ArticleBMC medical imaging2018
Automatic brain tissue segmentation based on graph filter.
Article in BMC medical imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Quality and efficiency of a semi-automatic anatomy-based method for tractography of associative white matter fascicles in brain tumor patients, a case series.Neurosurgical review · 2025Article
- Learning to detect boundary information for brain image segmentation.BMC bioinformatics · 2022Article
- Hyperconnected Openings Codified in a Max Tree Structure: An Application for Skull-Stripping in Brain MRI T1.Sensors (Basel, Switzerland) · 2022Article
- MhURI:A Supervised Segmentation Approach to Leverage Salient Brain Tissues in Magnetic Resonance Images.Computer methods and programs in biomedicine · 2021Article
- Segmentation of MRI brain scans using spatial constraints and 3D features.Medical & biological engineering & computing · 2020Article
- Semantic segmentation of cerebrospinal fluid and brain volume with a convolutional neural network in pediatric hydrocephalus-transfer learning from existing algorithms.Acta neurochirurgica · 2020Article
- An Improved Full Convolutional Network Combined with Conditional Random Fields for Brain MR Image Segmentation Algorithm and its 3D Visualization Analysis.Journal of medical systems · 2019Article
- Effect of exposure parameters of cone beam computed tomography on metal artifact reduction around the dental implants in various bone densities.BMC medical imaging · 2019Article
- Segmentation of Organs and Tumor within Brain Magnetic Resonance Images Using K-Nearest Neighbor Classification.Journal of medical physicsArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundAccurate segmentation of brain tissues from magnetic resonance imaging (MRI) is of significant importance in clinical applications and neuroscience research. Accurate segmentation is challenging due to the tissue heterogeneity, which is caused by noise, bias filed and partial volume effects.
methodsTo overcome this limitation, this paper presents a novel algorithm for brain tissue segmentation based on supervoxel and graph filter. Firstly, an effective supervoxel method is employed to generate effective supervoxels for the 3D MRI image. Secondly, the supervoxels are classified into different types of tissues based on filtering of graph signals.
resultsThe performance is evaluated on the BrainWeb 18 dataset and the Internet Brain Segmentation Repository (IBSR) 18 dataset. The proposed method achieves mean dice similarity coefficient (DSC) of 0.94, 0.92 and 0.90 for the segmentation of white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF) for BrainWeb 18 dataset, and mean DSC of 0.85, 0.87 and 0.57 for the segmentation of WM, GM and CSF for IBSR18 dataset.
conclusionsThe proposed approach can well discriminate different types of brain tissues from the brain MRI image, which has high potential to be applied for clinical applications.
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