Segmentation of cell nuclei in heterogeneous microscopy images: a reshapable templates approach.

Histological tissue images typically exhibit very sophisticated spatial color patterns. It is of great clinical importance to extract qualitative and quantitative information from these images. As an ad hoc solution, various unsupervised approaches address the object detection and segmentation problem which are suitable for limited classes of histology images. In this paper, we propose a general purpose localization and segmentation method which utilizes reshapable templates. The method combines both pixel- and object-level features for detecting regions of interest. Segmentation is carried out in two levels including both the coarse and fine ones. A set of simple-shaped templates is used for coarse segmentation. A content based template reshaping algorithm is proposed for fine segmentation of target objects. Experimentation was done using a publicly available image data set which contains 7931 manually labeled cells of heterogeneous histology images. The experiments have demonstrated acceptable level of detection and segmentation results for the proposed approach (precision=0.904, recall=0.870 and Zijdenbos similarity index=73%). Thus, the prototype software developed based on proposed method can be considered as a potential tool for pathologists in clinical process.