Interactive exploration of microstructural features in gigapixel microscopy images

Modern imaging technologies enable the study of microstructural features, which require capturing the finest details in high-resolution gigapixel images. Nevertheless, the resolution disparity between gigapixel images and megapixel displays presents a challenge to effective visual analysis because subtle texture differences are hardly perceivable at coarser resolutions. In this paper we present a hierarchical segmentation technique based on joint distribution of intensity and noise-resistant local binary patterns to differentiate subtle microstructural textures across various scales.
The coarse-to-fine segmentation procedure subdivides each parent segment into texturally-distinct child segments at progressively higher resolutions. The hierarchical structure of segments allows creating intermediate segmentation results interactively. Based on the intermediate results, we highlight regions with texture differences using distinct colors, which provide salient visual hints to users despite of the current viewing resolution. Our new technique has been validated on large microscopy images and shows promising results.