Speckle Reduction with Attenuation Compensation for Skin OCT Images Enhancement

The enhancement of skin image in optical coherence tomography (OCT) imaging can help dermatologists to investigate tissue layers more accurately, hence the more efficient diagnosis. In this paper, we propose an image enhancement technique including speckle reduction, attenuation compensation and cleaning to improve the quality of OCT skin images. A weighted median filter is designed to reduce the level of speckle noise while preserving the contrast. A novel border detection technique is designed to outline the main skin layers, stratum corneum, epidermis and dermis. A model of the light attenuation is then used to estimate the absorption coefficient of epidermis and dermis layers and compensate the brightness of the structures at deeper levels. The undesired part of the image is removed using a simple cleaning algorithm. The performance of the algorithm has been evaluated visually and numerically using the commonly used no-reference quality metrics. The results shows an improvement in the quality of the images. Keywords: Optical coherence tomography (OCT), Skin, Image enhancement, Speckle reduction, Attenuation compensation

Aging display's effect on interpretation of digital pathology slides

It is our conjecture that the variability of colors in a pathology image effects the interpretation of pathology cases, whether it is diagnostic accuracy, diagnostic confidence, or workflow efficiency. In this paper, digital pathology images are analyzed to quantify the perceived difference in color that occurs due to display aging, in particular a change in the maximum luminance, white point, and color gamut. The digital pathology images studied include diagnostically important features, such as the conspicuity of nuclei. Three different display aging models are applied to images: aging of luminance & chrominance, aging of chrominance only, and a stabilized luminance & chrominance (i.e., no aging). These display models and images are then used to compare conspicuity of nuclei using CIE deltaE2000, a perceptual color difference metric. The effect of display aging using these display models and images is further analyzed through a human reader study designed to quantify the effects from a clinical perspective. Results from our reader study indicate significant impact of aged displays on workflow as well as diagnosis as follow. As compared to the originals (no-aging), slides with the effect of aging simulated were significantly more difficult to read (p-value of 0.0005) and took longer to score (p-value of 0.02). Moreover, luminance+chrominance aging significantly reduced inter-session percent agreement of diagnostic scores (p-value of 0.0418)

Wavefront aberration correction in single mode fibre systems

A simple control loop system was built for the purpose of optimized compensation of wavefront aberrations correction using a micromachined deformable mirror controlled by PCI cards and sound card through simulated annealing algorithm implemented by using the integration of Visual C++ and MATLAB in MATLAB environment

Investigation of computer-based skin cancer detection using optical coherence tomography

In this paper, a procedure for computer-based detection of skin cancer, and in particular basal cell carcinoma (BCC), to assist dermatologists is investigated. The tissue compartments, which discriminate healthy and cancerous skins from an optical properties point of view, are studied. The application of an image-processing algorithm on a three-dimensional (3D) optical coherence tomography (OCT) image is explained. The algorithm finds the differences between healthy skin and BCC lesion by extracting scattering coefficient ?s, absorption coefficient ?a, and anisotropy factor g, from the 3D image of skin. We present the essential stages required to design a computer-based skin cancer detection algorithm using OCT and evaluate the performance of the algorithm using a phantom. The procedure to design the phantom and the choice of material used to model skin tissue based on BCC discriminators are discussed in detail

Denoising Based on Noise Parameter Estimation in Speckled OCT Images Using Neural Network

This paper presents a neural network based technique to denoise speckled images in optical coherence tomography (OCT). Speckle noise is modeled as Rayleigh distribution, and the neural network estimates the noise parameter, sigma. Twenty features from each image are used as input for training the neural network, and the sigma value is the single output of the network. The certainty of the trained network was more than 91 percent. The promising image results were assessed with three No-Reference metrics, with the Signal-to-Noise ratio of the denoised image being considerably increased

Photoacoustic Signal Enhancement: Towards Utilization of Low Energy Laser Diodes in Real-Time Photoacoustic Imaging

In practice, photoacoustic (PA) waves generated with cost-effective and low-energy laser diodes, are weak and almost buried in noise. Reconstruction of an artifact-free PA image from noisy measurements requires an effective denoising technique. Averaging is widely used to increase the signal-to-noise ratio (SNR) of PA signals; however, it is time consuming and in the case of very low SNR signals, hundreds to thousands of data acquisition epochs are needed. In this study, we explored the feasibility of using an adaptive and time-efficient filtering method to improve the SNR of PA signals. Our results show that the proposed method increases the SNR of PA signals more efficiently and with much fewer acquisitions, compared to common averaging techniques. Consequently, PA imaging is conducted considerably faster

An Intelligent Speckle Reduction Algorithm for Optical Coherence Tomography Images

Optical Coherence Tomography (OCT) offers three dimensional images of tissue microstructures. Although OCT imaging offers a promising high resolution method, due to the low coherent light source used in the configuration of OCT, OCT images suffers from an artefact called, speckle. Speckle deteriorates the image quality and effects image analysis algorithm such as segmentation and pattern recognition. We present a novel and intelligent speckle reduction algorithm to reduce speckle based on an ensemble framework of Multi-Layer Perceptron (MLP) neural networks. We tested the algorithm on images of retina obtained from a spectrometer-based Fourier-domain OCT system operating at 890 nm, and observed considerable improvement in the signal-to-noise ratio and contrast of the images

An intelligent speckle reduction algorithm for optical coherence tomography images

Optical Coherence Tomography (OCT) offers three dimensional images of tissue microstructures. Although OCT imaging offers a promising high resolution method, due to the low coherent light source used in the configuration of OCT, OCT images suffers from an artefact called, speckle. Speckle deteriorates the image quality and effects image analysis algorithm such as segmentation and pattern recognition. We present a novel and intelligent speckle reduction algorithm to reduce speckle based on an ensemble framework of Multi-Layer Perceptron (MLP) neural networks. We tested the algorithm on images of retina obtained from a spectrometerbased Fourier-domain OCT system operating at 890 nm, and observed considerable improvement in the signal-to-noise ratio and contrast of the images