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BY 4.0 license Open Access Published by De Gruyter October 9, 2021

Image processing to delineate the boundaries of peripheral arterial walls

  • Janani Shekar , Saritha Sukumaran , Ashish Bhave and Knut Möller


The analysis of the arterial wall properties is vital in the prediction of stroke events and arterial hypertension in humans. Numerous researchers have experimented with several approaches to model arterial vessels and to analyse their biomechanical behaviour for many years now. Our study is focussed on image processing of peripheral arterial cross sections to detect and isolate the distinct layers. These boundaries will enable the creation of FEM models for further analysis of arterial wall properties. In a clinical setting, it facilitates doctors to identify the optimum pressure that can be applied to the artery for the treatment of stenosis without damaging the morphology of the blood vessels. This paper aims at distinguishing the various layers of arterial walls from images by minimizing human intervention. Cross section images of arteries from various sources were collected[10][11]. The boundaries from the image were obtained using image processing techniques of MATLAB(R2021a). The approach identified was to convert the input RGB images to grayscale, thresholding and applying morphological operators to delineate the Intima, Media, and Adventitia. These regions of interests (ROI) were then superimposed to generate an image with differentiated boundaries and void of unnecessary noise and inhomogeneity. This approach gave us an insight of the differences in various methods of boundary detection and to infer the optimum approach for accurate demarcation of boundaries of the three layers of arterial walls. It paves a pathway for forward modelling and to perform detailed FEM analysis in in-vitro diagnostics. In a nutshell, it was observed that the edge detection procedure implemented could be used for healthy and stenotic arteries. Further studies must be conducted to test the efficiency across a wide range of images and hence generalise its usage. Upon satisfactory boundary detection, forward modelling could be performed using the identified geometric forms.

Published Online: 2021-10-09
Published in Print: 2021-10-01

© 2021 The Author(s), published by Walter de Gruyter GmbH, Berlin/Boston

This work is licensed under the Creative Commons Attribution 4.0 International License.

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