DocumentCode
3344464
Title
Improving histology images segmentation through spatial constraints and supervision
Author
Herve, Nicolas ; Servais, Aude ; Thervet, Eric ; Olivo-Marin, Jean-Christophe ; Meas-Yedid, Vannary
Author_Institution
Quantitative Image Anal. Unit, Inst. Pasteur, Paris, France
fYear
2010
fDate
26-29 Sept. 2010
Firstpage
3633
Lastpage
3636
Abstract
We introduce two approaches to improve an existing color segmentation technique based on a Split and Merge quantization process for the study of stained histological images. We propose to modify the merge criterion : first, we include a spatial constraints heuristic; then we suggest the use of supervision and a more elaborated visual features representation. We tested these approaches on a renal biopsies dataset to automatically quantify interstitial fibrosis and show that supervision brings very significant improvements.
Keywords
biological tissues; image colour analysis; image segmentation; medical image processing; color segmentation technique; histology image segmentation; interstitial fibrosis; spatial constraint heuristic; spatial supervision; split-and-merge quantization process; visual features representation; Color; Image color analysis; Image segmentation; Merging; Pixel; Quantization; Support vector machines; Color segmentation; color quantization; heuristic; histology; learning framework; spatial constraints;
fLanguage
English
Publisher
ieee
Conference_Titel
Image Processing (ICIP), 2010 17th IEEE International Conference on
Conference_Location
Hong Kong
ISSN
1522-4880
Print_ISBN
978-1-4244-7992-4
Electronic_ISBN
1522-4880
Type
conf
DOI
10.1109/ICIP.2010.5652083
Filename
5652083
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