Segmentation of Respiratory Bubbles in Underwater Diver Image Using Pixel Coordinate Information and K-Means Clustering

In underwater diving monitoring, camera sensors capture respiratory bubbles, which are useful for tracking breathing cycles, assessing breath volume, and identifying anomalies. However, segmenting these dynamic and irregular bubbles is challenging. Supervised deep learning offers high accuracy but demands significant training data. To overcome this, we used unsupervised K-means clustering, combining RGB color space with HSV color space images with relative coordinates to extract bubbles from diver images. Relative coordinates address spatial issues in clustering. We applied Contrast Limited Adaptive Histogram Equalization to balance color channels affected by the underwater environment. This validated our ability to segment respiratory bubbles by suppressing seabed reflections using cluster-based ensembles. We evaluate the result of the algorithm with the mean IoU score. And we also compared the impact of pre-processing on the result.