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|Title:||Segmentation of atherosclerotic carotid plaque in ultrasound video||Authors:||Petroudi, Styliani H.
Nicolaides, Andrew N.
Loizou, Christos P. ItemCrisRefDisplayStrategy.rp.deleted.icon
|Keywords:||Bioengineering;Atherosclerosis;Ultrasonic imaging;Algorithms;Heart beat||Category:||Electrical Engineering, Electronic Engineering, Information Engineering||Field:||Engineering and Technology||Issue Date:||2012||Publisher:||IEEE||Source:||4th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2012, San Diego, CA||Abstract:||The degree of stenosis of the common carotid artery (CCA) but also the characteristics of the arterial wall including plaque size, composition and elasticity represent important predictors used in the assessment of the risk for future cardiovascular events. This paper proposes and evaluates an integrated system for the segmentation of atherosclerotic carotid plaque in ultrasound video of the CCA based on normalization, speckle reduction filtering (with the hybrid median filter) and parametric active contours. The algorithm is initialized in the first video frame of the cardiac cycle with human assistance and the moving atherosclerotic plaque borders are tracked and segmented in the subsequent frames. The algorithm is evaluated on 10 real CCA digitized videos from B-mode longitudinal ultrasound segments and is compared with the manual segmentations of an expert, for every 20 frames in a time span of 3-5 seconds, covering in general 2 cardiac cycles. The segmentation results are very satisfactory with a true negative fraction (TNF) of 79.3%, a true-positive fraction (TPF) of 78.12%, a false-positive fraction (FPF) of 6.7% and a false-negative fraction (FNF) of 19.6% between the ground truth and the presented plaque segmentations, a Williams index (KI) of 80.3%, an overlap index of 71.5%, a specificity of 0.88±0.09, a precision of 0.86±0.10 and an effectiveness measure of 0.77±0.09. The results show that integrated system investigated in this study could be successfully used for the automated video segmentation of the carotid plaque.||URI:||http://ktisis.cut.ac.cy/handle/10488/6983||ISSN:||1557170X||DOI:||10.1109/EMBC.2012.6345869||Rights:||© 2012 IEEE||Type:||Conference Papers|
|Appears in Collections:||Δημοσιεύσεις σε συνέδρια/Conference papers|
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