Open AccessMethod for counting mitoses by image processing in feulgen stained breast cancer sections
Author(s) -
ten Kate T. K.,
Beliën J. A. M.,
Smeulders A. W. M.,
Baak J. P. A.
Publication year - 1993
Publication title -
cytometry
Language(s) - English
Resource type - Journals
eISSN - 1097-0320
pISSN - 0196-4763
DOI - 10.1002/cyto.990140302
Subject(s) - segmentation , feulgen stain , computer science , image processing , artificial intelligence , false positive paradox , set (abstract data type) , pattern recognition (psychology) , image segmentation , computer vision , image (mathematics) , pathology , medicine , staining , programming language
This study describes an image processing method for the assessment of the mitotic count in Feulgen‐stained breast cancer sections. The segmentation procedure was optimized to eliminate 95–98% of the nonmitoses, whereas 11% of the mitoses did not survive the segmentation procedure. Contour features and optical density measurements of the remaining objects were computed to allow for classification. Twelve specimens were analyzed, nine used to serve as a training set, and three put aside for later use as independent test set. The fully automatic image processing method correctly classified 81% of the mitoses at the specimen level while inserting 30% false positives. The automatic procedure strongly correlated with the interactive counting procedure (r = 0.98). Although the fully automatic method provided satisfactory results, it is not yet suited for clinical practice. The automated method with an interactive evaluation step gave an accurate reflection of the mitotic count showing an almost perfect correlation with the results of the interactive morphometry (r = 0.998). Therefore this semiautomated method may be useful as prescreening device. © 1993 Wiley‐Liss, Inc.