While millions of people world wide suffer from arterial diseases, such as peripheral arterial disease, there are a limited number of methods that can be used to diagnose and track these diseases which are also easy, quick and non-invasive.

This work focuses on what is needed to improve diagnosing and tracking of peripheral arterial disease (PAD) using visualisation techniques. Visualising the blood flow pulsation in the skin can be useful in cases of arterial diseases, as the diseases can influence the blood flow by obstructions and arterial stiffness. The main objective in the visualisation is to show the acceleration of the blood flow, as this is linked to the arterial stiffness.

The proposed algorithm for visualising the acceleration of the blood flow is comprised of multiple steps, including techniques such as motion reduction, Eulerian video magnification, remote photoplethysmography signal extraction and using the second derivative. The input of this algorithm are videos of the skin of patients, this makes this method easy and non-invasive.

Photoplethysmography (PPG) signals are present in videos of skin, but can not be seen with the naked eye. Using Eulerian video magnification the PPG signals are amplified for better processing and visibility. By combining groups of pixels into small patches, a decrease in processing time is achieved and it adds a filtering effect. The size of the patches controls the resolution of the visualisation. Movement from the camera or patient is detrimental when extracting the PPG signal from the video. To counteract the motion in the videos a motion reduction step, using optical flow, is applied. Using the Plane-orthogonal-to-skin (POS) algorithm, the signal extracted from videos is converted to a PPG signal. Calculating the second derivative of the PPG signal gives the acceleration of the signal. By splitting the acceleration signal into positive and negative numbers, the acceleration and deceleration of the blood flow is visualised.

Synthetic videos simulating the skin were generated in various levels of accuracy to aid the development of the algorithm and to conduct experiments. The levels range from a simple pulsating square to a moving PPG signal over a blood vessel like structure. In addition, real videos of patients were used.

The experiments show the feasibility of visualising the acceleration of the blood flow pulsation in the skin, but also highlights areas of improvements and future research. More fine-tuning of the algorithm is needed, in addition to acquiring more videos of patients with PAD before and after surgery in a controlled environment.

A working proof of concept of the algorithm is shown. It has the potential of being a novel method of diagnosing and tracking arterial diseases.