Dot Detection in Tissue Images Using Gaussian Derivatives
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Solution Overview
Problem
Existing image analysis methods for detecting dots in tissue samples, such as CISH and FISH, face challenges in distinguishing dots from lines and edges, requiring extensive parameter tuning and being less effective in noisy environments due to the use of multi-dimensional kernels.
Innovation Solution
The method employs a Gaussian gradient operator to calculate second and first order derivative values, inverts and filters these values, and uses specific multiplication and thresholding techniques to identify dots, leveraging the properties of Gaussian kernels for improved dot detection in both noisy and non-noisy conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multi-dimensional kernels are used for dot detection, then detection coverage is improved, but noise averaging capability deteriorates
Solution Approach 1:
The patent divides the detection process into separate one-dimensional operations along different axes. Instead of using a single multi-dimensional kernel, the method applies one-dimensional kernels separately along the x and y axes, then combines the results. This segmentation allows each one-dimensional operation to effectively average noise along its specific direction while maintaining overall detection coverage through the combination of results from multiple axes.
2Ease of operation
If traditional dot detection methods are used, then detection simplicity is maintained, but parameter tuning complexity increases
Solution Approach 1:
The patent transforms the dot detection problem into a parameter estimation problem by changing the approach from using fixed multi-dimensional kernels to using one-dimensional kernels with adjustable parameters. By estimating parameters such as dot size and position through the analysis of one-dimensional projections, the method reduces the number of parameters that need to be simultaneously tuned while maintaining detection accuracy. This parameter transformation simplifies the overall system by breaking down complex multi-parameter optimization into simpler, more manageable adjustments.
Data Source
AI summary
We present a method to detect dots in microscope images of tissue samples for the purpose of diagnosis or therapy selection. Dots in tissue arise trough CISH or FISH staining, but also through staining of small cellular compartments, or in other ways. The method is applicable to both brightfield and fluorescence modalities, as well as mass spectrometry imaging methodologies. The method is based on the use of first and second derivatives of the image along the image axes, computed using regularized derivative operators, i.e. Gaussian derivatives. The outputs of these operators are examined at appropriate distances from each location in the image. The found values must have specific signs. If the signs are all correct, these values are multiplied together to obtain a confidence measure for a dot being present at that location. If the signs do not match, no dot is present, and a zero is output.