Classification Based on LBP and SVM for Human Embryo Microscope Images
Embryo transfer is an extremely important step in the process of in- vitro fertilization and embryo transfer (IVF-ET). The identification of the embryo with the greatest potential for producing a child is a very big challenge faced by embryologists. Most current scoring systems of assessing embryo viability are based on doctors’ subjective visual analysis of the embryos’ morphological features. So it provides only a very rough guide to potential. A classifier as a computer-aided method which is based on Pattern Recognition can help to automatically and accurately select embryos. This paper presents a classifier based on the support vector machine (SVM) algorithm. Key characteristics are formulated by using the local binary pattern (LBP) algorithm, which can eliminate the inter-observer variation, thus adding objectivity to the selection process. The experiment is done with 185 embryo images, including 47 “good” and 138 “bad” embryo images. The result shows our proposed method is robust and accurate, and the accurate rate of classification can reach about 80.42%.
Keywordsembryo microscope images feature extraction automatic classifier local vector pattern support vector machine
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