KEYWORDS: Optical coherence tomography, Skin, Deep learning, Education and training, Speckle, Tumor growth modeling, Image quality, Signal to noise ratio, Image processing, Data modeling
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.
Optical coherence tomography is a three-dimensional imaging modality that captures microstructures of the tissue. The application of OCT in dermatology is limited due to the low visibility in these images. Numerous image denoising and enhancement algorithms have been implemented for quality improvement of the OCT skin images. One way to evaluate the performance of these algorithms is to quantify the quality of the processed images using different image quality metrices. Current image quality metrics though do not fairly represent the visual quality of the images. We propose an algorithm to quantify the quality of OCT images compatible with human visual perception, and the diagnostically important features in skin images. We implement a new metric called Signal to Noise Ratio. The metric is assessed on different number of averaged OCT images taken from the same cross section of the skin.
Survival from melanoma, the deadliest form of skin cancer, depends heavily on early detection. Several non-invasive medical imaging modalities have been developed to detect melanoma, of which optical coherence tomography (OCT) is gaining popularity. Although OCT generally does not yet provide sufficient performance in detecting melanoma, radiomic studies involving quantitative OCT image analysis demonstrate promising results. We propose extracting a large set of radiomic features from OCT images of skin, exploring how the features differ between melanoma and non-melanoma, and performing feature selection to identify the most informative OCT radiomic features that characterize melanoma for improved melanoma detection.
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