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International Journal of Biomedical Imaging
Volume 2011 (2011), Article ID 270247, 11 pages
http://dx.doi.org/10.1155/2011/270247
Research Article

Segmentation of Endothelial Cell Boundaries of Rabbit Aortic Images Using a Machine Learning Approach

1Department of Bioengineering, Imperial College London, London SW7 2AZ, UK
2Bristol Heart Institute, Bristol Royal Infirmary, Bristol BS2 8HW, UK
3Laboratoire d'InfoRmatique en Image et Systèmes d'information, Institut National des Sciences Appliquées de Lyon, LIRIS INSA De Lyon, 69621 Villeurbanne, France

Received 27 July 2010; Revised 19 March 2011; Accepted 30 March 2011

Academic Editor: Tiange Zhuang

Copyright © 2011 Saadia Iftikhar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents an automatic detection method for thin boundaries of silver-stained endothelial cells (ECs) imaged using light microscopy of endothelium mono-layers from rabbit aortas. To achieve this, a segmentation technique was developed, which relies on a rich feature space to describe the spatial neighbourhood of each pixel and employs a Support Vector Machine (SVM) as a classifier. This segmentation approach is compared, using hand-labelled data, to a number of standard segmentation/thresholding methods commonly applied in microscopy. The importance of different features is also assessed using the method of minimum Redundancy, Maximum Relevance (mRMR), and the effect of different SVM kernels is also considered. The results show that the approach suggested in this paper attains much greater accuracy than standard techniques; in our comparisons with manually labelled data, our proposed technique is able to identify boundary pixels to an accuracy of 93%. More significantly, out of a set of 56 regions of image data, 43 regions were binarised to a useful level of accuracy. The results obtained from the image segmentation technique developed here may be used for the study of shape and alignment of ECs, and hence patterns of blood flow, around arterial branches.