Clustering based fusion system for blastomere localization /
Shimaa Mohamed Khder Abdelmokhtar
Clustering based fusion system for blastomere localization / نظام دمج المجموعات لتحديد أماكن خلايا الأجنة في الصور الميكروسكوبيه باستخدام أنظمة الدمج Shimaa Mohamed Khder Abdelmokhtar ; Supervised Ayman Eldeib , Inas A. Yassine - Cairo : Shimaa Mohamed Khder Abdelmokhtar , 2019 - 74 P. : charts , facsimiles ; 30cm
Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Systems and Biomedical Engineering
Microscopic digital image processing paves the way to deep study and evaluation of blastomere identification and/or localization to provide specific selection for in vitro fertilization (IVF) transfer. The main purpose of this thesis is to develop an automated image processing system which assist embryologists to study and investigate the behavior of developing pre implanted embryos wishing to improve IVF outcomes. The proposed methods objective is calculate the number of blastomeres based on a clustering based fusion system, where the clustering algorithm enhance the results. Experimental results on a dataset of 50 embryo images with various blastomere numbers and sizes confirm that the proposed method identifies blastomeres with average Precision, sensitivity, and Overall Quality of 87.9%, 92.9%, and 82.3%, respectively
Automatic localization Blastomere Microscopic images
Clustering based fusion system for blastomere localization / نظام دمج المجموعات لتحديد أماكن خلايا الأجنة في الصور الميكروسكوبيه باستخدام أنظمة الدمج Shimaa Mohamed Khder Abdelmokhtar ; Supervised Ayman Eldeib , Inas A. Yassine - Cairo : Shimaa Mohamed Khder Abdelmokhtar , 2019 - 74 P. : charts , facsimiles ; 30cm
Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Systems and Biomedical Engineering
Microscopic digital image processing paves the way to deep study and evaluation of blastomere identification and/or localization to provide specific selection for in vitro fertilization (IVF) transfer. The main purpose of this thesis is to develop an automated image processing system which assist embryologists to study and investigate the behavior of developing pre implanted embryos wishing to improve IVF outcomes. The proposed methods objective is calculate the number of blastomeres based on a clustering based fusion system, where the clustering algorithm enhance the results. Experimental results on a dataset of 50 embryo images with various blastomere numbers and sizes confirm that the proposed method identifies blastomeres with average Precision, sensitivity, and Overall Quality of 87.9%, 92.9%, and 82.3%, respectively
Automatic localization Blastomere Microscopic images