Multi-layer Sample Image Acquisition via Adaptive Thresholding
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Solution Overview
Problem
Existing automated systems for analyzing multi-layer samples struggle to accurately capture and identify objects across different depth levels, leading to incomplete or inaccurate object identification, as they require conversion to mono-layer samples, which is complex and expensive, or fail to capture objects in focus due to varying depths.
Innovation Solution
A method and system that captures multiple images of a Field Of View at varying focal depths, uses adaptive thresholding and contour detection to create object masks, and computes sharpness to select optimal images for accurate object identification and representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple images are captured at varying focal depths to identify objects at different depths, then object identification accuracy improves, but image processing complexity and time increase
Solution Approach 1:
The patent performs preliminary actions by capturing multiple images at varying focal depths before final object identification. This allows the system to have depth information pre-available, enabling more accurate object detection across different depth levels without requiring extensive post-processing of single images.
Solution Approach 2:
The patent segments the image processing task by dividing it into multiple focal depth planes. Each captured image represents a specific depth slice, allowing the system to process and analyze objects at different depths separately, then combine results for comprehensive identification.
2Ease of operation
If multi-layer samples are converted to mono-layer samples for analysis, then object analysis simplicity improves, but sample processing complexity and cost increase
Solution Approach 1:
The patent creates a virtual mono-layer representation through computational processing of multi-layer images. Instead of physically converting the sample, the system generates processed images that simulate mono-layer conditions, maintaining analysis simplicity while avoiding complex physical sample manipulation.
Solution Approach 2:
The patent replaces mechanical sample conversion processes with computational image processing methods. The physical multi-layer sample remains intact, but digital processing techniques create simplified representations that enable easy object analysis without physical sample modification.
3Device complexity
If traditional imaging techniques are used for multi-layer samples, then equipment simplicity is maintained, but object detection completeness deteriorates due to depth limitations
Solution Approach 1:
The patent introduces dynamic focusing capability to the imaging system, allowing the focal depth to vary across multiple positions. This dynamic adjustment enables the simple imaging equipment to capture objects at different depth levels by changing focus, thereby improving detection completeness without adding complex hardware.
Solution Approach 2:
The patent adds the depth dimension to the traditional 2D imaging approach by capturing images at multiple focal depths. This transforms the imaging process from planar to volumetric, enabling detection of objects throughout the multi-layer sample's depth while using relatively simple equipment.
Data Source
AI summary
Embodiments of present disclosure discloses system and method for acquisition of optimal images of object in multi-layer sample. Initially, images for FOV of multi-layer sample comprising objects are retrieved. Each of images are captured by varying focal depth of image capturing unit associated with system. Further, objects associated with multi-layer sample in FOV are identified. For identification, cumulative foreground mask of FOV is obtained based on adaptive thresholding performed on foreground image of FOV. Based on contour detection performed on cumulative foreground mask of FOV, object masks, corresponding to objects, is obtained for identifying objects. Further, sharpness of each of images associated with each of object masks is computed. Based on sharpness, optimal images from images for each of objects is selected for acquisition of optimal images of objects in multi-layer sample.