title: A supervised learning framework for pancreatic islet segmentation with multi-scale color-texture features and rolling guidance filters.
abstract: Islet cell quantification and function is important for developing novel therapeutic interventions for diabetes. Existing methods of pancreatic islet segmentation in histopathological images depend strongly on cell/nuclei detection, and thus are limited due to a wide variance in the appearance of pancreatic islets. In this paper, we propose a supervised learning pipeline to segment pancreatic islets in histopathological images, which does not require cell detection. The proposed framework firstly partitions images into superpixels, and then extracts multi-scale color-texture features from each superpixel and processes these features using rolling guidance filters, in order to simultaneously reduce inter-class ambiguity and intra-class variation. Finally, a linear support vector machine (SVM) is trained and applied to segment the testing images. A total of 23 hematoxylin-and-eosin-stained histopathological images with pancreatic islets are used for verifying the framework. With an average accuracy of 95%, training time of 20 min and testing time of 1 min per image, the proposed framework outperforms existing approaches with better segmentation performance and lower computational cost. © 2016 International Society for Advancement of Cytometry.
authors: School of Information Science and Engineering, Xiamen University, Xiamen, China.|Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, 15213, Pennsylvania.
authors: Yue
authors: Y
authors: Huang
authors: Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, 15213, Pennsylvania.
authors: Chi
authors: C
authors: Liu
authors: Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, 15224, Pennsylvania.
authors: John F
authors: JF
authors: Eisses
authors: Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, 15224, Pennsylvania.
authors: Sohail Z
authors: SZ
authors: Husain
authors: Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, 15213, Pennsylvania. gustavor@cmu.edu.|Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, 15213, Pennsylvania. gustavor@cmu.edu.
authors: Gustavo K
authors: GK
authors: Rohde
mesh_terms: D000818:Animals; D003952:Diagnostic Imaging / Q000379:methods*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007515:Islets of Langerhans / Q000473:pathology*; D008297:Male; D051379:Mice; D010363:Pattern Recognition, Automated / Q000379:methods; D060388:Support Vector Machine
keywords: histopathological image segmentation; multi-scale features; pancreatic islet; rolling guidance filter; supervised learning