Geometrical and Statistical Visual Inspection of Imprinted Tablets

In this paper we address automated visual inspection of tablets that may, in contrast to manual tablet sorting, provide objective and reproducible tablet quality assurance. Visual inspection of the ever–increasing numbers of the produced imprinted tablets, regulatory enforced for unambiguous identification of active ingredients and dosage strength of each tablet, is especially demanding. The problem becomes more tractable by incorporating some a priori knowledge of the imprint shape and/or appearance. For this purpose, we consider two alternatives, the so-called geometrical and statistical image analysis methods. The geometrical method, incorporating geometrical a priori knowledge of the imprint shape, enables specific inspection of imprinted and non-imprinted tablet surface, while the statistical method exploits a priori knowledge of tablet surface appearance, derived from a training image database. The two methods were evaluated on a large tablet image database, consisting of 3445 images of four types of imprinted tablets, with and without typical production defects. A “gold standard” for testing the performances of the two inspection methods was established by manually classifying the tablets. The results, obtained by ROC analysis, indicated that statistical method yields better defect detection sensitivity and specificity and is thus more suitable for automatic visual inspection of imprinted tablets.