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Determining the meaningful texture features for human embryonic stem cells (hESC) is important in the development of online hESC classification system. This paper proposes the use of novel support vector machine with bio-inspired one-against-all (OAA) multi-class structural and statistical Gabor descriptors for hESC classification. It investigates the statistical histogram information at four different...
In this paper, we propose an automatic method to detect human embryonic stem cell regions. The proposed method utilizes the K-means algorithm with weighted entropy. As in phase contrast images the cell regions have high intensity variation, they usually yield higher entropy values than the substrate regions which have less intensity variation. Thus, the entropy can be used as an important feature...
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