The examination of the dermis/epidermis junction (DEJ) is clinically important for skin cancer diagnosis. Reflectance
confocal microscopy (RCM) is an emerging tool for detection of skin cancers in vivo. However, visual localization of
the DEJ in RCM images, with high accuracy and repeatability, is challenging, especially in fair skin, due to low contrast,
heterogeneous structure and high inter- and intra-subject variability. We recently proposed a semi-automated algorithm
to localize the DEJ in z-stacks of RCM images of fair skin, based on feature segmentation and classification. Here we
extend the algorithm to dark skin. The extended algorithm first decides the skin type and then applies the appropriate
DEJ localization method. In dark skin, strong backscatter from the pigment melanin causes the basal cells above the
DEJ to appear with high contrast. To locate those high contrast regions, the algorithm operates on small tiles (regions)
and finds the peaks of the smoothed average intensity depth profile of each tile. However, for some tiles, due to
heterogeneity, multiple peaks in the depth profile exist and the strongest peak might not be the basal layer peak. To
select the correct peak, basal cells are represented with a vector of texture features. The peak with most similar features
to this feature vector is selected. The results show that the algorithm detected the skin types correctly for all 17 stacks
tested (8 fair, 9 dark). The DEJ detection algorithm achieved an average distance from the ground truth DEJ surface of
around 4.7μm for dark skin and around 7-14μm for fair skin.
Optical Coherence Tomography (OCT) is a non-invasive imaging modality that acquires cross sectional images of tissue
in-vivo. It accelerates skin diagnosis by eliminating invasive biopsy and laborious histology in the process.
Dermatologists have widely used it for looking at morphology of skin diseases such as psoriasis, dermatitis, basal cell
carcinoma etc. Skin scientists have also successfully used it for looking at differences in epidermal thickness and its
underlying structure with respect to age, body sites, ethnicity, gender, and other related factors.
Similar to other in-vivo imaging systems, OCT images suffer from a high degree of speckle and noise content, which
hinders examination of tissue structures. Most of the previous work in OCT segmentation of skin was done manually.
This compromised the quality of the results by limiting the analyses to a few frames per area.
In this paper, we discuss a region growing method for automatic identification of the upper and lower boundaries of the
epidermis in living human skin tissue. This image analysis method utilizes images obtained from a frequency-domain
OCT. This system is high-resolution and high-speed, and thus capable of capturing volumetric images of the skin in
short time. The three-dimensional (3D) data provides additional information that is used in the segmentation process to
help compensate for the inherent noise in the images. This method not only provides a better estimation of the epidermal
thickness, but also generates a 3D surface map of the epidermal-dermal junction, from which underlying topography can
be visualized and further quantified.
The appearance and color distribution of skin are important characteristics that affect the human perception of health and vitality. Dermatologists and other skin researchers often use color and appearance to diagnose skin conditions and monitor the efficacy of procedures and treatments. Historically, most skin color and chromophore measurements have been performed using reflectance spectrometers and colorimeters. These devices acquire a single measurement over an integrated area defined by an aperture, and are therefore poorly suited to measure the color of pigmented lesions or other blemishes. Measurements of spots smaller than the aperture will be washed out with background, and spots that are larger may not be adequately sampled unless the blemish is homogenous.
Recently, multispectral imaging devices have become available for skin imaging. These devices are designed to image regions of skin and provide information about the levels of endogenous chromophores present in the image field of view. This data is presented as four images at each measurement site including RGB color, melanin, collagen, and blood images. We developed a robust segmentation technique that can segment skin blemishes in these images and provide more precise values of melanin, blood, and collagen by only analyzing the segmented region of interest. Results from hundreds of skin images show this to be a robust automated segmentation technique over a range of skin tones and shades.
The ability to image and quantitate fluorescently labeled markers in vivo has generally been limited by autofluorescence of the tissue. Skin, in particular, has a strong autofluorescence signal, particularly when excited in the blue or green wavelengths. Fluorescence labels with emission wavelengths in the near-infrared are more amenable to deep-tissue imaging, because both scattering and autofluorescence are reduced as wavelengths are increased, but even in these spectral regions, autofluorescence can still limit sensitivity. Multispectral imaging (MSI), however, can remove the signal degradation caused by autofluorescence while adding enhanced multiplexing capabilities. While the availability of spectral "libraries" makes multispectral analysis routine for well-characterized samples, new software tools have been developed that greatly simplify the application of MSI to novel specimens.
