Multi-Pass Adaptive Voting for Nuclei Detection in Histopathological Images.

ABSTRACT: Nuclei detection is often a critical initial step in the development of computer aided diagnosis and prognosis schemes in the context of digital pathology images. While over the last few years, a number of nuclei detection methods have been proposed, most of these approaches make idealistic assumptions about the staining quality of the tissue. In this paper, we present a new Multi-Pass Adaptive Voting (MPAV) for nuclei detection which is specifically geared towards images with poor quality staining and noise on account of tissue preparation artifacts. The MPAV utilizes the symmetric property of nuclear boundary and adaptively selects gradient from edge fragments to perform voting for a potential nucleus location. The MPAV was evaluated in three cohorts with different staining methods: Hema

SUBMITTER: Lu C 

PROVIDER: S-EPMC5046183 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature