Development of Blood Vessel Segmentation Methods for Clinical Use - Bukenya, Faiza (2023)
Blood vessel segmentation is important for diagnosing disease, monitoring treatment response and for treatment planning. However, the performance of the available segmentation techniques is limited by the image artefacts (such as low contrast variation between blood vessel pixels and background, noise, and stain variation) in the medical images and therefore, the available automated segmentation techniques fail to distinguish between blood vessels from non-blood vessels. In addition, the existing segmentation techniques have a challenge of segmenting blood vessels of various sizes especially small vessels, and therefore they cannot be used during diagnosis of small blood vessel diseases (such as stroke and Alzheimer's disease) and cannot be used in medical applications that require monitoring the blood vessel growth. For a more accurate diagnosis, an automated blood vessel segmentation method should segment blood vessel of various sizes in the image. This thesis aims to develop both 3D and 2D hybrid blood vessel segmentation techniques that can overcome the challenges associated with the available methods, coupled with a high accuracy rate when applied to medical images.