Haralick Texture Analysis for Stem Cell Identification
نویسنده
چکیده
The author presents an automatic texture analysis image processing technique for determining the differentiation status of stem cells in time-lapse data. Haralick texture features, and their prerequisite gray-level co-occurrence matrices, were used to quantify texture samples, linear discriminant analysis for dimensionality reduction while preserving class discrimination, and support vector machines for forming decision boundaries. Brute force was utilized for each training session for several parameters. The algorithm was tested on four types of data: randomly generated, synthetic texture samples, phase contrast, and quantitative phase. Overall accuracy rates were found to exceed 85% for all data types encountered. Keywords—Haralick, texture, hESCs, iPSCs, stem cells, differentiation, LDA, SVM, classification, image processing.
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