Blood Cell Image Focus Adjustment via Lightness Analysis
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Solution Overview
Problem
Current blood cell analysis systems face challenges in capturing high-quality images due to focus issues caused by temperature changes and the inability to effectively focus on all types of blood cells, leading to the need for improved detection and refocusing methods.
Innovation Solution
A system using a processor and image capture device that determines a focus distance by identifying cell boundaries, generating rings offset from the boundaries, and calculating a predicted nominal focus value based on lightness values of pixels within these rings to assess and adjust image focus.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional focusing methods are used, then the system can maintain focus under stable conditions, but the system fails to adapt to temperature changes and different blood cell types
Solution Approach 1:
The system employs a feedback mechanism where the image quality metric is calculated from captured images and used to automatically adjust the focus of the imaging device. This closed-loop control enables the system to adapt to temperature changes and different blood cell types by continuously monitoring image quality and making real-time focus adjustments, resolving the contradiction between adaptability and stability.
2Manufacturing precision
If manual focusing adjustments are made frequently, then image quality can be maintained, but the operation complexity and time consumption increase
Solution Approach 1:
The system performs self-service by automatically evaluating image quality and adjusting focus without user intervention. The processor calculates the image quality metric and controls the focus mechanism autonomously, eliminating the need for manual focusing operations while maintaining high image quality, thus resolving the contradiction between precision and ease of operation.
3Measurement precision
If advanced focusing algorithms are implemented, then focus accuracy improves, but the processing time and computational complexity increase
Solution Approach 1:
The system extracts only the essential information needed for focus evaluation by calculating an image quality metric based on specific image characteristics rather than performing comprehensive image analysis. This selective extraction of critical features maintains focus measurement accuracy while significantly reducing processing time and computational complexity, resolving the contradiction between precision and time consumption.
Data Source
AI summary
A visual analysis system may be automatically focused (or the focus of such a system may be automatically corrected) by subjecting one or more images captured by such a system to a multi-layer analysis. In such an analysis, a cell boundary may be identified in an input image based on the lightness value of the pixels of the input image. Based on the identified cell boundary, a predicted nominal focus value is determined which can provide a focusing distance (e.g., a distance between the focal plane of a camera used when an image was captured and the actual focal plane for an in-focus image). This focusing distance may then be used to (re) focus a camera or for other purposes (e.g., generating an alert).