High resolution imaging system using spectral domain Optical Coherence Tomography using NIR source

Bhatia, Prachi; Choudhari, Suyog; Rodrigues, Anjana; Patil, Mukesh D.; Makkar, Roshan

doi:10.1109/wispnet.2016.7566535

Cited by 2 publications

(2 citation statements)

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“…Figure 1 describes the working principle of Michelson Interferometer, where half-silvered beam splitter is used (Kim et al, 2018;Lee et al, 2011). Bhatia et al (2016) described the core of the OCT systems and implemented the spectral-domain OCT(SD-OCT). The detected signal from the OCT gives three terms of getting in the data, which were constant term, autocorrelation and cross-correlation (Bhatia et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Bhatia et al (2016) described the core of the OCT systems and implemented the spectral-domain OCT(SD-OCT). The detected signal from the OCT gives three terms of getting in the data, which were constant term, autocorrelation and cross-correlation (Bhatia et al, 2016). The different applications of OCT in clinical and non-clinical fields are discovered.…”

Section: Introductionmentioning

confidence: 99%

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Algorithm for B-scan Image Reconstruction in Optical Coherence Tomography

Patil¹,

Mahajan²,

Subramani³

et al. 2021

JST

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Optical coherence tomography (OCT) is an evolving medical imaging technology that offers in vivo cross-sectional, sub-surface images in real-time. OCT has become popular in the medical as well as non-medical fields. The technique extensively uses for food industry, dentistry, dermatology, and ophthalmology. The technique is non-invasive and works on the Michelson interferometry principle, i.e., dependent on back reflections of the signal and its interference. The objective is to develop an algorithm for signal processing to construct an OCT image and then to enhance the quality of the image using image processing techniques like filtering. The image construction was primarily based on the Fourier transform (FT) of the dataset obtained by data acquisition. This FT could be performed rapidly with the extensively used algorithm of fast Fourier transform (FFT). The depth-wise information could be extracted from each A-scan, i.e., axial scan and also the B-scan was obtained from the A-scan to see the structure of sample. The maximum penetration depth achieved with proposed system was 2.82mm for 1024 data points. First and second layer of leaf were getting at thickness of 1mm and 1.6mm, respectively. A-scans for Human fingertip gave its first, second and third layer was at a thickness of 0.75mm, 0.9mm and 1.6mm, respectively. A-scans for foam sheet gave its first, second and third layer was at a thickness of 0.6mm, 0.75mm, and 0.85mm, respectively.

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