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Asst. Prof. Dr. Dheya Ahmed Ibrahim Dhahi
Position
Officer of the Quality Assurance and University Friends Division
Position
Officer of the Quality Assurance and University Friends Division
General Specialization
Computer Science
General Specialization
Computer Science
Subspecialty
Artificial Intelligence And Image Processing
Subspecialty
Artificial Intelligence And Image Processing
Education
- Earned a Ph.D. in Computer Science – Image Processing and Artificial Intelligence in 2018 from University of Buckingham, UK
- Earned his Master's Degree in Computer Science - Image Processing in 2012 from Anbar University, Iraq
- Earned his Bachelor's Degree in Computer Science in 2009 from Anbar University in Iraq
Experience
• Worked at AKELEY WOOD SCHOOL- Buckingham-UK
• I worked as an assistant in private laboratories at the School of Computing, University of Buckingham, UK.
• Teaching at Degla University College – from 10/9/2012 to 1/8/2014
• I worked as a department rapporteur in the Communications Technology Engineering Department at Imam Jaafar Al-Sadiq University.
• I worked as Director of the Quality Assurance Department at Imam Jaafar Al-Sadiq University, peace be upon him, for three years
• I worked as an assistant in private laboratories at the School of Computing, University of Buckingham, UK.
• Teaching at Degla University College – from 10/9/2012 to 1/8/2014
• I worked as a department rapporteur in the Communications Technology Engineering Department at Imam Jaafar Al-Sadiq University.
• I worked as Director of the Quality Assurance Department at Imam Jaafar Al-Sadiq University, peace be upon him, for three years
Publications
Automatic segmentation and measurements of gestational sac using static B-mode ultrasound images, Proceedings of the SPIE,2016
Using trainable segmentation and watershed transform for identifying unilocular and multilocular cysts from ultrasound images of ovarian tumour, SPIE,2017
Multi-level Trainable Segmentation for Measuring Gestational and Yolk Sacs from Ultrasound Images, springer, 2017
Using trainable segmentation and watershed transform for identifying unilocular and multilocular cysts from ultrasound images of ovarian tumour, SPIE,2017
Multi-level Trainable Segmentation for Measuring Gestational and Yolk Sacs from Ultrasound Images, springer, 2017
