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