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

Brain Sciences

Yu Wang¹  Xuanjing Shen²  Qi Qi² 

[1] College of Applied Technology, Jilin University, Changchun 130012, China;College of Computer Science and Technology, Jilin University, Changchun 130012, China;

关键词: superpixel segmentation;    3d histogram reconstruction;    simple linear iterative clustering;    local tri-directional pattern;   

DOI  :  10.3390/brainsci10020116

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown