SignificanceRapid diagnosis and analysis of human keloid scar tissues in an automated manner are essential for understanding pathogenesis and formulating treatment solutions.AimOur aim is to resolve the features of the extracellular matrix in human keloid scar tissues automatically for accurate diagnosis with the aid of machine learning.ApproachMultiphoton microscopy was utilized to acquire images of collagen and elastin fibers. Morphological features, histogram, and gray-level co-occurrence matrix-based texture features were obtained to produce a total of 28 features. The minimum redundancy maximum relevancy feature selection approach was implemented to rank these features and establish feature subsets, each of which was employed to build a machine learning model through the tree-based pipeline optimization tool (TPOT).ResultsThe feature importance ranking was obtained, and 28 feature subsets were acquired by incremental feature selection. The subset with the top 23 features was identified as the most accurate. Then stochastic gradient descent classifier optimized by the TPOT was generated with an accuracy of 96.15% in classifying normal, scar, and adjacent tissues. The area under curve of the classification results (scar versus normal and adjacent, normal versus scar and adjacent, and adjacent versus normal and scar) was 1.0, 1.0, and 0.99, respectively.ConclusionsThe proposed approach has great potential for future dermatological clinical diagnosis and analysis and holds promise for the development of computer-aided systems to assist dermatologists in diagnosis and treatment.
KEYWORDS: 3D displays, 3D image reconstruction, Far-field diffraction, Digital imaging, Holography, Computer generated holography, Field emission displays, Holograms, 3D modeling, Digital holography
The widespread use of holographic VR/AR devices are limited by bulky refractive and diffractive optics. To address these problems, a NED system combining the 3D CGH based on Fraunhofer diffraction and a metalens with 5 mm diameter as an eyepiece is proposed in this paper. Because of the capability of wavefront shaping in a subwavelength scale, the metalens eyepiece surly facilitates lightening the CGH-NED systems. Experiments are carried out for this design, where Fraunhofer diffraction with digital lens phases of different focal lengths are applied, and the metalens transforms the holographic reconstructed 3D image into virtual image to realize NED. The metalens eyepiece composed of silicon nitride anisotropic nanofins is fabricated with the diffraction efficiency and field of view for 532 nm incidence of 15.7% and 31°, respectively. Our work combining of CGH and metalens may provide a promising solution in future for computer-generated holographic 3D portable display.
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