Feature-Adaptive Interactive Thresholding of Large 3D Volumes
This addresses segmentation challenges in volumetric image processing for domain experts, offering an incremental improvement over plain thresholding methods.
The paper tackles the problem of global thresholds failing to properly segment volumetric images with artifacts, noise, or grayscale fluctuations by introducing Feature-Adaptive Interactive Thresholding (FAITH), which adapts thresholds locally based on user-selected seed voxels and geometric features, enabling efficient segmentation of large volumes.
Thresholding is the most widely used segmentation method in volumetric image processing, and its pointwise nature makes it attractive for the fast handling of large three-dimensional samples. However, global thresholds often do not properly extract components in the presence of artifacts, measurement noise or grayscale value fluctuations. This paper introduces Feature-Adaptive Interactive Thresholding (FAITH), a thresholding technique that incorporates (geometric) features, local processing and interactive user input to overcome these limitations. Given a global threshold suitable for most regions, FAITH uses interactively selected seed voxels to identify critical regions in which that threshold will be adapted locally on the basis of features computed from local environments around these voxels. The combination of domain expert knowledge and a rigorous mathematical model thus enables a very exible way of local thresholding with intuitive user interaction. A qualitative analysis shows that the proposed model is able to overcome limitations typically occuring in plain thresholding while staying efficient enough to also allow the segmentation of big volumes.