Vessel enhancement in digital X-ray angiographic sequences by temporal statistical learning.
In this paper, we present a vessel enhancement method, SVM temporal filtering (STF), for X-ray angiographic (XA) images using Support Vector Machine (SVM). We show that the linear SVM applied to vessel enhancement can be regarded as a matched linear filter optimizing the contrast-to-noise ratio in XA images. We propose a non-linear kernel function for the SVM leading to good enhancement with noisy, varying grey-level dynamics at vessel pixels. One key advantage over the matched filters is that an optimal filter is learnt from images, not estimated at design stage. Results on clinical XA images show that learning-based enhancement achieves better results compared to simple subtraction and other image stacking methods.
Lassó
András
A
Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Magyar tudósok körútja 2, Budapest 1117, Hungary. lasso@topcat.iit.bme.hu
Trucco
Emanuele
E
D000465:Algorithms; D000792:Angiography / Q000379:methods*; D001185:Artificial Intelligence; D006801:Humans; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D011856:Radiographic Image Enhancement / Q000379:methods*