Graph-based multi-resolution segmentation of histological whole slide images

Authors:
V. Roullier;V-T. Ta;O. Lézoray;A. Elmoataz
Affiliations:
Université de Caen Basse-Nonnandie, Caen Cedex, France;Université de Caen Basse-Nonnandie, Caen Cedex, France;Université de Caen Basse-Nonnandie, Caen Cedex, France;Université de Caen Basse-Nonnandie, Caen Cedex, France
Venue:
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Year:
2010

Citing 4
Cited 1

An Efficient k-Means Clustering Algorithm: Analysis and Implementation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer-aided evaluation of neuroblastoma on whole-slide histology images: Classifying grade of neuroblastic differentiation

Pattern Recognition
A boosting cascade for automated detection of prostate cancer from digitized histology

MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Nonlocal Discrete Regularization on Weighted Graphs: A Framework for Image and Manifold Processing

IEEE Transactions on Image Processing

Mitosis extraction in breast-cancer histopathological whole slide images

ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present a graph-based multi-resolution approach for mitosis extraction in breast cancer histological whole slide images. The proposed segmentation uses a multiresolution approach which reproduces the slide examination done by a pathologist. Each resolution level is analyzed with a focus of attention resulting from a coarser resolution level analysis. At each resolution level, a spatial refinement by semi-supervised clustering is performed to obtain more accurate segmentation around edges. The proposed segmentation is fully unsupervised by using domain specific knowledge.