Image Segmentation Using Deep Learning
| dc.contributor.author | Akbari, Nasrin | |
| dc.contributor.supervisor | Baniasadi, Amirali | |
| dc.contributor.supervisor | Numanagić, Ibrahim | |
| dc.date.accessioned | 2022-09-27T18:04:10Z | |
| dc.date.available | 2022-09-27T18:04:10Z | |
| dc.date.copyright | 2022 | en_US |
| dc.date.issued | 2022-09-27 | |
| dc.degree.department | Department of Electrical and Computer Engineering | en_US |
| dc.degree.level | Master of Applied Science M.A.Sc. | en_US |
| dc.description.abstract | The image segmentation task divides an image into regions of similar pixels based on brightness, color, and texture, in which every pixel in the image is as- signed to a label. Segmentation is vital in numerous medical imaging applications, such as quantifying the size of tissues, the localization of diseases, treatment plan- ning, and surgery guidance. This thesis focuses on two medical image segmentation tasks: retinal vessel segmentation in fundus images and brain segmentation in 3D MRI images. Finally, we introduce LEON, a lightweight neural network for edge detection. The first part of this thesis proposes a lightweight neural network for retinal blood vessel segmentation. Our model achieves cutting-edge outcomes with fewer parameters. We obtained the most outstanding performance results on CHASEDB1 and DRIVE datasets with an F1 measure of 0.8351 and 0.8242, respectively. Our model has few parameters (0.34 million) compared to other networks such as ladder net with 1.5 million parameters and DCU-net with 1 million parameters. The second part of this thesis investigates the association between whole and re- gional volumetric alterations with increasing age in a large group of healthy subjects (n=6739, age range: 30–80). We used a deep learning model for brain segmentation for volumetric analysis to extract quantified whole and regional brain volumes in 95 classes. Segmentation methods are called edge or boundary-based methods based on finding abrupt changes and discontinuities in the intensity value. The third part of the thesis introduces a new Lightweight Edge Detection Network (LEON). The proposed approach is designed to integrate the advantages of the deformable unit and DepthWise Separable convolutions architecture to create a lightweight back- bone employed for efficient feature extraction. Our experiments on BSDS500 and NYUDv2 show that LEON, while requiring only 500000 parameters, outperforms the current lightweight edge detectors without using pre-trained weights. | en_US |
| dc.description.embargo | 2022-10-12 | |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/14272 | |
| dc.language | English | eng |
| dc.language.iso | en | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.subject | Edge Detection | en_US |
| dc.subject | Retinal Vessel Segmentation | en_US |
| dc.subject | Deep Learning | en_US |
| dc.subject | Brain Volume Analysis | en_US |
| dc.title | Image Segmentation Using Deep Learning | en_US |
| dc.type | Thesis | en_US |