| Title: | Image Resizing Impact on Optic Disc and Optic Cup Segmentation |
| Authors: | Virbukaite, Sandra Bernataviciene, Jolita |
| Citation: | WSCG 2022: full papers proceedings: 30. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 306-309. |
| Issue Date: | 2022 |
| Publisher: | Václav Skala - UNION Agency |
| Document type: | conferenceObject |
| URI: | http://hdl.handle.net/11025/49610 |
| ISBN: | 978-80-86943-33-6 |
| ISSN: | 2464-4617 |
| Keywords: | segmentace optického disku;segmentace optického poháru;konvoluční neuronové sítě |
| Keywords in different language: | optic disc segmentation;optic cup segmentation;convolutional neural networks |
| Abstract in different language: | Optic disc (OD) and Optic Cup (OC) segmentation play an important role in the automatic assessment of eye health where the Convolutional Neural Networks (CNNs) have been extensively employed. The application of CNNs requires identical image size to work properly but the eye fundus images vary due to different datasets. In this paper we evaluate eye fundus image resizing level impact on OD and OC segmentation. For this evaluation we apply the most popular medical images segmentation autoencoder named U-Net. The experiments demonstrate that OD and OC segmentation results are improved averagely by 5.5 percent resizing images to size of 512x512 than 128x128. |
| Rights: | © Václav Skala - UNION Agency |
| Appears in Collections: | WSCG 2022: Full Papers Proceedings |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| C79-full.pdf | Plný text | 1,4 MB | Adobe PDF | View/Open |
Please use this identifier to cite or link to this item:
http://hdl.handle.net/11025/49610Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.