Flotation froth image segmentation based on an improved watershed algorithm with highlight overlap correction and multiple edge constraints

Accurate segmentation of froth images is crucial for online monitoring of flotation performance. However, uneven illumination, specular highlight overlap and low-contrast edges frequently cause over-segmentation and under-segmentation when classical watershed transform is applied. In this study, an improved watershed algorithm incorporates specular highlight overlap correction and multi-directional edge constraints. First, Intuitionistic Fuzzy C-means (IFCM), adaptive thresholding and open-close morphological reconstruction are combined to extract and classify highlights into small, medium and large bubbles. An overlap-correction fusion strategy is then designed to remove spurious markers. Second, horizontal–vertical edges extracted by Laplacian of Gaussian (LoG) and ±45° edges obtained by diagonal gradient filters are merged to generate a complete constraint line. Finally, the fused markers and the edge-preserving constraint line are fed into a marker-controlled watershed, achieving robust segmentation of multi-scale froth images. Experiments on 50 on-site images from a tungsten–molybdenum flotation plant show that the proposed method yields mean Adjusted Rand Index (ARI) values of 81.85 %, 87.63 % and 91.63 % for small, medium and large bubbles, respectively, outperforming two state-of-the-art watershed variants.