Automatic segmentation of the lumen of carotid arteries in ultrasound images is a starting step in providing preventive care for patients with atherosclerosis. To perform the segmentation this paper introduces a model utilizing a threshold algorithm. The model was tested with two different threshold algorithms, Otsu and Sauvola, then scored against professionally drawn masks. The scores were calculated with Dice and Jaccard-Needham as well as specificity, recall, and f1-score. The results showed promising mean and median similarity between the predictions and masks. Future work includes either optimizing the current model or augmenting it to give an even better ground to continue the work on providing preventive care for atherosclerosis patients.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:umu-209703 |
| Date | January 2023 |
| Creators | Kadeby, Alexander |
| Publisher | Umeå universitet, Institutionen för datavetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | UMNAD ; 1390 |
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