Journal of Medical Signals & Sensors Published by Wolters Kluwer-Medknow.
Related Docs
View other Related Docs
1. Segmentation of Gbm in Mri Images Using an Efficient Speed Function Based on Level Set Method, Proceedings - 2017 10th International Congress on Image and Signal Processing# BioMedical Engineering and Informatics# CISP-BMEI 2017 (2017)
2. An Automatic Level Set Method for Hippocampus Segmentation in Mr Images, Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization (2020)
3. Mr-Guided Attenuation Map for Prostate Pet-Mri: An Intensity and Morphologic-Based Segmentation Approach for Generating a Five-Class Attenuation Map in Pelvic Region, Annals of Nuclear Medicine (2017)
Experts (# of related papers)
View all Related Experts
Hamidreza Saligheh Rad (3)
Mohammad Reza Ay (2)
Other Related Docs
4. Segmentation of Effective Cells in Multiple Myeloma Cancer Using Deformable Models and K-Means Clustering, Journal of Isfahan Medical School (2017)
5. Brain Tumor Segmentation Using Hierarchical Combination of Fuzzy Logic and Cellular Automata, Journal of Medical Signals and Sensors (2022)
6. Systematic Bone Infection Detection in Axial Diabetic Foot Mri, 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology# CIBCB 2015 (2015)
7. Mri Segmentation Using Fuzzy C-Means and Radial Basis Function Neural Networks, Current Science (2018)
8. Single Ste-Mr Acquisition in Mr-Based Attenuation Correction of Brain Pet Imaging Employing a Fully Automated and Reproducible Level-Set Segmentation Approach, Molecular Imaging and Biology (2017)
9. 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)
10. Segmentation of White Blood Cells From Microscopic Images Using a Novel Combination of K-Means Clustering and Modified Watershed Algorithm, Journal of Medical Signals and Sensors (2017)