Spot Detection in Nucleic Acid Sequencing Images
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Solution Overview
Problem
Current image processing methods in gene sequencing, especially for third-generation sequencing, face challenges in accurately detecting and positioning spots in images with randomly distributed probes, which is crucial for precise nucleotide identification and data generation.
Innovation Solution
A method and system for image processing that includes pre-processing steps such as background subtraction and filtering, followed by spot detection using a computed threshold to identify and validate candidate spots, allowing for sub-pixel center coordinate and intensity value calculation, thereby improving spot detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image pre-processing is performed to reduce noise and computation, then spot detection accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies image pre-processing operations (noise reduction, background subtraction, filtering) before spot detection to improve detection accuracy. By performing these preparatory actions in advance, the actual spot detection can be more accurate and efficient, resolving the contradiction between preprocessing overhead and detection quality.
Solution Approach 2:
The patent divides the image processing into distinct segments: pre-processing phase (noise reduction, background subtraction) and spot detection phase (threshold computation, candidate identification). This segmentation allows optimization of each phase independently, balancing the time cost of pre-processing with the accuracy benefits.
2Measurement precision
If a computed threshold is used to validate candidate spots, then spot identification accuracy is improved, but computation complexity increases
Solution Approach 1:
The patent computes an optimal threshold parameter from the image data itself (using statistical methods like Otsu's method or adaptive thresholding) rather than using a fixed threshold. This dynamic parameter adjustment improves spot identification accuracy by adapting to different image conditions, while the threshold computation is performed once rather than for each candidate spot.
Solution Approach 2:
The system uses the image data itself to generate the threshold parameter through automated computation, eliminating the need for manual threshold setting or complex external calibration. The algorithm self-adjusts the threshold based on the specific characteristics of each image.
3Measurement precision
If sub-pixel center coordinate computation is performed, then positioning precision is improved, but processing complexity increases
Solution Approach 1:
The patent extends the coordinate measurement from pixel level to sub-pixel level by introducing fractional coordinate values. This dimensional refinement allows positioning precision beyond the discrete pixel grid, achieving sub-pixel accuracy through interpolation methods while maintaining computational feasibility.
Solution Approach 2:
The patent applies sub-pixel computation only to the identified candidate spots rather than processing the entire image at sub-pixel resolution. This partial application of the computationally intensive operation limits the complexity increase to only the necessary locations, balancing precision requirements with processing efficiency.
Data Source
AI summary
The present disclosure provides a method and a system for processing an image. The method includes: an image pre-processing step, including analyzing an image input to be processed so as to obtain a first image; and a spot detecting step, including analyzing the first image so as to compute a spot determining threshold; analyzing the first image so as to acquire a candidate pixel spot, and judging whether the candidate pixel spot is the spot according to the spot determining threshold; computing a sub-pixel center coordinate of the spot and an intensity value of the sub-pixel center coordinate if yes; and discarding the candidate pixel spot if no. According to the method, the image is denoised in the image pre-processing step, such that a calculation in the spot detecting step may be reduced. In addition, the accuracy of determining the image spot may be improved.