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.
Optical Coherence Tomography (OCT) has a great potential to elicit clinically useful information from tissues due to its high axial and transversal resolution. In practice, an OCT setup cannot reach to its theoretical resolution due to imperfections of its components, which make its images blurry. The blurriness is different alongside regions of image; thus, they cannot be modeled by a unique point spread function (PSF). In this paper, we investigate the use of solid phantoms to estimate the PSF of each sub-region of imaging system. We then utilize Lucy-Richardson, Hybr and total variation (TV) based iterative deconvolution methods for mitigating occurred spatially variant blurriness. It is shown that the TV based method will suppress the so-called speckle noise in OCT images better than the two other approaches. The performance of proposed algorithm is tested on various samples, including several skin tissues besides the test image blurred with synthetic PSF-map, demonstrating qualitatively and quantitatively the advantage of TV based deconvolution method using spatially-variant PSF for enhancing image quality.
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.
Optical Coherence Tomography (OCT) offers real-time high-resolution three-dimensional images of tissue
microstructures. In this study, we used OCT skin images acquired from ten volunteers, neither of whom had any skin
conditions addressing the features of their anatomic location. OCT segmented images are analyzed based on their
optical properties (attenuation coefficient) and textural image features e.g., contrast, correlation, homogeneity, energy,
entropy, etc. Utilizing the information and referring to their clinical insight, we aim to make a comprehensive
computational model for the healthy skin. The derived parameters represent the OCT microstructural morphology and
might provide biological information for generating an atlas of normal skin from different anatomic sites of human
skin and may allow for identification of cell microstructural changes in cancer patients. We then compared the
parameters of healthy samples with those of abnormal skin and classified them using a linear Support Vector Machines
(SVM) with 82% accuracy.
Optical coherence tomography (OCT) is a noninvasive diagnostic method that
offers a view into the superficial layers of the skin in vivo in real-time. OCT
delivers morphological images of microstructures within the skin. Epidermal
thickness in OCT images is of paramount importance, since dermo-epidermal
junction (DEJ) location alteration is the start of several skin abnormalities. Due to
the presence of speckle noise, devising an algorithm for locating DEJ in the OCT
images is challenging. In this study we propose a semi-automatic DEJ detection
algorithm based on graph theory that is resistant to speckle. In this novel approach
we use attenuation map as a complementary feature compared to the previous
methods that are mainly based on the intensity information. The method is based
on converting border segmentation problem to the shortest path problem using
graph theory. To smooth borders, we introduced a thinning fuzzy system enabling
closer match to manual segmentation. Subsequently, an averaged A-scan analysis
is performed to obtain the mean epidermal thickness. The DEJ detection method is
performed on 96 B-Scan OCT skin images taken from different sites of body of
healthy individuals. The results are evaluated based on several expert’s visual