Abstract:
This paper focuses on induced pluripotent stem cell (iPSC) colony image classification using machine learning methods and different feature sets obtained from the intensi...Show MoreMetadata
Abstract:
This paper focuses on induced pluripotent stem cell (iPSC) colony image classification using machine learning methods and different feature sets obtained from the intensity histograms. Intensity histograms are obtained from the whole iPSC colony images and as a baseline for it they are determined only from the iPSC colony area of images. Furthermore, we apply to both of the datasets two simple feature selection methods having altogether four datasets. Altogether, 30 different classification methods are tested and we perform thorough experimental tests. The best accuracy (55%) is obtained for the feature set evaluated from the whole image using Directed Acyclic Graph Support Vector Machines (DAGSVM). DAGSVM is also the best choice when intensity histograms are evaluated only from the iPSC colony area. By this means accuracy of 54% is achieved. The obtained results are promising for further research where, for instance, more sophisticated feature selection and extraction methods and other multi-class extensions of SVM will be examined. However, intensity histograms are not alone adequate for iPSC colony image classification.
Date of Conference: 05-08 October 2014
Date Added to IEEE Xplore: 04 December 2014
Electronic ISBN:978-1-4799-3840-7
Print ISSN: 1062-922X