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Analytical Cellular Pathology
Volume 23 (2001), Issue 2, Pages 75-88
http://dx.doi.org/10.1155/2001/683747

Prognostic Classification of Early Ovarian Cancer Based on very Low Dimensionality Adaptive Texture Feature Vectors from Cell Nuclei from Monolayers and Histological Sections

Birgitte Nielsen,1,2 Fritz Albregtsen,1 Wanja Kildal,2 and Håvard E. Danielsen2,3

1Department of Informatics, University of Oslo, P.O.Box 1080 Blindern, N‐0316 Oslo, Norway
2Division of Digital Pathology, The Norwegian Radium Hospital, Montebello, N‐0310 Oslo, Norway
3Division of Genomic Medicine, The University of Sheffield, Sheffield, S102TN, England

Accepted 13 November 2001

Copyright © 2001 Hindawi Publishing Corporation. 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

In order to study the prognostic value of quantifying the chromatin structure of cell nuclei from patients with early ovarian cancer, low dimensionality adaptive fractal and Gray Level Cooccurrence Matrix texture feature vectors were extracted from nuclei images of monolayers and histological sections. Each light microscopy nucleus image was divided into a peripheral and a central part, representing 30% and 70% of the total area of the nucleus, respectively. Textural features were then extracted from the peripheral and central parts of the nuclei images. The adaptive feature extraction was based on Class Difference Matrices and Class Distance Matrices. These matrices were useful to illustrate the difference in chromatin texture between the good and bad prognosis classes of ovarian samples. Class Difference and Distance Matrices also clearly illustrated the difference in texture between the peripheral and central parts of cell nuclei. Both when working with nuclei images from monolayers and from histological sections it seems useful to extract separate features from the peripheral and central parts of the nuclei images.