2018
DOI: 10.1364/ao.57.010117
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Quantification of cerebral vascular perfusion density via optical coherence tomography based on locally adaptive regional growth

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Cited by 14 publications
(11 citation statements)
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“…Uneven lighting is an additional problem that has to be faced in blood vessel segmentation. We employ the previously proposed "locally adaptive region growing" algorithm to segment blood vessels [39]. This is an improved algorithm based on the traditional region growing The advantage of this method is that it can better adapt to the drastic changes of local blood vessel signal intensity.…”
Section: Blood Vessels Segmentationmentioning
confidence: 99%
“…Uneven lighting is an additional problem that has to be faced in blood vessel segmentation. We employ the previously proposed "locally adaptive region growing" algorithm to segment blood vessels [39]. This is an improved algorithm based on the traditional region growing The advantage of this method is that it can better adapt to the drastic changes of local blood vessel signal intensity.…”
Section: Blood Vessels Segmentationmentioning
confidence: 99%
“…15 Optical coherence tomography (OCT) is a noninvasive method that allows volumetric imaging of biological tissues at high resolution and high speeds. 16,17 Another OCT-based technique, calculating optical attenuation coefficient (OAC) from OCT signals, has been attracting the attention of scholars. 18 Infrared light decays exponentially when penetrating tissues and its attenuation coefficient can be used to distinguish between different types of tissues, or different physiological states of the same tissue, and can be utilized to detect brain edema.…”
Section: Introductionmentioning
confidence: 99%
“…In this study, a spectral‐domain OCT (SD‐OCT) system with a central wavelength of 1310 nm and a spectral bandwidth of 60 nm was used to visualize the femoral vein and for all acquisitions as previously described . Briefly, the spectrometer used A line scan infrared InGaAs array detector operating at a 92 kHz line rate, and for each cross‐sectional image 400 A lines were collected.…”
Section: Methodsmentioning
confidence: 99%