Analysis of Carotid Artery Transverse Sections in Long Ultrasound Video Sequences

Analysis of Carotid Artery Transverse Sections in Long Ultrasound Video Sequences

WoS Article

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.

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.

Atery; Ultrasound; Image processing; Video processing; Optical flow; Tracking; Interest point; Viola– Jones detector; Hough transform; Lucas–Kanade method

Atery; Ultrasound; Image processing; Video processing; Optical flow; Tracking; Interest point; Viola– Jones detector; Hough transform; Lucas–Kanade method

ŘÍHA, K.; ZUKAL, M.; HLAWATSCH, F.

2019

03.01.2018

ELSEVIER SCIENCE INC

0301-5629

ULTRASOUND IN MEDICINE AND BIOLOGY

44

1

United Kingdom of Great Britain and Northern Ireland

153

167

15

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