Abstract
Background: Atherosclerosis is the systemic disease responsible for most of the cardiovascular diseases. Increased Intima-Media Thickness (IMT) of carotid artery is a validated indicator of disease progression and cardiovascular risk.
Methods: In this work an automatic segmentation technique is attempted to improve and preserve the inter-region edges in B-mode longitudinal ultrasound images of Common Carotid Arteries (CCA). The edge information generated using Gaussian filter is used to set the Level set function towards the boundaries of the Intima-Media Complex (IMC). The automated analysis using a variational level set method without re-initialization procedure is used to extract texture and geometric features to analyze pathological conditions more accurately. Result: Results show that the proposed framework is able to segment IMC and 96.7% correlation with ground truth area. It is also observed that maximum regional overlap obtained using dice similarity with average of 88%, Jaccard index 75% and volume similarity 97%. Discussion: The texture and ratio-metric features show significant demarcation (p<0.0001) between normal and atherosclerosis subjects. The most significant feature such as autocorrelation shows mean and standard deviation values of 0.821±0.065 in normal and 0.579 ±0.143 in abnormal. Aspect ratio calculated from geometric features is found to have maximum of 7.9 for abnormal and found to decrease with severity of the disease, 12.75 for normal images of CCA. The integration of edge map in the level set framework could extract the boundaries by preserving the edge details and show good correlation with the ground truth values. Further, the group of images investigated for significant features show distinct separation between normal and atherosclerosis subjects. Conclusion: These findings could be clinically useful in diagnosis and treatment of cardiovascular disease.Keywords: Atherosclerosis, common carotid artery, intima-media thickness, stroke, ultrasound image, variational level set.
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