Intensity-vesselness Gaussian mixture model (IVGMM) for 2D + t segmentation of coronary arteries for X-ray angiography image sequences

Abstract

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.