02301nas a2200253   4500008004100000020004100041245008500082210006900167260006500236520141300301653001501714653002201729653001501751653001901766653002501785100001601810700001801826700001801844700002101862700002001883700001201903700001201915856012001927       2022                        eng d  a978-1-5106-4740-4; 978-1-5106-4739-800aSkin mimicking solid optical tissue phantom fulfillment and its characterization0 aSkin mimicking solid optical tissue phantom fulfillment and its   a1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USAbSPIE3 a<p>
	Skin mimicking optical tissue phantoms are widely used in diagnostics systems for characterization, optimization, routine calibration and validation. In general, solid phantoms are more preferred in comparison to liquid phantoms. Therefore, our aim is to prepare and characterize the solid tissue phantoms having skin equivalent optical properties. In this work, we have used epoxy resin and hardener as a base material and titanium oxide (TiO2) nanoparticles and ink as a scatterer and absorber media, respectively. The total transmission (Tt), collimated transmission (Tc), and diffuse reflectance (Rd) spectra of the developed phantoms were measured with an integrating sphere installed in UV-VIS spectrometer within the wavelength range 400-700 nm. To characterize the optical properties such as absorption (mu(a)), reduced scattering (mu(s)'), and anisotropy factor (g) of the developed tissue phantoms, the numerical model based on Inverse Adding Doubling (IAD) has been used. With various concentrations of absorber and scatterer, a calibration curve was prepared. The calculated experimental optical properties from IAD matched with the predicted intrinsic optical properties of the skin. Thus, the preliminary results suggest that the recipe used in this study may be used as an alternative approach to developing skin mimicking solid optical phantom for diagnostics system applications.</p>
10aAbsorption10aanisotropy factor10ascattering10askin mimicking10asolid tissue phantom1 aKumar, Ajay1 aHinge, Sarika1 aDixit, Hemant1 aKanawade, Rajesh1 aKulkarni, Gauri1 aChoi, B1 aZeng, H  uhttps://library.ncl.res.in/content/skin-mimicking-solid-optical-tissue-phantom-fulfillment-and-its-characterization02520nas a2200265   4500008004100000020004100041245010700082210006900189260007000258520157700328653000801905653001801913653001401931653003301945653001201978100002101990700001902011700001502030700002102045700001902066700002002085700001202105700001202117856012502129       2023                        eng d  a978-1-5106-5809-7; 978-1-5106-5810-300aFast normalization and despeckled method for skin optical coherence tomography image via deep learning0 aFast normalization and despeckled method for skin optical cohere  a1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USAbSPIEcJUL3 a<p>
	Optical coherence tomography (OCT) is well-known for its high-resolution, non-invasive imaging modality with many medical uses, including skin imaging. Nevertheless, speckle noise limits the analytical capabilities of this imaging tool, causing deterioration in contrast and less exact detection of tissue microstructural heterogeneity. To address this issue, we proposed OCT despeckling approach by combing it with normalization to reduce the speckle noise more effectively. The proposed method contains multiple steps including phase correlation for alignment of misaligned frames, frame averaging which minimizes speckle noise, region-wise pixels normalization that helps to normalize intensity pixels, a modified BM3D filtering to suppress the white and speckle, and contrast enhancement to improve the contrast appropriately. To establish the approach, we applied 130 distinct B-scan skin OCT images and validate and evaluate the performance using qualitatively and quantitatively. Although the output obtained by the algorithm is promising, the method is time-consuming because of a series of steps. To reduce the time complexity, we also develop a supervised deep learning model by mapping between noisy-despeckled image pairs. The effectiveness and applicability of our DL approach were assessed using 130 skin OCT B-scans from various body areas taken from 45 healthy people between the ages of 20 and 60. With the support of the experimental results, we demonstrate that our DL model is capable to normalize and despeckling OCT images simultaneously.</p>
10aCNN10adeep learning10adenoising10aOptical coherence tomography10aspeckle1 aRahaman, Jarjish1 aLukas, Brandon1 aMay, Julia1 aPuyana, Carolina1 aTsoukas, Maria1 aAvanaki, Kamran1 aChoi, B1 aZeng, H  uhttps://library.ncl.res.in/content/fast-normalization-and-despeckled-method-skin-optical-coherence-tomography-image-deep