Malignant melanoma is by far the most dangerous type of skin cancer. Currently, the gold standard to diagnose melanoma in the clinic is excisional biopsy and histopathologic analysis. Approximately 15-30 benign lesions are biopsied to diagnose each melanoma. Additionally, biopsies are invasive and result in pain, anxiety, scarring and disfigurement of patients, and they can be a financial burden to the health care system. Among several imaging techniques developed to enhance melanoma diagnosis, optical coherence tomography (OCT) with its high-resolution and intermediate penetration depth can potentially provide required diagnostic information, noninvasively. We propose an image analysis algorithm, ‘optical properties extraction (OPE)’ that drastically improves the specificity and sensitivity of OCT by identifying unique optical radiomic signatures pertinent to melanoma detection. We evaluate the performance of the algorithm using several tissue-mimicking phantoms. We then test the OPE algorithm with sixty-nine human subjects and demonstrate that melanoma can be differentiated from benign nevi with 97% sensitivity, and 98% specificity.
Optical coherence tomography (OCT) imaging is a high resolution and non-invasive imaging modality that provides cross-sectional images of a tissue. The time of flight in the signal processing of the OCT system is calculated based on a constant refractive index. Different layers of the complex tissues however have different refractive indices. This issue prompts pixels in the image to be misplaced and that makes it difficult for the physicians to have an accurate diagnosis of abnormalities based on OCT imaging, e.g., in measurement of the thickness of skin layers. In this paper, we propose a novel post-processing method to correct for the refractive index. The proposed method is based on imaging a tissue to which a needle has been penetrated.
We propose an algorithm to compensate for the refractive index error in the optical coherence tomography (OCT) images of multilayer tissues, such as skin. The performance of the proposed method has been evaluated on one- and two-layer solid phantoms, as well as the skin of rat paw.
In this study, we assess the applicability of optical coherence tomography (OCT) for non-invasive imaging of skin morphology for the assessment of efficacy of cosmetic skin wrinkle-reduction products in humans. Evaluation of skin care products for reduction of facial wrinkles is largely limited to photographic (non-quantitative) comparison of skin surface texture before and after either single or prolonged application of skin care product. OCT could be a technique for monitoring changes in cross-sectional skin morphology. An optical attenuation coefficient analysis is also carried out to quantitatively study the changes in different layers of the skin.
OCT skin images suffer from artifacts. Speckle is the main artifact while the other one is called background noise. In this study, we propose an algorithm that significantly reduces the background noise before applying a speckle reduction method. The results show that the diagnostically relevant features in the images become clearer after applying the proposed method. We used sub-pixel weighted median filtering for speckle reduction. The results from background noise removal in combination with the proposed speckle reduction algorithm show a significant improvement in the clarity of diagnostically relevant features in in-vivo human skin images.
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