Optical Coherence Microscopy (OCM) enables the acquisition of high resolution, en face images. Most current OCM systems are based on slow analog or high speed digital demodulation schemes. In this paper we demonstrate a low-cost, high speed analog fringe generation and demodulation method. A high power operational amplifier drives a mirrored piezoelectric stack mounted in the reference arm of the interferometer. The drive signal is synchronized with the demodulation frequency of two analog lock-in amplifiers, which extract the first and second harmonic power of the coherence fringes. Tenth order Bessel low-pass filters (LPFs) allow fast system response and reduce carrier frequency noise. Four outputs (X and Y components of first and second harmonic) are acquired with a low-cost data acquisition board and combined to eliminate the slow phase drift in the interferometer. C# software processes and displays the image, and performs automatic interferometer pathlength matching and adjustment. We present images of Arabidopsis leaf in situ, sections of carrot, and ex vivo rat ovary. Excellent image quality is achieved at acquisition speeds up to 40,000 samples/second.
Optical coherence tomography (OCT) is an imaging modality capable of acquiring cross-sectional images of tissue using back-reflected light. Conventional OCT images have a resolution of 10-15μm, and are thus best suited for visualizing tissue layers and structures. OCT images of collagen (with and without endothelial cells) have no resolvable features and may appear to simply show an exponential decrease in intensity with depth. However, examination of these images reveals that they display a characteristic repetitive structure due to speckle.
The purpose of this study is to evaluate the application of statistical and spectral texture analysis techniques for differentiating living and non-living tissue phantoms containing various sizes and distributions of scatterers based on speckle content in OCT images. Statistically significant differences between texture parameters and excellent classification rates were obtained when comparing various endothelial cell concentrations ranging from 0 cells/ml to 25 million/ml. Statistically significant results and excellent classification rates were also obtained using various sizes of microspheres with concentrations ranging from 0 microspheres/ml to 500 million microspheres/ml.
This study has shown that texture analysis of OCT images may be capable of differentiating tissue phantoms containing various sizes and distributions of scatterers.
Optical coherence tomography (OCT) is a cross-sectional imaging modality capable of acquiring images to depths of a few millimeters at resolutions ranging from 10-15 μm. This makes OCT useful for visualizing layers and structures within the tissue, but not effective for seeing in vivo cellular level detail. Random spatially dependent speckle patterns were seen in our images due to the coherent properties of light utilized in OCT. These speckle patterns are dependent on various optical parameters of the system, including numerical aperture, as well as the size and distribution of light scattering particles within the sample.
The purpose of this study is to evaluate the application of statistical and spectral texture analysis techniques for differentiating tissue types based on the structural and speckle content in OCT images. Good correct classification rates were obtained when five different bovine tissues were compared in pairs, averaging 80% correct, and reasonable rates were obtained comparing normal vs. abnormal mouse lung tissue, averaging 64.0% and 88.6%, respectively.
This study has shown that texture analysis of OCT images may be capable of differentiating tissue types without reliance on visible structures.
Most identification of tissues in OCT images has relied on the presence or absence of features and layers. However, in some pathologies as well as some normal tissues OCT images appear homogeneous. Examination of these images reveals that they display a characteristic repetitive structure due to speckle. Since speckle is influenced by the local index of refraction mismatches, it may be possible to differentiate between different types of tissues based on analysis of the speckle pattern. The determination of tissue type may be supported as well by local contrast distribution analysis or speckle decorrelation degree, which are widely used in measurement and characterization of surface roughness. In this study we examined three areas: 1) the application of speckle theory based on surface roughness to a three- dimensional media and a short coherence length light source, 2) the effect that the optical system design has on the received speckle distribution, and the optimum optical system geometry for speckle analysis, and 3) the speckle properties of OCT images of tissue phantoms and various tissues such as fat and muscle. Results obtained from two methods of speckle analysis (texture analysis and speckle contrast) were compared for their ability to differentiate between tissue types.
Glaucoma is a set of diseases that cause optic nerve damage and visual field loss. The most important risk factor for the development of glaucoma is elevated intraocular pressure. One approach used to alleviate the pressure increase is to surgically install glaucoma implants. Two standard Ahmed and ten experimental ePTFE implants were evaluated in this study in rabbit eyes. The implants were imaged with optical coherence tomography (OCT) at 0, 7, 15, 30, and 90 days after implantation. Histology was collected at days 7, 15, 30, and 90 and compared to the OCT images. Preliminary analysis of images indicates that OCT can visualize the development of fibrous encapsulation of the implant, tissue erosion, fibrin accumulation in the implant tube, and tube position in the anterior chamber. A new OCT handheld probe was developed to facilitate in vivo imaging in rabbit eye studies. The OCT probe consists of a mechanical scaffold designed to allow the imaging fiber to be held in a fixed position with respect to the rabbit eye, with minimal anesthesia. A piezo electric lateral scanning device allows the imaging fiber to be scanned across the tissue so that 2D images may be acquired.
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