Fuzzy c-means segmentation of major vessels in angiographic images of stroke

J Med Imaging (Bellingham). 2018 Jan;5(1):014501. doi: 10.1117/1.JMI.5.1.014501. Epub 2018 Jan 4.


Patients suffering from ischemic stroke develop varying degrees of pial arterial supply (PAS), which can affect patient response to reperfusion therapy and risk of hemorrhage. Since vessel segmentation may be an important part in identifying PAS, we present a fuzzy c-means (FCM) clustering method to segment major vessels in x-ray angiograms. Our approach consists of semiautomatic region of interest (ROI) delineation, separation of major vessels from capillary blush and/or background noise through FCM clustering, and identification of the major vessel category. This method was applied to a database of x-ray angiograms of 24 patients acquired at various frame rates. The ground truth for performance evaluation was the designation by an expert radiologist selecting image pixels as being vessel or nonvessel. From receiver operating characteristic (ROC) analysis, area under the ROC curve (AUC) was the performance metric in the task of distinguishing between major vessels and blush or background. When clustering data into three categories and performing FCM segmentation on each ROI separately, the AUC was 0.89 for the entire database and [Formula: see text] for all examined frame-rates. In conclusion, our method showed promising performance in identifying major vessels and is anticipated to become an integral part of automatic quantification of PAS.

Keywords: fuzzy c-means; quantitative image analysis; stroke; vessel segmentation; x-ray digital subtraction angiography.