@conference {48195,
	title = {Skin mimicking solid optical tissue phantom fulfillment and its characterization},
	booktitle = {Photonics in Dermatology and Plastic Surgery 2022},
	year = {2022},
	publisher = {SPIE},
	organization = {SPIE},
	address = {1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA},
	abstract = {<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){\textquoteright}), 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>
},
	keywords = {Absorption, anisotropy factor, scattering, skin mimicking, solid tissue phantom},
	isbn = {978-1-5106-4740-4; 978-1-5106-4739-8},
	doi = {10.1117/12.2609577},
	author = {Kumar, Ajay and Hinge, Sarika and Dixit, Hemant and Kanawade, Rajesh and Kulkarni, Gauri},
	editor = {Choi, B and Zeng, H}
}
@conference {48799,
	title = {Fast normalization and despeckled method for skin optical coherence tomography image via deep learning},
	booktitle = {Photonics in Dermatology and Plastic Surgery 2023},
	year = {2023},
	month = {JUL},
	publisher = {SPIE},
	organization = {SPIE},
	address = {1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA},
	abstract = {<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>
},
	keywords = {CNN, deep learning, denoising, Optical coherence tomography, speckle},
	isbn = {978-1-5106-5809-7; 978-1-5106-5810-3},
	doi = {10.1117/12.2651211},
	author = {Rahaman, Jarjish and Lukas, Brandon and May, Julia and Puyana, Carolina and Tsoukas, Maria and Avanaki, Kamran},
	editor = {Choi, B and Zeng, H}
}
