Cell Image Segmentation via Spatial Frequency Analysis
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Solution Overview
Problem
In images of cells cultured in a medium, distinguishing cell images from background and debris becomes challenging as culture progresses, especially when using simple processing methods like binarization or edge detection.
Innovation Solution
An image processing method that segments images based on local spatial frequency components, using two-dimensional discrete cosine transform (DCT) to differentiate between cell, background, and debris by representing blocks with low frequency components for brightness and high frequency components for texture, allowing precise extraction of cell areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simple processing methods like binarization or edge detection are used, then the processing speed is fast and easy to implement, but the accuracy of distinguishing cell images from debris deteriorates
Solution Approach 1:
The patent divides the image into multiple blocks and processes each block independently to extract spatial frequency components. This segmentation allows detailed analysis of local image characteristics without processing the entire image at once, improving accuracy while managing computational complexity through distributed processing.
Solution Approach 2:
The patent transforms image data from the spatial domain to the frequency domain using two-dimensional discrete cosine transform. This dimensional transformation enables distinction between cell images and debris based on spatial frequency characteristics, providing a new perspective for accurate segmentation.
2Adaptability or versatility
If traditional imaging methods are used, then the cell area can be specified with existing techniques, but the ability to observe cell changes over time without modifying the cell deteriorates
Solution Approach 1:
The patent replaces physical cell modification methods (chemical staining, fluorescence tagging) with an image processing-based approach. By using spatial frequency analysis on grayscale images, the system can identify cell boundaries and track cell changes over time without altering the cells, enabling non-invasive long-term observation.
3Measurement precision
If the image is divided into blocks and spatial frequency components are extracted, then the accuracy of cell area extraction is improved, but the processing time increases
Solution Approach 1:
The patent extracts only the necessary spatial frequency components (low frequency and high frequency components) from each block rather than processing all frequency components. This selective extraction maintains sufficient accuracy for cell area identification while reducing computational burden and processing time.
Data Source
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Figure 3A~3C
AI summary
To segment properly a cell area and another area from an original image including a cell and a background, an image processing method according to the present invention includes obtaining an original image including a cultured cell image with a background image (Step S101), dividing the original image into blocks each composed of a predetermined number of pixels and obtaining a spatial frequency component of an image in each block for each block (Step S104-S106), and classifying each block as the one belonging to a cell cluster corresponding to the cell or the one belonging to other than the cell cluster in a two-dimensional feature amount space composed of a first feature amount which is a total of intensities of low frequency components having a frequency equal to or lower than a predetermined frequency and a second feature amount which is a total of intensities of high frequency components having a higher frequency than the low frequency component (Step S107-S108), and segmenting the original image into an area occupied by the blocks classified as the cell cluster and another area (Step S109).