Cell Recognition Device Using Laplacian-of-Gaussian Filter
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Solution Overview
Problem
Current image processing technologies face challenges in accurately extracting individual cells from cell images, particularly in three-dimensional cell clusters, due to issues like partial overlap and image quality deterioration, which affects the precision of cell recognition and analysis.
Innovation Solution
An image processing device and method that calculates feature values using a Laplacian-of-Gaussian filter to detect peak positions, forms cell regions based on pixel value distributions, and corrects region positions to account for proximity and morphology, enabling accurate identification and separation of cells even in overlapping conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image processing methods are used to extract individual cells from cell images, then the processing can be performed with simple algorithms, but the accuracy of cell recognition deteriorates due to partial overlap and image quality deterioration in three-dimensional cell clusters
Solution Approach 1:
The patent divides the cell image processing into multiple stages: initial cell region extraction, overlap detection, and iterative refinement. Each cell region is processed separately through multiple cycles of detection and correction, allowing accurate segmentation of overlapping cells without requiring overly complex global processing algorithms.
Solution Approach 2:
The patent performs preliminary extraction of cell regions using initial image processing before detecting overlaps and performing corrections. This preliminary action establishes a foundation for subsequent refinement steps, where overlap regions are identified and adjusted iteratively to improve accuracy without starting from scratch.
2Measurement precision
If multiple image processing steps are performed to improve cell extraction accuracy, then the recognition precision improves, but the processing time increases
Solution Approach 1:
The patent employs periodic iterative processing where cell regions are extracted, overlaps are detected, corrections are applied, and the process repeats for a predetermined number of cycles or until convergence. This periodic refinement improves accuracy progressively while allowing early termination if sufficient accuracy is achieved, balancing precision and time efficiency.
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
This approach allows for high-accuracy extraction and recognition of individual cells from complex cell images, improving the precision of cell analysis and overcoming issues related to partial overlap and image quality degradation.
Implementation Method 1
calculate a feature value, the feature value representing how likely a pixel value in each of pixels in a cell image formed by capturing an image of a cell cluster composed of a plurality of cells is to be an extreme value; detect, as peak positions, pixel positions the feature value of which are greater than a prescribed feature value threshold value in the cell image
Data Source
AI summary
Provided is an image processing device including: a memory; and a processor comprising hardware, the processor configured to: calculate a feature value; detect, as peak positions, pixel positions the feature value of which are greater than a prescribed feature value threshold value; record the detected peak positions; form, one at a time for the detected peak positions, a cell region; identify a center position of the formed cell region; determine, by using at least one of the peak position, a morphology of the cell region, and the center position of the cell region, a proximity state between the currently formed cell region and a previously formed cell region; and correct, when it is determined that the proximity state is satisfied, at least one of the currently formed cell region and the previously formed cell region.