Abstract
BackgroundAutomatic vascular segmentation in X-ray angiographic image sequence is of crucial interest, for instance, for better quantifying coronary arteries in diagnostic and interventional procedures.MethodsA novel inter/intra-frame constrained vascular segmentation method is proposed to automatically segment vessels in coronary X-ray angiographic image sequence. First, a morphological filter operator is applied to remove structures undergoing the respiratory motion from the original image sequence. Second, an inter-frame constrained robust principal component analysis (RPCA) is utilized to remove the quasi-static structures from the image sequence. Third, an intra-frame constrained RPCA is employed to smooth the final extracted vascular sequence. Fourth, a multi-feature fusion is designed to improve the vascular contrast and the final vascular segmentation is realized by thresholding-based method.ResultsExperiments are conducted on 22 clinical X-ray angiographic image sequences. The global and local contrast-to-noise ratio of the proposed method are 6.6344 and 4.2882, respectively. And the precision, sensitivity and F1 value are 0.7378, 0.7960 and 0.7658, respectively. It demonstrates that our method is effective and robust for vascular segmentation from image sequence.ConclusionsThe proposed method is effective to remove non-vascular structures, reduce motion artefacts and other non-uniform illumination caused noises. Also, the proposed method is online which can just process one image per time without re-optimizing the model.
Highlights
Automatic vascular segmentation in X-ray angiographic image sequence is of crucial interest, for instance, for better quantifying coronary arteries in diagnostic and interventional procedures
Since X-ray angiography (XRA) has better imaging quality and faster imaging speed, it is regarded as the gold standard for the diagnosis and treatment of coronary artery disease (CAD)
To remove more motion artefacts and noises, we introduce the intra-frame constrained robust principal component analysis (RPCA) and denote it according to Eq (2) as follows:
Summary
Automatic vascular segmentation in X-ray angiographic image sequence is of crucial interest, for instance, for better quantifying coronary arteries in diagnostic and interventional procedures. Wang et al [2] utilized the level-set algorithm to segment the coronary artery by constructing the speed function with the curvature, intensity and model term. Sun et al [3] proposed the local region based active contour method by shape fitting the energy function It improved the segmentation accuracy and is much more robust to the nonuniform intensity distribution and fitting initialization. Hassouna et al [6] modeled the background with two Gaussian and a rayleigh distributions and the vessels with a Gaussian distribution, respectively They utilized the ExpectationMaximization algorithm to estimate the distribution parameters and employed the Markov Random Field to be the spatial constraint to realize the final vascular segmentation. Luo et al [12] improved the DeepVessel network by considering the non-uniform intensity and noise coexistence
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