Automatic identification of prescription drugs using shape distribution models.

Caban JJ, Rosebrock A, Yoo TS

In: 19th IEEE International Conference on Image Processing (ICIP); 2012 Sep 30-Oct 3; Lake Buena Vista, Florida. p. 1005-8. DOI: 10.1109/ICIP.2012.6467032

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Abstract:

Medication errors are one of the safety problems most frequently seen in hospital organizations. It is estimated that 12.2% of all hospitalized patients are involved in some form of adverse drug event (ADE) [1]. A significant amount of ADEs result from handing the incorrect drug to a patient or prescribing the wrong medication. This paper introduces a simple yet robust classification technique that can be used to automatically identify prescriptions drugs within images. The system uses a modified shape distribution technique to examine the shape, color, and imprint of a pill and create an invariant descriptor that can be used to recognize the same drug under different viewing conditions. The proposed technique has been successfully evaluated with 568 of the most prescribed drugs in the United States and has shown a 91.13% accuracy in automatically identifying the correct medication.

Caban JJ, Rosebrock A, Yoo TS. Automatic identification of prescription drugs using shape distribution models. 
In: 19th IEEE International Conference on Image Processing (ICIP); 2012 Sep 30-Oct 3; Lake Buena Vista, Florida. p. 1005-8. DOI: 10.1109/ICIP.2012.6467032