Abstract
Ultrasound provides a non-invasive means for visualizing various tissues within the human body. However, these visualizations tend to be filled with speckle noise and other artifacts, due to the sporadic nature of high frequency sound waves. Many techniques for segmenting ultrasound images have been introduced in order to deal with these problems. One such technique is the active contouring.
In this paper, two proposed alterations to the dynamic programming parametric active contour model (or snake) are introduced. The first alteration allows the snake to converge to the one-response result of a modified Canny edge detector. The second provides a function that allows a user to preset a-priori knowledge about a given object being detected, by means of curve fitting and energy modification. The results yield accurate segmentations of cross-sectional transverse carotid artery ultrasound images that are validated by an independent clinical radiologist. Utilizing the proposed alterations leads to a reduction of clinician interaction time while maintaining an acceptable level of accuracy for varying measures such as percent stenosis.
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Hamou, A.K., Osman, S., El-Sakka, M.R. (2007). Carotid Ultrasound Segmentation Using DP Active Contours. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2007. Lecture Notes in Computer Science, vol 4633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74260-9_85
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DOI: https://doi.org/10.1007/978-3-540-74260-9_85
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