An Analysis System for DNA Gel Electrophoresis Images Based on Automatic Thresholding an Enhancement
This work addresses a domain-specific problem for biologists and researchers by providing an incremental improvement in automating image analysis for DNA gel electrophoresis.
The authors tackled the problem of automating DNA gel electrophoresis image analysis to overcome manual bottlenecks and reproducibility issues, resulting in a fully automated system that eliminates noise defects in average quality images and improves poor quality ones.
Gel electrophoresis, a widely used technique to separate DNA according to their size and weight, generates images that can be analyzed automatically. Manual or semiautomatic image processing presents a bottleneck for further development and leads to reproducibility issues. In this paper, we present a fully automated system with high accuracy for analyzing DNA and proteins. The proposed algorithm consists of four main steps: automatic thresholding, shifting, filtering, and data processing. Automatic thresholding, used to equalize the gray values of the gel electrophoresis image background, is one of the novel operations in this algorithm. Enhancement is also used to improve poor quality images that have faint DNA bands. Experimental results show that the proposed technique eliminates defects due to noise for average quality gel electrophoresis images, while it also improves the quality of poor images.