Speaker
Description
Image exploration and quality control (QC) are essential first steps in any bioimage analysis task. Traditionally, researchers manually inspect randomly sampled images, examine metadata, and extract image features to investigate the data. This process ensures a deeper understanding of the image data and allows for informed algorithm development. However, it often requires multiple open-source tools and/or custom code.
Here we propose a novel Python application designed to streamline image exploration and quality control in bioimage analysis. Our application supports multiple image formats, extracts metadata to ensure consistency, and performs comprehensive feature extraction. By incorporating anomaly detection, it identifies issues such as focussing, illumination artifacts, chromatic aberration, over/under exposure, and dynamic range utilization. Additionally, our tool integrates a lightweight n-dimensional image viewer for efficient visualization of images.
By automating the initial QC steps, our application reduces the need for extensive manual inspection, facilitating more structured and comprehensive image analysis which ultimately enhances the accuracy and reproducibility of experimental results in bioimage analysis.
| Authors | Ranit Karmakar, Federico M. Gasparoli, Simon F. Nørrelykke |
|---|---|
| Keywords | bioimage analysis, image exploration, quality control, Python application, metadata extraction, feature extraction, anomaly detection, open-source |
