A two-stage adaptive thresholding segmentation for noisy low-contrast images

Image recognition is the process of recognizing and classifying objects with machine learning algorithms. Image binarization is the first and most challenging step in image recognition, in which foreground objects are separated from their background. When foreground objects have complex morphological structure and background noise is strong, foreground objects are often being fractured into subcomponents. To address the over-segmentation issue of organisms with complex structures, we propose a 2-stage adaptive binarization approach based on Sauvola's binarization algorithm. We tested the effectiveness of the new approach on a set of underwater images with jellyfish collected in nearshore waters using a shadowgraph underwater plankton imaging system, PlanktonScope, because jellyfish have relatively complex structure and are often over-segemented. The results showed that the 2-stage approach improved the integrity of extracted jellyfish compared to traditional binarization methods, including Sauvola's algorithm. The analysis of local entropy values showed that the first stage effectively suppresses redundant information in the image and reduces the number of Region of Interests (ROIs), and the second stage preserves relatively weak and low-intensity signals to ensure the integrity of the extracted targets. The 2-stage approach improves hardware resource utilization and computational efficiency. It is robust for images acquired in sub-optimal conditions and enhances the accuracy of analytical results in the study of marine organisms using imaging systems.