Fig. 2
From: Clustered nuclei splitting based on recurrent distance transform in digital pathology images
The scheme of processing using our algorithm with miniature images for better understanding. Cropped binary input contains one cluster. Next, the distance transform (D) is calculated. Foreground markers (FGM) are established with distance transform thresholded with D_tresh (value based on maximum value present in distance transform map, parameter T, and the recurrence index recur_idx). Background markers (BGM) are obtained by applying watershed algorithm to distance transform. Then, we impose minima (FGM and BGM) on the gradient magnitude of the input image. If the ridge is present in the result after applying watershed algorithm, then it is used to split the objects. If the resulting objects (tested in terms of area, eccentricity, and roundness) are still classified as clusters, then the algorithm starts again with increased recurrence index