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Analytical Cellular Pathology
Volume 15 (1997), Issue 1, Pages 1-18
http://dx.doi.org/10.1155/1997/147187

Image Analysis Based Grading of Bladder Carcinoma. Comparison of Object, Texture and Graph Based Methods and Their Reproducibility

Heung‐Kook Choi,1,5 Torsten Jarkrans,1 Ewert Bengtsson,1 Janos Vasko,2 Kenneth Wester,3 Per-Uno Malmström,4 and Christer Busch3

1Centre for Image Analysis, Uppsala University, Uppsala, Sweden
2Department of Pathology, Umeå University, Umeå, Sweden
3Department of Pathology, University Hospital, Uppsala, Sweden
4Department of Urology, University Hospital, Uppsala, Sweden
5School of Information and Computer Science, Inje University, Kim‐Hae, Republic of Korea

Received 17 September 1996; Revised 21 May 1997

Copyright © 1997 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

The possibility that computerized image analysis could increase the reproducibility of grading of bladder carcinoma as compared to conventional subjective grading made by pathologists was investigated. Object, texture and graph based analysis were carried out from Feulgen stained histological tissue sections. The object based features were extracted from gray scale images, binary images obtained by thresholding the nuclei and several other images derived through image processing operations. The textural features were based on the spatial gray‐tone co‐occurrence probability matrices and the graph based features were extracted from the minimum spanning trees connecting all nuclei. The large numbers of extracted features were evaluated in relation to subjective grading and to factors related to prognosis using multivariate statistical methods and multilayer backpropagation neural networks. All the methods were originally developed and tested on material from one patient and then tested for reproducibility on entirely different patient material. The results indicate reasonably good reproducibility for the best sets of features. In addition, image analysis based grading showed almost identical correlation to mitotic density and expression of p53 protein as subjective grading. It should thus be possible to use this kind of image analysis as a prognostic tool for bladder carcinoma.