Investigating local spatially-enhanced structural and textural descriptors for classification of iPSC colony images.

Induced pluripotent stem cells (iPSC) can be derived from fully differentiated cells of adult individuals and used to obtain any other cell type of the human body. This implies numerous prospective applications of iPSCs in regenerative medicine and drug development. In order to obtain valid cell culture, a quality control process must be applied to identify and discard abnormal iPSC colonies. Computer vision systems that analyze visual characteristics of iPSC colony health can be especially useful in automating and improving the quality control process. In this paper, we present an ongoing research that aims at the development of local spatially-enhanced descriptors for classification of iPSC colony images. For this, local oriented edges and local binary patterns are extracted from the detected colony regions and used to represent structural and textural properties of the colonies, respectively. We preliminary tested the proposed descriptors in classifying iPSCs colonies according to the degree of colony abnormality. The tests showed promising results for both, detection of iPSC colony borders and colony classification.