JOURNAL ARTICLE
RESEARCH SUPPORT, N.I.H., EXTRAMURAL
RESEARCH SUPPORT, NON-U.S. GOV'T
Add like
Add dislike
Add to saved papers

A supervised learning framework for pancreatic islet segmentation with multi-scale color-texture features and rolling guidance filters.

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.

Full text links

We have located links that may give you full text access.
Can't access the paper?
Try logging in through your university/institutional subscription. For a smoother one-click institutional access experience, please use our mobile app.

For the best experience, use the Read mobile app

Mobile app image

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app

All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.

By using this service, you agree to our terms of use and privacy policy.

Your Privacy Choices Toggle icon

You can now claim free CME credits for this literature searchClaim now

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app