OBJECTIVE: This study aimed to propose an intensity-vesselness
Gaussian mixture model (IVGMM) tracking for 2D + t segmentation of coronary
arteries for X-ray angiography (XA) image sequences.
METHODS: We compose a two dimensional (2D) feature vector of
intensity and vesselness to characterize the Gaussian mixture models. In our
IVGMM tracking, vessel segmentation is performed for each image frame based
on these vessel and background IVGMMs and then the segmentation results of
the current image frame is used to update these IVGMMs. The 2D + t
segmentation of coronary arteries over the 2D XA image sequence is solved by
means of iterating two processes, i.e., segmentation of coronary arteries and
update of the IVGMMs.
RESULTS: The performance of the proposed IVGMM tracking was
evaluated using clinical 2D XA datasets. We evaluated the segmentation
accuracy of the IVGMM tracking by comparing with two previous 2D vessel
segmentation methods and seven background subtraction (BGS) methods. Of the
ten segmentation methods, IVGMM tracking shows the highest similarity to the
manual segmentation in terms of precision, recall, Jaccard index (JI), F1
score, and peak signal-to-noise ratio (PSNR).
CONCLUSIONS: It is concluded that the IVGMM tracking could
obtain reasonable segmentation accuracy outperforming conventional vessel
enhancement methods and object tracking methods.
