ArticleScientific reports2019
Label fusion method combining pixel greyscale probability for brain MR segmentation.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of automated segmentation of 3D computed-tomography scans for volumetric body composition analysis.Journal of cachexia, sarcopenia and muscle · 2023Pooled it
- Automatic SNR measurement of brain MR images using a deep learning-based approach.Acta radiologica open · 2025Article
- Topology-preserving contourwise shape fusion.Scientific reports · 2025Article
- An Efficient Optimization Approach for Glioma Tumor Segmentation in Brain MRI.Journal of digital imaging · 2022Article
- Architectural Distortion-Based Digital Mammograms Classification Using Depth Wise Convolutional Neural Network.Biology · 2021Article
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Authors and funding
2 authors.
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Abstract
Multi-atlas-based segmentation (MAS) methods have demonstrated superior performance in the field of automatic image segmentation, and label fusion is an important part of MAS methods. In this paper, we propose a label fusion method that incorporates pixel greyscale probability information. The proposed method combines the advantages of label fusion methods based on sparse representation (SRLF) and weighted voting methods using patch similarity weights (PSWV) and introduces pixel greyscale probability information to improve the segmentation accuracy. We apply the proposed method to the segmentation of deep brain tissues in challenging 3D brain MR images from publicly available IBSR datasets, including images of the thalamus, hippocampus, caudate, putamen, pallidum and amygdala. The experimental results show that the proposed method has higher segmentation accuracy and robustness than the related methods. Compared with the state-of-the-art methods, the proposed method obtains the best putamen, pallidum and amygdala segmentation results and hippocampus and caudate segmentation results that are similar to those of the comparison methods.
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