A classifier (500) measures robustness responsive to object presentation effects and decision boundary effects. A cytological image analysis computer (562) obtains objects of interest and classifies them responsive to a decisi on tree classifier (Figure 8). The robustness of classification is calculated dynamically as objects are classified responsive to a segmentation robustnes s (38) and a classification decisiveness measure (42). The results of the decisiveness measure and the segmentation robustness data are combined to provide enhanced overall classification reliability (48).
ROBUSTNESS OF CLASSIFICATION MEASUREMENT