Open Access

ARTICLE

Computation of Aortic Geometry Using MR and CT 3D Images

Maryam Altalhi¹, Sami Ur Rehman², Fakhre Alam², Ala Abdulsalam Alarood³, Amin ur Rehman², M. Irfan Uddin^4,*

1 Department of Management Information System, College of Business Administration, Taif Univesity, P.O. Box 11099, Taif, 21944, Saudi Arabia
2 Department of Computer Science and Information Technology, University of Malakand, Chakdara, Dir, Pakistan
3 College of Computer Science and Engineering, University of Jeddah, 20959, Jeddah, Saudi Arabia
4 Institute of Computing, Kohat University of Science and Technology, Kohat, 26000, Pakistan

* Corresponding Author: M. Irfan Uddin. Email:

Intelligent Automation & Soft Computing 2022, 31(2), 961-969. https://doi.org/10.32604/iasc.2022.020607

Received 29 May 2021; Accepted 30 June 2021; Issue published 22 September 2021

Abstract

The proper computation of geometric parameters of the aorta and coronary arteries are very important for surgery planning, disease diagnoses, and age-related changes observation in the vessels. The accurate knowledge about the geometry of aorta and coronary arteries is required for the proper investigation of heart related diseases. The geometry of aorta and coronary arteries includes the diameter of the ascending and descending aorta and coronary arteries, length of the coronary arteries, branching angles of the coronary arteries and branching points. These geometric parameters from arteries can be computed from the 3D image data. In this paper, we propose an approach for calculating geometric parameters such as length, diameter of the aorta and angles of the coronary arteries. The proposed method automatically computes the geometry of aorta and left and right coronary arteries. The geometry is computed by logically dividing the aorta, calculating the centerline and extracting the features of aorta and coronary arteries. The method has been tested on different 3D CT/MR image data. The results of the proposed method are tested on different data sets to check its accuracy. The results show more accuracy and less computation time on noisy image data as compared to the already developed method. The obtained results are visualized and compared using visualization toolkit (VTK).

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Keywords

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