<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In this paper the authors raise the issue of automatic discrimination of atherosclerotic plaques within an artery lumen based on numerical and statistical thresholding of Computerized Tomography Angiographic (CTA) images and their advanced dimensioning as a support for preoperative vessel assessment. For the study, a set of tomograms of the aorta, as well as the ilio-femoral and femoral arteries were examined. In each case a sequence of about 130-480 images of the artery cutoff planes were analyzed prior to their segmentation based on morphological image transformation. A crucial step in the staging of atherosclerotic alteration is recognition of the plaque in the CTA image. To solve this problem, statistical and linear fitting methods, including the least-squares approximation by polynomial and spline polynomial functions, as well as the error fitting function were used. Also, new descriptors of atherosclerotic changes, such as the lumen decrease factor, the circumference occupancy factor, and the convex plaque area factor, are proposed as a means of facilitating preoperative vessel examination. Finally, ways to reduce the computational time are discussed. The proposed methods can be very useful for automatic quantification of atherosclerotic changes visualized by CTA imaging.</p></abstract>

In this paper the authors raise the issue of automatic discrimination of atherosclerotic plaques within an artery lumen based on numerical and statistical thresholding of Computerized Tomography Angiographic (CTA) images and their advanced dimensioning as a support for preoperative vessel assessment. For the study, a set of tomograms of the aorta, as well as the ilio-femoral and femoral arteries were examined. In each case a sequence of about 130-480 images of the artery cutoff planes were analyzed prior to their segmentation based on morphological image transformation. A crucial step in the staging of atherosclerotic alteration is recognition of the plaque in the CTA image. To solve this problem, statistical and linear fitting methods, including the least-squares approximation by polynomial and spline polynomial functions, as well as the error fitting function were used. Also, new descriptors of atherosclerotic changes, such as the lumen decrease factor, the circumference occupancy factor, and the convex plaque area factor, are proposed as a means of facilitating preoperative vessel examination. Finally, ways to reduce the computational time are discussed. The proposed methods can be very useful for automatic quantification of atherosclerotic changes visualized by CTA imaging.

Recognition of atherosclerotic plaques and their extended dimensioning with computerized tomography angiography imaging

Institute of Theory of Electrical Engineering, Measurement and Information Systems Warsaw University of Technology, Politechniki 1, 00-661 Warsaw, Poland

Department of Pathomorphology Military Institute of Medicine, Szaserów 128, 04-141 Warsaw, Poland

Department of Vascular and Endovascular Surgery Military Institute of Medicine, Szaserów 128, 04-141 Warsaw, Poland

Department of Radiology Military Institute of Medicine, Szaserów 128, 04-141 Warsaw, Poland

International Journal of Applied Mathematics and Computer Science

In this paper the authors raise the issue of automatic discrimination of atherosclerotic plaques within an artery lumen based on numerical and statistical...

Recognition of atherosclerotic plaques and their extended dimensioning with computerized tomography angiography imaging

Online veröffentlicht: 25. März 2014

Seitenbereich: 33 - 47

DOI: https://doi.org/10.2478/amcs-2014-0003

Schlüsselwörter
computed tomography, atherosclerotic plaque, image processing, approximation.

This content is open access.

Recognition of atherosclerotic plaques and their extended dimensioning with computerized tomography angiography imaging

Online veröffentlicht: 25. März 2014

Seitenbereich: 33 - 47

DOI: https://doi.org/10.2478/amcs-2014-0003

Schlüsselwörtercomputed tomography, atherosclerotic plaque, image processing, approximation.

This content is open access.

Schlüsselwörter
computed tomography, atherosclerotic plaque, image processing, approximation.