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Publications in Scientific Journals:

K. Riha, M. Zukal, F. Hlawatsch:
"Analysis of carotid artery transverse sections in long ultrasound video sequences";
Ultrasound in Medicine and Biology, 44 (2018), 1; 153 - 167.



English abstract:
Examination of the common carotid artery (CCA) based on an ultrasound video sequence is an effective method for detecting cardiovascular diseases. Here, we propose a video processing method for the automated geometric analysis of CCA transverse sections. By explicitly compensating the parasitic phenomena of global movement and feature drift, our method enables a reliable and accurate estimation of the movement of the arterial wall based on ultrasound sequences of arbitrary length and in situations where state-of-the-art methods fail or are very inaccurate. The method uses a modified Viola-Jones detector and the Hough transform to localize the artery in the image. Then it identifies dominant scatterers, also known as interest points (IPs), whose positions are tracked by means of the pyramidal Lucas-Kanade method. Robustness to global movement and feature drift is achieved by a detection of global movement and subsequent IP re-initialization, as well as an adaptive removal and addition of IPs. The performance of the proposed method is evaluated using simulated and real ultrasound video sequences. Using the Harris detector for IP detection, we obtained an overall root-mean-square error, averaged over all the simulated sequences, of 2.16 1.18 px. The computational complexity of our method is compatible with real-time operation; the runtime is about 30-70 ms/frame for sequences with a spatial resolution of up to 490 490 px. We expect that in future clinical practice, our method will be instrumental for non-invasive early-stage diagnosis of atherosclerosis and other cardiovascular diseases.

Keywords:
Artery, Ultrasound, Image processing, Video processing, Optical flow, Tracking, Interest point, Viola-Jones detector, Hough transform, Lucas-Kanade method


"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
http://dx.doi.org/10.1016/j.ultrasmedbio.2017.08.933

Electronic version of the publication:
https://publik.tuwien.ac.at/files/publik_277341.pdf


Created from the Publication Database of the Vienna University of Technology.