Learning edge-defining thresholds for local binary segmentation
Leiguang Gong, Casimir A. Kulikowski, Reuben Saul Mezrich · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
Selecting a globally effective threshold to define edges for local binary thresholding and segmentation of images presents major problems given the significant variability in intensity and edge statistics from image to image and study to study. Previously reported results of applying binary local thresholding have depended on the careful empirical choice of a threshold range adapted to a particular class of images. We have developed two new systematic methods that learn the edge-defining threshold from the gradient image generated by applying a gradient operator. The first method minimizes a criterion function, and the second takes advantage of local constancy properties of the intensity threshold as a function of a selected edge-defining threshold. An edge-defining threshold is then obtained for each sub- image, and a global threshold derived from them for the whole image. Experiments with MR images from phantoms and various human and animal studies have shown the effectiveness of this approach.