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.

Foreign