Investigating pill recognition methods for a new national library of medicine image dataset

Abstract

With the increasing access to pharmaceuticals, chances are that medication administration errors will occur more frequently. On average, individuals above age 65 take at least 14 prescriptions per year. Unfortunately, adverse drug reactions and noncompliance are responsible for 28% of hospitalizations of the elderly. Correctly identifying pills has become a critical task in patient care and safety. Using the recently released National Library of Medicine (NLM) pill image database, this paper investigates descriptors for pill detection and characterization. We describe efforts in investigating algorithms to segment NLM pills images automatically, and extract several features to assembly pill groups with priors based on FDA recommendations for pill physical attributes. Our contributions toward pill recognition automation are three-fold: we evaluate the 1,000 most common medications in the United States, provide masks and feature matrices for the NLM reference pill images to guarantee reproducibility of results, and discuss strategies to organize data for efficient content-based image retrieval.