Nuclear Segmentation of Glioblastoma Multiforme Cells by Multireference Level Set

Abstract

Histological tissue section consists of rich information about cell type, cellular morphology, cell state and health etc. which is very important for clinical diagnosis and therapy. Automated analysis provides insights of tumor subtypes. Since tumor sections are collected from different laboratory, some issues arises called technical and biological variations. In this paper we developed an approach for nuclear segmentation on tumours histological section, which addresses problems of processing tissues at different laboratory under microscope. Eventually, the resolution is formulated in multi reference level set frame. Experimental results show performance of proposed method.