Annotation and retrieval of cell images

Authors:
Maria F. O'Connor;Arthur Hughes;Chaoxin Zheng;Anthony Davies;Dermot Kelleher;Khurshid Ahmad
Affiliations:
School of Computer Science & Statistics, Trinity College, Dublin, Ireland;School of Computer Science & Statistics, Trinity College, Dublin, Ireland;School of Computer Science & Statistics, Trinity College, Dublin, Ireland;Institute of Molecular Medicine, Trinity College, Dublin, Ireland;Institute of Molecular Medicine, Trinity College, Dublin, Ireland;School of Computer Science & Statistics, Trinity College, Dublin, Ireland
Venue:
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
Year:
2010

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Abstract

A multi-net neural computing system is described that can be used for classifying images based on intrinsic image features and extrinsic collateral linguistic description of the contents. A novel representation scheme based on wavelet analysis of images and a subsequent Zernike moment computation helps in a systematic extraction of image features; collateral linguistic description are obtained by the automatic extraction of single and compound keywords. We give a formal description of the system using the Z formal specification notation. An image data set comprising 480 fluorescent stained images of lymphocytes was used in the test of a 3-component unsupervised multi-net neural computing system. The classification accuracy of this system was found to be just over 85%.