An supervised learning method for overlapping cells
Pengfei Shen, Jie Yang · Advances in computer science research · 2015
The clustering phenomenon often appears in histopathology image, some cells overlap or touch together to from a big area.It is necessary to design an effective algorithm to separate the clustering cells into single one.We describe a generic method for segmentation microscopy images based on supervised modeling.The main idea is to use the example input segmentations to learn a statistical model of the shape and texture of the structures to be segmented.The segmentation of the test image can be functioned by maximizing the normalized cross correlation between the model and neighborhoods in the test image, accompanied by a final adjustment that utilizes nonrigid registration.This method can effectively and efficiently solve the overlapping and over-segmentation problem.