ArticleBrain sciences2020
Image Segmentation of Brain MRI Based on LTriDP and Superpixels of Improved SLIC.
Article in Brain sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- SLIC-Occ: functional segmentation of occupancy images improves precision of ECEJNMMI physics · 2023Article
- VGG16 Feature Extractor with Extreme Gradient Boost Classifier for Pancreas Cancer Prediction.Journal of imaging · 2023Article
- Latest Advances in Human Brain Dynamics.Brain sciences · 2021Article
- Superpixel Segmentation Based on Grid Point Density Peak Clustering.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
3 authors.
Funding
Abstract
Non-uniform gray distribution and blurred edges often result in bias during the superpixel segmentation of medical images of magnetic resonance imaging (MRI). To this end, we propose a novel superpixel segmentation algorithm by integrating texture features and improved simple linear iterative clustering (SLIC). First, a 3D histogram reconstruction model is used to reconstruct the input image, which is further enhanced by gamma transformation. Next, the local tri-directional pattern descriptor is used to extract texture features of the image; this is followed by an improved SLIC superpixel segmentation. Finally, a novel clustering-center updating rule is proposed, using pixels with gray difference with original clustering centers smaller than a predefined threshold. The experiments on the Whole Brain Atlas (WBA) image database showed that, compared to existing state-of-the-art methods, our superpixel segmentation algorithm generated significantly more uniform superpixels, and demonstrated the performance accuracy of the superpixel segmentation in both fuzzy boundaries and fuzzy regions.
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Registered trials
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