Mass Spectrometry Image Alignment via Deformation and ROI Tracking
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Solution Overview
Problem
Current methods for aligning mass spectrometry imaging data from different samples are inefficient and inaccurate, particularly when dealing with biological tissue sections, as they rely on manual deformation processing and fail to ensure that the region of interest is set consistently across images, leading to potential inclusion of data from micro regions outside the intended area in analyses.
Innovation Solution
An imaging data processing apparatus and program that perform image alignment and region of interest setting, using deformation processing to match positions and sizes of micro regions across images, and determining the corresponding micro regions within the set region of interest by considering the center point movement during alignment, ensuring accurate extraction of micro regions for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual deformation processing is used for image alignment, then flexibility in adjusting images is improved, but processing efficiency and accuracy deteriorate
Solution Approach 1:
The system performs automatic image alignment by having the images align themselves through computational processing. The alignment processing unit automatically calculates deformation amounts and applies transformations without requiring manual intervention, enabling the system to serve itself in the alignment task while maintaining high efficiency and accuracy
Solution Approach 2:
The patent replaces manual mechanical deformation operations with computational image processing. Instead of physically manipulating images through manual dragging and resizing, the system uses digital signal processing techniques including calculating deformation amounts based on reference positions and applying mathematical transformations to achieve precise alignment automatically
2Ease of operation
If manual deformation processing is used for image alignment, then ease of operation is improved, but alignment accuracy deteriorates
Solution Approach 1:
The system performs preliminary actions by automatically calculating the optimal deformation amount before applying the transformation. The alignment processing unit computes the required adjustments based on reference positions and deformation information in advance, ensuring that the alignment is performed with high precision before the actual image processing occurs
Solution Approach 2:
The patent uses copying by creating a reference image that serves as a template for alignment. The system copies the positional relationships and structural features from the reference image to guide the deformation process, ensuring that aligned images maintain consistent spatial relationships and achieve accurate registration
3Device complexity
If region of interest is set on deformed images without considering micro region movement, then processing simplicity is improved, but analysis accuracy deteriorates
Solution Approach 1:
The system implements feedback by using the deformation information to adjust the region of interest settings. The alignment processing unit provides feedback about the deformation applied to each image, and this information is used to recalculate and adjust the ROI boundaries to match the deformed micro regions, ensuring that the analysis remains accurate despite the transformations
Solution Approach 2:
The patent applies dynamics by making the region of interest adaptable and flexible rather than fixed. The system dynamically adjusts the ROI boundaries based on the deformation information, allowing the region to change its shape and position in response to the image transformations, thereby maintaining accuracy throughout the alignment process
Data Source
AI summary
In a data processing unit, alignment is performed by appropriately deforming one image among MS imaging images acquired from different samples so that positions and sizes on the MS imaging image are matched (S1 to S5). When the aligned image is displayed on a screen of a display unit and a user sets a region of interest on the image serving as a reference (S6), a micro region including a center point within a range of the set region of interest is extracted in each of an image serving as the reference and an image not serving as the reference (S7). In the image subjected to image deformation, although the shape of each micro region is distorted and micro regions are not arranged in an orderly grid manner, by assuming that the micro regions in which the center point is included within the range of the region of interest is included in the range of the region of interest, it is possible to perform a comparative analysis based on the data value within an appropriate micro region corresponding to the region of interest regardless of the image deformation.