Automatic Classification of Human Embryo Microscope Images Based on LBP Feature

Liang Xu, Xuefeng Wei, Ya-bo Yin, Weizhou Wang, Yun Tian, Mingquan Zhou

Abstract: It is significant in-vitro fertilization (IVF) to automatically evaluate the implantation potential for embryos with a computer. In this essay, an automatic classification algorithm based on local binary pattern (LBP) feature and the support vector machine (SVM) algorithm is presented to classify the embryo images which will suggest whether the image is suitable for the implantation. The LBP operator is first time to be used to extract the texture feature of embryo images, and it is verified that the feature has the capacity of making two types of images linearly separable. Furthermore, a classifier based on the SVM algorithm is designed to determine the best projection direction for classify embryo images in the LBP feature space. Experiments were made with 6-fold cross validation over 185 images, and the result demonstrates that the proposed algorithm is capable of automatically classifying the embryo images with accuracy and efficiency.