Machine Vision Pill Verification Using Image Feature Comparison
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Solution Overview
Problem
Current medication verification systems in pharmacies lack the ability to accurately confirm whether the pills dispensed match the prescribed medication, relying on manual counting or weight-based methods that do not ensure the correct type of pills are being dispensed.
Innovation Solution
A machine vision counting and verification system that uses image processing to compare the visually observable features of pills on a tray to reference data, generating a likelihood rating to confirm the pills match the prescribed medication, utilizing a database of features captured with the same camera and lighting conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual counting is used to verify pills, then the pharmacist can visually confirm the correct medication, but the process is slow and cumbersome
Solution Approach 1:
The patent replaces the manual mechanical counting process with an automated optical imaging system. A camera captures images of pills on a tray, and image processing algorithms automatically count and verify the pills, substituting human visual inspection and manual counting with an automated vision-based system that maintains verification accuracy while dramatically increasing speed.
Solution Approach 2:
The system creates digital copies (images) of the physical pills for analysis. By capturing images of the pills and processing these digital representations, the system can count and verify medications without physically handling each pill, thereby speeding up the process while maintaining verification reliability.
2Productivity
If weighing scales are used to count pills, then the counting process is faster, but there is no inherent provision for determining whether the correct type of pills are being dispensed
Solution Approach 1:
The patent merges the counting function and verification function into a single integrated system. The same optical imaging system that counts the pills also verifies their identity by analyzing visual features such as color, shape, size, and markings. This combination eliminates the need for separate verification steps while maintaining both speed and accuracy.
Solution Approach 2:
The imaging system performs multiple functions: it counts the pills, identifies their type through feature analysis, and verifies they match the prescription. This multi-functional approach replaces the specialized weighing scale for counting with a universal vision system that can both count and verify medication identity.
3Productivity
If optical beam pour through systems are used to count pills, then vision based counting techniques are employed, but these systems cannot determine whether the pills counted are the correct pills
Solution Approach 1:
The system goes beyond simple counting by analyzing multiple visual features of the pills including color, shape, size, and surface markings. This excessive analysis of pill characteristics ensures accurate identification and verification while maintaining high-speed counting capability through automated image processing.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the accuracy of medication verification by ensuring the correct pills are dispensed, improving the efficiency and reliability of the prescription filling process.
Implementation Method 1
receiving image data generated responsive to disposal of the units on a tray disposed a distance from an image acquisition component where the image data includes data indicative of visually observable features of the units
Data Source
AI summary
A method of processing graphical image data representing optically scanned medication-related units may include receiving image data generated responsive to disposal of the units on a tray disposed a distance from an image acquisition component, the image data including data indicative of visually observable features of the units disposed on the tray. The method further includes comparing at least two features among the visually observable features from the image data to reference data indicative of corresponding features of reference units. The reference data is selected for comparison based on an identification of the reference data as corresponding to a prescription being processed. The reference data includes data indicative of features of the reference units extracted from images captured using hardware corresponding to hardware used to generate the image data. The method further includes generating a likelihood rating for each of the at least two features based on the comparing.