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Open AccessArticle

Image Segmentation of Brain MRI Based on LTriDP and Superpixels of Improved SLIC

by Yu Wang 1,2,*, Qi Qi 2 and Xuanjing Shen 2
1
College of Applied Technology, Jilin University, Changchun 130012, China
2
College of Computer Science and Technology, Jilin University, Changchun 130012, China
*
Author to whom correspondence should be addressed.
Brain Sci. 2020, 10(2), 116; https://doi.org/10.3390/brainsci10020116
Received: 29 January 2020 / Revised: 18 February 2020 / Accepted: 18 February 2020 / Published: 20 February 2020
(This article belongs to the Special Issue Human Brain Dynamics: Latest Advances and Prospects)
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. View Full-Text
Keywords: superpixel segmentation; 3D histogram reconstruction; simple linear iterative clustering; local tri-directional pattern superpixel segmentation; 3D histogram reconstruction; simple linear iterative clustering; local tri-directional pattern
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Wang, Y.; Qi, Q.; Shen, X. Image Segmentation of Brain MRI Based on LTriDP and Superpixels of Improved SLIC. Brain Sci. 2020, 10, 116.

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