Analysis of Abnormalities in Common Carotid Artery Images Using Multiwavelets

According to the report given by World Health Organization, by 2030 almost 23.6million people will die from cardiovascular diseases (CVD), mostly from heart disease and stroke. The main objective of this work is to develop a classifier for the diagnosis of abnormal Common Carotid Arteries (CCA). This paper proposes a new approach for the analysis of abnormalities in longitudinal B-mode ultrasound CCA images using multiwavelets. Analysis is done using HM and GHM multiwavelets at various levels of decomposition. Energy values of the coefficients of approximation, horizontal, vertical and diagonal details are calculated and plotted for different levels. Plots of energy values show high correlation with the abnormalities of CCA and offer the possibility of improved diagnosis of CVD. It is clear that the energy values can be used as an index of individual atherosclerosis and to develop a cost effective system for cardiovascular risk assessment at an early stage.