A bag-of-words model for cellular image segmentation

Abstract

Cellular segmentation in microscopy images is an important step in modern biological research. Microscopy image segmentation is known to be a difficult problem, as illustrated in the paper, in many scenarios the microscopic images become a real challenge for existing methods to accurately segment these cellular objects of interest. In this paper we propose a learning based approach using a bag-of-words model and dedicated feature design to deal with this problem. By introducing the recent machine learning and computer vision techniques including sparse coding, superpixel representation, our approach is shown to achieve good performance in practice. © 2012 Springer Berlin Heidelberg.