Please use this identifier to cite or link to this item: https://doi.org/10.1111/j.1365-2818.2010.03414.x
Title: Automatic measurement of volume percentage stroma in endometrial images using texture segmentation
Authors: Law, Y.N.
Yip, A.M. 
Lee, H.K.
Keywords: Endometrial hyperplasia
Feature selection
Mumford-Shah segmentation model
Texture segmentation
Issue Date: Feb-2011
Source: Law, Y.N., Yip, A.M., Lee, H.K. (2011-02). Automatic measurement of volume percentage stroma in endometrial images using texture segmentation. Journal of Microscopy 241 (2) : 171-178. ScholarBank@NUS Repository. https://doi.org/10.1111/j.1365-2818.2010.03414.x
Abstract: The popularity of digital microscopy and tissue microarrays allow the use of high-throughput imaging for pathology research. To coordinate with this new technique, it is essential to automate the process of extracting information from such high amount of images. In this paper, we present a new model called the Subspace Mumford-Shah model for texture segmentation of microscopic endometrial images. The model incorporates subspace clustering techniques into a Mumford-Shah model to solve texture segmentation problems. The method first uses a supervised procedure to determine several optimal subspaces. These subspaces are then embedded into a Mumford-Shah objective function so that each segment of the optimal partition is homogeneous in its own subspace. The method outperforms a widely used method in bioimaging community called k-means segmentation since it can separate textures which are less separated in the full feature space, which confirm the usefulness of subspace clustering in texture segmentation. Experimental results also show that the proposed method is well performed on diagnosing premalignant endometrial disease and is very practical for segmenting image set sharing similar properties. © 2010 Bioinformatics Institute.
Source Title: Journal of Microscopy
URI: http://scholarbank.nus.edu.sg/handle/10635/102908
ISSN: 00222720
DOI: 10.1111/j.1365-2818.2010.03414.x
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