Detection of bladder cancers using a SAMBA 200 cell image processor.

Abstract

The cell image analysis of urinary sediments was performed using a SAMBA 200 system. Cell profiles were created using 18 parameters related to size, shape, densitometry and chromatin texture. Learning sets of about 50 cell images per class were constructed for bening, degenerated benign, atypical, malignant and degenerated malignant urothelial cell types as well as for squamous epithelial and white blood cell types. A four-level hierarchic decision tree involving a discriminant analysis at each node was designed and then evaluated against a test set of 700 cells from the various classes. All of the cell images involved in this study were acquired from Papanicolaou-stained specimens obtained for routine screening. In spite of some misclassification errors, the analysis of the occurrence of cells in the various classes, especially the percentage of cells classified as suspicious (both atypical and malignant cells), by the SAMBA 200 system resulted in the separate clustering of the positive specimens (49 carcinomas grade II and higher) and the negative ones (26 benign samples). The preliminary results suggest that the cell population features (occurrence rate of cells in the various classes and mean cell profile within a class) may be of diagnostic value in designing a classifier dedicated to the prescreening of urinary sediments for the detection of bladder cancers.