2016
DOI: 10.1117/1.jmi.3.2.024501
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Processing to determine optical parameters of atherosclerotic disease from phantom and clinical intravascular optical coherence tomography three-dimensional pullbacks

Abstract: , "Processing to determine optical parameters of atherosclerotic disease from phantom and clinical intravascular optical coherence tomography three-dimensional pullbacks," J. Med. Imag. 3(2), 024501 (2016) Abstract. Analysis of intravascular optical coherence tomography (IVOCT) data has potential for real-time in vivo plaque classification. We developed a processing pipeline on a three-dimensional local region of support for estimation of optical properties of atherosclerotic plaques from coronary artery, IVOC… Show more

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Cited by 3 publications
(2 citation statements)
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“…The DHI information is directly related to the mean light attenuation within the tissue. Following accepted models for light attenuation in biological specimens, 38,39 taking the depth at half intensity or the direct attenuation coefficient yields similar information. The intensity as a function of depth for any given OCT A-line flows an exponential decay, linearized by taking the logarithm.…”
Section: Multilayer Descriptormentioning
confidence: 98%
“…The DHI information is directly related to the mean light attenuation within the tissue. Following accepted models for light attenuation in biological specimens, 38,39 taking the depth at half intensity or the direct attenuation coefficient yields similar information. The intensity as a function of depth for any given OCT A-line flows an exponential decay, linearized by taking the logarithm.…”
Section: Multilayer Descriptormentioning
confidence: 98%
“…In the field of coronary artery plaque segmentation, Lu et al (2014) proposed a method based on image feature extraction and Support Vector Machine (SVM), which realized semi-automatic segmentation of OCT images and achieved a 83% accuracy on a test dataset. Shalev et al (2016) proposed a segmentation method based on hidden Markov random field (HMRF), which can detect plaques from OCT cardiovascular images. Wang et al (2017) proposed a semi-automatic segmentation algorithm using K-means clustering to obtain points aggregation which is needed for random walk in the next stage, then used the obtained points aggregation as seed points, realizing plaque segmentation by random walk algorithm of weight function.…”
Section: Related Workmentioning
confidence: 99%