The trade-offs between manual and computer-based stereology/classification: application to estimates of volume fraction

摘要

Estimation of volume or the ratio of volumes in an image requires both mensuration and classification. The former is achieved through stereology - a set of techniques that estimate such parameters as area, volume, surface area, length, and number. Classification is achieved by extracting features that capture discriminating information (e.g., about tissue type). Both stereology and classification can be performed either manually or by computer. Manual techniques for the combination are based on coarse point counting (low resolution), but assumed perfect pixel classification. Computer-based methods, on the other hand, rely on very fine point counting but in general suffer from imperfect pixel classification. This paper examines the interaction between manual and image processing-based approaches; in particular, we present a measure that combines the classification and measurement errors. Estimation of the variance is used to define the conditions under which each method is and is not advantageous despite its underlying error. This allows the user to choose a method that optimizes overall performance, given the human and machine capabilities available. Illustrations are given of cases in which each method can be preferable, as measured by the variance of the estimate of the performance that was inferred from the measurement.