Quantifying optical microangiography images obtained from a spectral domain optical coherence tomography system

Roberto Reif, Jia Qin, Lin An, Zhongwei Zhi, Suzan Dziennis, Ruikang Wang, Roberto Reif, Jia Qin, Lin An, Zhongwei Zhi, Suzan Dziennis, Ruikang Wang

Abstract

The blood vessel morphology is known to correlate with several diseases, such as cancer, and is important for describing several tissue physiological processes, like angiogenesis. Therefore, a quantitative method for characterizing the angiography obtained from medical images would have several clinical applications. Optical microangiography (OMAG) is a method for obtaining three-dimensional images of blood vessels within a volume of tissue. In this study we propose to quantify OMAG images obtained with a spectral domain optical coherence tomography system. A technique for determining three measureable parameters (the fractal dimension, the vessel length fraction, and the vessel area density) is proposed and validated. Finally, the repeatability for acquiring OMAG images is determined, and a new method for analyzing small areas from these images is proposed.

Figures

Figure 1
Figure 1
Experimental setup of the spectral domain optical coherence tomography system. SLD: superluminescent diode, OC: optical circulator, PC: polarization controller, M: mirror.
Figure 2
Figure 2
(a) OMAG image obtained from a mouse ear. Scale bar is 0.1 mm. (b) Black and white segmented image of (a). (c) Skeletonization of the segmented image (b). (d) Overlay of (c) and (a).
Figure 3
Figure 3
(a) Fifth iteration of a pentaflake. Cropped pentaflake for a size of (b) 64 × 64 (solid square in (a)) and (c) 256 × 256 (dashed square in (a)) pixels. (d) Box size versus the number of boxes. (e) Mean and standard deviation obtained from images of different sizes.
Figure 4
Figure 4
(a) Overlay of the GS image with the optimum automatically segmented image. (b) Overlay of the skeletonized GS image with the optimum automatically segmented skeleton image. (c) ROC curve of the automatically segmented image using a combination of 1000 different variables V1, V2, and V3. (d) Mean and standard deviation of the percent variation of the FD, VLF, and VAD between the manually and automatically segmented five test images.
Figure 5
Figure 5
OMAG images obtained from the same mouse ear at day (a) 1, (b) 2, and (c) 3. Scale bar is 0.5 mm.
Figure 6
Figure 6
(a) Black and white segmented image multiplied by the (a) fractal dimension, (c) vessel length fraction, and (e) vessel area density of the OMAG image from day 1 (Figure 5(a)). Mean and standard deviation of the (b) fractal dimension, (d) vessel length fraction, and (f) vessel area density from the two regions of interests in Figure 5(a). The values were calculated with a 32 × 32 pixel length sliding window. The dashed lines in (a), (c), and (e) indicate the segmentation of the edges in the image.

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Source: PubMed

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