Cell Image Analysis via Multi-Thresholding and Smoothing
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Solution Overview
Problem
Traditional morphological operations fail to accurately extract cell body regions from pathological images due to jagged cell boundaries and incomplete filling, often merging or deleting important cell regions.
Innovation Solution
A computer-implemented method and system using a multi-level thresholding, smoothing, cell grouping, and recovering missing region modules to process fluorescent images, smoothing boundaries, filling holes, and separating touching cells, which includes thresholding, smoothing, classifying cells as convex or concave, and recovering missing regions to generate accurate cell shapes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional morphological operations are used for cell extraction, then the processing is simple and fast, but the cell body regions are inaccurately extracted due to merging separated cells or deleting important cell regions
Solution Approach 1:
The patent divides the cell extraction process into multiple distinct modules: multi-level thresholding for initial segmentation, smoothing for boundary refinement, hole filling for complete region recovery, and convex/concave cell separation for final differentiation. Each module addresses specific aspects of the extraction problem, improving overall accuracy while maintaining manageable complexity through modular design.
Solution Approach 2:
The patent applies preliminary smoothing operations to cell boundaries before performing thresholding and segmentation. By pre-processing the images to reduce jagged edges and normalize cell appearances, the subsequent extraction steps become more accurate and reliable, preventing the merging of separated cells and preserving important cell regions.
2Manufacturing precision
If multi-level thresholding and smoothing operations are applied, then cell boundary accuracy is improved, but processing time increases
Solution Approach 1:
The patent employs multi-level thresholding where threshold values are applied in sequential stages rather than a single step. The process iteratively refines cell region extraction by applying different threshold levels to progressively improve boundary accuracy, allowing the system to achieve high precision while managing computational load through staged processing.
Solution Approach 2:
The patent changes the threshold parameter values across multiple processing levels, starting with broader thresholds for initial segmentation and progressively using more refined thresholds for boundary refinement. This parameter variation strategy enables accurate cell boundary extraction while avoiding excessive computational complexity by systematically adjusting threshold values rather than using overly complex algorithms.
3Shape
If aggressive smoothing is applied to cell boundaries, then jagged profiles are reduced, but cell shape fidelity may be compromised
Solution Approach 1:
The patent applies smoothing operations selectively and partially to cell boundaries rather than uniformly to all regions. The smoothing is applied primarily to reduce jagged profiles while preserving important shape characteristics through controlled application of smoothing algorithms, and the degree of smoothing can be adjusted to balance boundary smoothness against shape fidelity based on specific imaging conditions.
Data Source
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AI summary
A method, computer program, and system are disclosed of enhancing cell images for analysis. The method includes performing a multi-thresholding process on a cell image to generate a plurality of images of the cell image; smoothing each component within each of the plurality of images; merging the smoothed components into a merger layer; classifying each of the components of the merged layer into convex cell regions and concave cell regions; combining the concave cell regions with a cell boundary for each of the corresponding concave cell regions to generate a smoothed shape profile for each of the concave cell regions; and generating an output image by combining the convex cell regions with the concave cell regions with smoothed shape profiles.