Gene-Sequencing Image Compression with Peak and Noise Separation
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Solution Overview
Problem
Gene-sequencing images generated by fluorescence or microarray techniques are excessively large and sparse, making them unsuitable for archiving due to the lack of effective compression methods that can maintain clinically relevant information without significant information loss.
Innovation Solution
A method that separates pixels into information and noise sets based on peak identification and standard deviation, using a noise threshold parameter to classify pixels, allowing for compression and decompression of gene-sequencing images while preserving important sequencing data, achieving a compression ratio of 10 to 20 times with near lossless integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If common compression techniques are applied to gene-sequencing images, then file size is reduced, but clinically relevant information is lost
Solution Approach 1:
The patent segments the image data into three distinct categories: peaks (information-bearing pixels), plateau pixels, and background pixels. This segmentation allows differential processing where only essential peak information is stored, while other regions are reconstructed, achieving compression without losing clinically relevant information.
Solution Approach 2:
The patent changes the representation parameters by storing only peak pixel coordinates and intensity values rather than all pixel data. Additionally, it uses statistical parameters (mean, standard deviation) to model and reconstruct plateau and background regions, transforming the data representation to achieve compression while preserving information.
2Quantity of substance
If direct thresholding is applied to remove noise, then file size is reduced, but spots of lower intensity that might be of clinical relevance are also removed
Solution Approach 1:
The patent applies different processing qualities to different regions: peaks receive precise storage with full intensity information, plateau pixels are reconstructed using statistical models, and background pixels are reconstructed using noise models. This local differentiation ensures clinically relevant spots are preserved while achieving compression.
Solution Approach 2:
Instead of directly thresholding and discarding data, the patent creates statistical copies (models) of the plateau and background regions that can be reconstructed during decompression. This copying approach preserves the ability to recover clinically relevant information while reducing stored data volume.
Data Source
AI summary
A method for compression and/or decompression of bio-medical images such as DNA sequencing images is provided. The method comprises regeneration of a complete image using multiple distribution functions generated on the fly. The compression ratio achieved is in the order of 10 to 20 times, and is near lossless with respect to the clinically relevant information; which makes it well suited for these DNA sequencing images.