Application of Support Vector Machine to Recognize Trans-differentiated Neural Progenitor Cells for Bright-Field Microscopy

One possible solution of the investigation of the cell fate decision and its function is the study of cell morphology. Bright-field imaging analysis allow us to use a labeling free and non-invasive approach to measure the morphological dynamics during cellular reprogramming, which includes induced pluripotent stem cells (iPSCs), and trans-differentiated neural progenitor cells (NPCs) from somatic cell source. In order to automatically analyze and cultivate cells, a system classifying NPCs under bright-field microscopic imaging is necessary. In this paper, we investigate the use of support vector machine (SVM) based on a set of features for this task. The results illustrate that such a data driven approach has remarkable recognition and generalization performance.