Image segmentation with boundary-to-pixel direction and magnitude based on watershed and attention mechanism.

An improved image segmentation algorithm with boundary-to-pixel direction and magnitude (IS-BPDM) is proposed to deal with small regions segmentation while keeping the accuracy of edge segmentation. First, we develop a BPDM network embedded with watershed and attention module and use an adaptive loss function to learn each pixel's robust and accurate BPDM which is defined as a two-dimensional vector, including direction and magnitude, and pointing from its nearest boundary pixel to itself. Then, we use the learned BPDMs to obtain the refined initial segmented regions by considering the pixels near boundary have shorter magnitude and near root pixels have longer magnitude, meanwhile adjacent pixels in different regions or nearby pixels on both sides of root pixel in same region have opposite directions and nearby pixels in same region have similar directions. Last, we utilize a fast grouping method according to direction similarity to combine these initial segmented regions into final segmentation. The experimental results show that compared with the state-of-the-art segmentation methods, the IS-BPDM approach proposed in this paper achieves better segmentation accuracy and high computational efficiency and outperforms in small regions segmentation on public datasets. [ABSTRACT FROM AUTHOR]