Image Processing Device for Emphasizing Linear Features in Fluorescence Microscopy
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Solution Overview
Problem
Existing image processing technologies face challenges in effectively emphasizing linear features, such as neurites in cell images, while minimizing noise and accurately extracting these features from captured images, especially in fluorescence microscopy where pixel signals corresponding to linear portions are weak.
Innovation Solution
An image processing device and method that includes multiple processing parts to set pixel values for weighted images, utilizing first and second pixel groups aligned at various angles, calculating candidate pixel values based on pixel values within these groups, and setting pixel values in weighted images to emphasize linear portions by smoothing and binarization, thereby enhancing the visibility of linear features like neurites.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image processing is applied to emphasize linear features, then linear features become more visible, but noise is also amplified and linear portions may be lost
Solution Approach 1:
The patent divides the image processing into multiple stages: first image processing to generate intermediate pixel values, second image processing to generate alternative pixel values, and weighted combination to produce final pixel values. This segmentation allows each stage to focus on specific aspects (noise reduction, edge preservation, linear feature enhancement) without amplifying noise globally, thus resolving the contradiction between feature visibility and noise amplification.
Solution Approach 2:
The patent dynamically adjusts the weighting parameter (alpha) in the weighted combination formula based on local image characteristics such as gradient magnitude and pixel group variance. By changing this parameter adaptively rather than using a fixed value, the system can enhance linear features in regions where they exist while suppressing noise in regions where they don't, thus resolving the contradiction between feature enhancement and noise control.
2Object-affected harmful factors
If noise reduction processing is applied to the image, then noise is minimized, but information about linear portions may be lost
Solution Approach 1:
The patent applies different processing strategies to different regions of the image based on local characteristics. The first image processing part focuses on noise reduction while the second image processing part focuses on edge and linear feature preservation. The weighted combination then merges these locally optimized results, ensuring that noise is reduced in smooth regions while linear portion information is preserved in regions containing such features.
Solution Approach 2:
The patent introduces an intermediary weighted combination step that mediates between the results of first image processing (noise-reduced but potentially feature-attenuated) and second image processing (feature-preserved but potentially noise-containing). By using adaptive weighting based on local image properties, this intermediary step recovers linear portion information that may have been lost in the first processing while maintaining the noise reduction benefits.
3Reliability
If multiple image processing methods are applied to preserve linear features, then linear portions are better preserved, but processing complexity increases
Solution Approach 1:
The patent segments the complex processing task into three manageable modules: first image processing (noise reduction), second image processing (edge/feature preservation), and weighted combination (adaptive merging). Each module has a clear, simple function that can be implemented efficiently. The segmentation allows the system to achieve reliable linear feature preservation through the coordinated action of simple, well-defined processing steps rather than a single complex algorithm.
Data Source
AI summary
An image processing device includes first image processing part, and first image processing part includes first pixel group setting part that sets plurality of first pixel groups which is set to correspond to first pixel, and which is disposed along in plurality of directions that form plurality of angles different from each other with respect to predetermined direction in first image or which is disposed along in predetermined direction in each of plurality of images obtained by moving first image by plurality of angles different from each other with respect to predetermined direction, first calculation part configured to calculate plurality of first candidate pixel values based on size of pixel value of pixel included in each of plurality of first pixel groups, and first pixel value setting part that sets pixel value of second pixel of second image based on plurality of first candidate pixel values.