Abstract
Jones matrix-based polarization sensitive optical coherence tomography (JM-OCT) simultaneously measures optical intensity, birefringence, degree of polarization uniformity, and OCT angiography. The statistics of the optical features in a local region, such as the local mean of the OCT intensity, are frequently used for image processing and the quantitative analysis of JM-OCT. Conventionally, local statistics have been computed with fixed-size rectangular kernels. However, this results in a trade-off between image sharpness and statistical accuracy. We introduce a superpixel method to JM-OCT for generating the flexible kernels of local statistics. A superpixel is a cluster of image pixels that is formed by the pixels' spatial and signal value proximities. An algorithm for superpixel generation specialized for JM-OCT and its optimization methods are presented in this paper. The spatial proximity is in two-dimensional cross-sectional space and the signal values are the four optical features. Hence, the superpixel method is a six-dimensional clustering technique for JM-OCT pixels. The performance of the JM-OCT superpixels and its optimization methods are evaluated in detail using JM-OCT datasets of posterior eyes. The superpixels were found to well preserve tissue structures, such as layer structures, sclera, vessels, and retinal pigment epithelium. And hence, they are more suitable for local statistics kernels than conventional uniform rectangular kernels.
Highlights
Optical coherence tomography (OCT) possesses high resolution and high acquisition speed, and three-dimensional volumetric imaging and video rate monitoring capabilities
We present a superpixel method that is based on the idea of the SLIC algorithm [27, 28] but is specially designed for multifunctional Jones matrix OCT (JM-OCT)
Our JM-OCT superpixel method generates superpixels by clustering pixels based on their spatial proximity and optical feature similarity where the optical features include OCT intensity, BR, degree of polarization uniformity (DOPU), and OCT angiography (OCTA)
Summary
Optical coherence tomography (OCT) possesses high resolution and high acquisition speed, and three-dimensional volumetric imaging and video rate monitoring capabilities. An OCT signal intensity image provides layered structures of the retina and helps in the accurate diagnosis of retinal disease. BR is estimated using signals in a small local region [8, 13] These local statistics are computed using a fixed-size rectangular kernel. We introduce clusters of pixels with a flexible shape, so-called superpixels, as the kernel for computing local statistics. A superpixel is formed with image pixels that share similar signal values and possess high spatial proximity [26]. The SLIC algorithm generates superpixels by clustering pixels based on their spatial proximity and color similarity. Our JM-OCT superpixel method generates superpixels by clustering pixels based on their spatial proximity and optical feature similarity where the optical features include OCT intensity, BR, DOPU, and OCTA. Systemic methods for optimizing parameters used for superpixel generation are presented
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