Affecting Factors of Weight and Gender on Carotid Arterial Wall Motion: Diameter and Displacement Assessment

There are many cases that relate to the atherosclerosis disease due to the development of plaque inside the blood vessel. Several factors cause a person suffering from atherosclerosis disease such as overweight, high blood pressure, unhealthy blood cholesterol level, and smoking habit. As the prevention, this work aims to detect the early stage of atherosclerosis disease which divided into gender and body mass index (BMI) categories. The focused parameters are diameter size and wall displacement of common carotid artery (CCA). In order to assess those two parameters, offline B-mode ultrasound images are used which then converted into grayscale images. All images are segmented to track the better edges of carotid arterial wall. We observed the correlation of CCA diameter and wall displacement to BMI and gender categories, where the diameter in overweight category own 9.7%, 4.8%, 14.8%, and 4.9% higher compared to underweight and normal weight categories, both in male and female groups, respectively. For wall displacement, it shows significant differences of 11% to 25% higher in overweight category compared to the other two weight categories. These findings suggest that the CCA diameters and wall displacement may reflect to the atherosclerosis before plaque formation, thus could be an important factor during the development of atherosclerosis. Moreover, the assessment conducted in both conditions; longitudinal and transverse images, confirmed there is only slightly error 7.4% and 7.2% for male and female groups

Image Processing Using FPGAs

This book presents a selection of papers representing current research on using field programmable gate arrays (FPGAs) for realising image processing algorithms. These papers are reprints of papers selected for a Special Issue of the Journal of Imaging on image processing using FPGAs. A diverse range of topics is covered, including parallel soft processors, memory management, image filters, segmentation, clustering, image analysis, and image compression. Applications include traffic sign recognition for autonomous driving, cell detection for histopathology, and video compression. Collectively, they represent the current state-of-the-art on image processing using FPGAs