Classification of Ultrasound Carotid Artery Images Using Texture Features
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The Cardio Vascular Disease (CVD) is the most fatal disease in the world in terms of deaths. The cardio vascular disease, associated with stroke and heart attack, is mainly caused by the increase in calcium deposition in the carotid artery. The Intima-Media Thickness (IMT) of the Common Carotid Artery (CCA) is widely used as an early indicator of CVD. The risk of CVD varies with age groups and this can be categorized based on the texture pattern of image of the carotid artery. This work presents an automated method to classify the carotid artery abnormalities by determining an appropriate Region of Interest (ROI), extracting the texture features, and calculating the IMT. The Ultrasound specimen image is acquired, intensity normalized, pre-processed to remove the speckle noise and then segmented. The texture analysis for segmented images is done using AM – FM techniques. The instantaneous values of the amplitude and frequency of each image specimen is obtained and it is quantized. It is then compared with the standard texture pattern, to identify whether the artery is normal or abnormal. Simulation results shows significant texture differences between the higher-risk age group of >60 years and the lower-risk age group of <50 and the 50-60 age group. Detecting the level of CVD was done by measuring the IMT. The overall process aims at implementing a fully automated system which helps in avoiding human errors, while measuring these values. The measurement technique is described in detail, highlighting the advantages compared to other methods and reporting the experimental results. Finally, the intrinsic accuracy of the system is estimated by an analytical approach. It also decreases inter-reader bias, potentially making it applicable for use in cardiovascular risk assessment.
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