A supervised learning framework for pancreatic islet segmentation with multi-scale color-texture features and rolling guidance filters.
Cytometry A
; 89(10): 893-902, 2016 10.
Article
em En
| MEDLINE
| ID: mdl-27560544
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.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Diagnóstico por Imagem
/
Ilhotas Pancreáticas
Tipo de estudo:
Diagnostic_studies
/
Guideline
Limite:
Animals
Idioma:
En
Revista:
Cytometry A
Ano de publicação:
2016
Tipo de documento:
Article
País de afiliação:
China