In this thesis, we proposed two novel methods for blastomere extraction and trophectoderm segmentation in an attempt to aid physicians in determining embryo’s viability. Accurate assessment of embryo’s viability can play a vital role towards optimization of in-vitro fertilisation (IVF) treatment outcomes. The first proposed automatic method is developed to identify blastomeres in human embryo HMC (Hoffman Modulation Contrast) images of day-1 to day-2. Our algorithm applies isoperimetric graph partitioning, followed by a novel region merging algorithm to approximate blastomeres positions. Ellipsoidal models are then used to approximate the shape and the size of each blastomere. The proposed algorithm is evaluated on a dataset of 40 embryo images and it exhibits an average blastomere extraction accuracy of 80%. The second method segments Trophectoderm (TE) regions in embryos of day-5 (also known as blastocysts) by first eliminating the inhomogeneities of the blastocysts surface using Retinex theory. A level set algorithm is then used to segment TE regions. We have tested our method on a dataset of 85 images and have achieved a segmentation accuracy of 85% for grade A, 89% for grade B and 92% for grade C.
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Thesis advisor: Saeedi, Parvaneh
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