Image analysis based grading of bladder carcinoma. Comparison of object, texture and graph based methods and their reproducibility.
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.
Choi
H K
HK
Centre for Image Analysis, Uppsala University, Sweden.
Jarkrans
T
T
Bengtsson
E
E
Vasko
J
J
Wester
K
K
Malmström
P U
PU
Busch
C
C
D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007150:Immunohistochemistry; D008940:Mitotic Index; D011446:Prospective Studies; D015203:Reproducibility of Results; D012189:Retrospective Studies; D015996:Survival Rate; D016159:Tumor Suppressor Protein p53 / Q000378:metabolism; D001749:Urinary Bladder Neoplasms / Q000401:mortality / Q000473:pathology*
D016159:Tumor Suppressor Protein p53