Medication Imaging Pre-processing for Dispensing Error Detection
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Solution Overview
Problem
Automated systems in pharmacies face challenges in detecting dispensing errors with medication due to difficulties in differentiating between medication and packaging, and in recognizing various arrangements and angles of medication items, leading to reliance on costly and error-prone human spot checks.
Innovation Solution
A machine vision system that captures images of medication, processes them to identify pixels corresponding to the receptacle, and excludes areas outside the defined ellipse of the receptacle from further processing, enhancing image analysis by focusing only on the medication within the bottle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated image processing is used to detect dispensing errors, then productivity and coverage increase, but measurement precision deteriorates due to difficulty in differentiating medication from packaging and recognizing various arrangements
Solution Approach 1:
The patent applies preliminary action by performing image pre-processing before the main analysis. Specifically, it converts the original image to a processed image that highlights only the medication items within the receptacle, excluding packaging and other irrelevant elements. This preliminary processing step prepares the image data in advance to improve the accuracy of subsequent error detection algorithms.
Solution Approach 2:
The patent extracts the relevant information by isolating medication items from the complex background of packaging and receptacle. The pre-processing algorithm identifies and extracts only the pixels corresponding to medication items, creating a simplified image representation that focuses attention solely on what needs to be detected for error identification.
2Measurement precision
If human spot checks are used to verify dispensing accuracy, then measurement precision improves through careful visual inspection, but productivity decreases and cost increases
Solution Approach 1:
The patent creates a processed image copy that represents the medication items in a simplified, enhanced format. This processed image serves as a copy of the original scene but with irrelevant elements removed and medication items highlighted, making it easier and faster for automated systems to analyze accurately without requiring human visual inspection.
3Measurement precision
If comprehensive image processing is applied to the entire image, then measurement precision improves, but device complexity and processing time increase
Solution Approach 1:
The patent extracts and removes irrelevant elements (packaging, receptacle, background) from the image processing pipeline. By pre-processing to identify and exclude these elements, the system reduces the complexity of subsequent processing steps, as algorithms only need to analyze the simplified regions containing medication items rather than the entire complex image.
Solution Approach 2:
The patent segments the image into relevant and irrelevant regions through pre-processing. By dividing the image analysis into distinct phases (identification of receptacle boundaries, extraction of medication items, and focused analysis), the system reduces overall processing complexity while maintaining high measurement precision for medication identification.
Data Source
AI summary
A computer system includes an input configured to receive a first image of medication located in a receptacle, memory, and a processor configured to execute instructions including creating a second image based on the first image, dividing pixels of the second image into first and second subsets, and scanning the second image along a first axis to count, for each point along the first axis, a number of pixels in the first subset along a line perpendicular to the first axis that intersects the first axis at the point. The instructions also include estimating positions of first and second edges of the receptacle along the first axis based on the counts of the pixels, defining an opening of the receptacle based on the estimated positions of the first and second edges, and outputting a processed image that indicates areas of the image that are outside of the defined opening.