Adaptable Ring for Vision-Based Measurements and Shape Analysis

A vision-based measurement approach for pill shape detection is presented along with other applications. Rapid and accurate pill identification is needed by medical and law enforcement personnel during emergencies. But real-world pill identification is challenging due to varied lighting conditions, minor manufacturing defects, and subsequent pill wear. Surmounting these challenges is possible using multiple inputs: pill color, imprint, and shape. Of these different inputs, pill shape is the most important and difficult parameter due to its variations. In this paper, we describe a novel technique to accurately detect the complex pharmaceutical pill shapes using measurements derived from a superimposed adaptable ring centered automatically on either the shape's centroid or its bounding box midpoint determined based on the measurements from two other rings, namely the inner ring and the outer ring. It is shown that the measurements from the overlays of the adaptable ring suffice to successfully classify the shapes of the pills currently in the Pillbox database (U.S. National Library of Medicine, 2014) with an accuracy of 98.7%. Our method demonstrated higher accuracy when compared with Hu-moments on the same data set. Using logistic regression techniques, Hu-moments provided an accuracy of 96.6%. Though developed for the domain of pharmaceutical pill shapes, we discuss how the measurements from the adaptable ring can also be used in other industrial applications to increase the level of accuracy with the help of this real-time less computationally complex method.