Morphology based cell classification : unsupervised machine learning approach

Individual cells adapt their morphology as a function of their differentiation status and in response to environmental cues and selective pressures. While it known that the great majority of these cues and pressures are mediated by changes in intracellular signal transduction, the precise regulatory mechanisms that govern cell shape, size and polarity are not well understood. Systematic investigation of cell morphology involves experimentally perturbing biochemical pathways and observing changes in phenotype. In order to facilitate this work, experimental biologists need software capable of analyzing a large number of microscopic images to classify cells and recognize cell types. Furthermore, automatic cell classification enables pathologists to rapidly diagnose diseases like leukemia that are marked by cell shape deformation. This thesis describes a methodology to identify cells in microscopy images and compute quantitative descriptors that characterize their morphology. Phase-contrast microscopy data is used for the purpose of demonstration. Cells are identified with minimal user input using advanced image segmentation methods. Features (e.g. area, perimeter, curvature, circularity, convexity, etc.) are extracted from segmented cell boundary to quantify cell morphology. Correlated features are combined to reduce dimensionality and the resulting feature set is clustered to identify distinct cell morphologies. Clustering results obtained from different combinations of features are compared to identify a minimal set of features without compromising classification accuracy.Science, Faculty ofMathematics, Department ofGraduat