A CLUSTERING-BASED FUSION SYSTEM FOR BLASTOMERE LOCALIZATION

Microscopic digital image processing paves the way for study and evaluation of blastomere identification and localization as a preprocessing step for the embryos selection for the In VitroFertilization (IVF) transfer. Computer vision aims at developing automated image system to localize and grade blastomeres before injection. In this paper, we propose a clustering-based system that supports the localization and counting of blastomeres. The dataset, employed in this study, is formed of 50 Images collected at Assisted Reproduction Technology (ART) Unit, International Islamic Center for Population Studies and Research, Al-Azhar University, Egypt. The proposed system is formed of 2 modules named preprocessing and segmentation modules, where different algorithms were investigated for each module. The preprocessing module includes Image denoising and enhancement tasks. Whereas the edge enhancement investigates the performance of Ostu’s thresholding, Canny and Sobel edge detection techniques, while employing Circular Hough Transform (CHT) for the segmentation task. A fusion-based algorithm was then employed to merge the segmented Blastomeres of the previously defined systems to boost the performance through integrated blastomeres, as well the confidence in localization. The fusion-based algorithm showed very promising results reaching an average Precision, sensitivity, and Overall Quality of 87.9%, 92.9%, and 82.3%, respectively.