Prediction of Embryo Selection Using Efficient Otsu Segmentation for in- Vitro Fertilization Techinques

In-Vitro Fertilization (IVF) is an effective way to find out the problem similar to ovulation, poor egg quality, inability of sperm and a genetic disorder of father or mother. The procedure involves in evaluating the embryo from different image datasets for image thresholding technique. The Otsu based approaches is effective to categorize the embryo on the presence of dense layer or cell. Our goal is to build the threshold for embryo segmentation using Otsu’s segmentation method. The proposed method is based on segmentation which evaluates the embryo automatically with the features of number of cells, shapes, and density as white and black layers which is obtained in the embryo. Here, the clinical data is measured through the attributes and the values with different epochs for the performance of Otsu algorithm. The experimental results provide the segmentation performance with 73% of accuracy respectively. The results were validated using the dense connectivity in embryo and provides the stable outcome to improve the efficiency of IVF procedure to success rate.