02520nas 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