Description

In the earlier cell detection process, the major challenge of robust brain tumor nuclei/cell detection is to handle significant variations in cell appearance and to split touching cells. In this paper, we present an automatic cell detection framework using sparse reconstruction and adaptive dictionary learning. There are two main implementation present here is: 1) A sparse reconstruction based approach to split touching cells. 2) An adaptive dictionary learning method used to handle cell appearance variations.