Mohammad R. N. Avanaki

OCT skin image enhancement through attenuation compensation

The enhancement of optical coherence tomography (OCT) skin images can help dermatologists investigate the morphologic information of the images more effectively. In this paper, we propose an enhancement algorithm with the stages that includes speckle reduction, skin layer detection, and attenuation compensation. A weighted median filter is designed to reduce the level of speckle while preserving the contrast. A novel skin layer detection technique is then applied to outline the main skin layers: stratum corneum, epidermis, and dermis. The skin layer detection algorithm does not make any assumption about the structure of the skin. A model of the light attenuation is then used to estimate the attenuation coefficient of the stratum corneum, epidermis, and dermis layers. The performance of the algorithm has been evaluated qualitatively based on visual evaluation and quantitatively using two no-reference quality metrics: signal-to-noise ratio and contrast-to-noise ratio. The enhancement algorithm is tested on 35 different skin OCT images, which show significant improvements in the quality of the images, especially in the structures at deeper levels.

Spatial Compounding Algorithm for Speckle Reduction of Dynamic Focus OCT Images

Optical coherence tomography is capable of imaging the microstructures within tissues. To preserve the transverse resolution at all imaging depths, we implement a dynamic focusing scheme. To improve the quality of images further, a simple speckle reduction scheme is employed which uses the vibration introduced by the translation stage used for axial scanning. A spatial compounding technique is developed based on co-registration followed by an averaging algorithm. We conclude that the degree of speckle reduction achieved is worth the expense of more complicated processing required.

Image quality improvement in optical coherence tomography using Lucy–Richardson deconvolution algorithm

Optical coherence tomography (OCT) has the potential for skin tissue characterization due to its high axial and transverse resolution and its acceptable depth penetration. In practice, OCT cannot reach the theoretical resolutions due to imperfections of some of the components used. One way to improve the quality of the images is to estimate the point spread function (PSF) of the OCT system and deconvolve it from the output images. In this paper, we investigate the use of solid phantoms to estimate the PSF of the imaging system. We then utilize iterative Lucy-Richardson deconvolution algorithm to improve the quality of the images. The performance of the proposed algorithm is demonstrated on OCT images acquired from a variety of samples, such as epoxy-resin phantoms, fingertip skin and basaloid larynx and eyelid tissues.

Speckle reduction using an artificial neural network algorithm

This paper presents an algorithm for reducing speckle noise from optical coherence tomography (OCT) images using an artificial neural network (ANN) algorithm. The noise is modeled using Rayleigh distribution with a noise parameter, sigma, estimated by the ANN. The input to the ANN is a set of intensity and wavelet features computed from the image to be processed, and the output is an estimated sigma value. This is then used along with a numerical method to solve the inverse Rayleigh function to reduce the noise in the image. The algorithm is tested successfully on OCT images of Drosophila larvae. It is demonstrated that the signal-to-noise ratio and the contrast-to-noise ratio of the processed images are increased by the application of the ANN algorithm in comparison with the respective values of the original images.

Quantitative evaluation of scattering in optical coherence tomography skin images using the extended Huygens–Fresnel theorem

An optical properties extraction algorithm is developed based on enhanced Huygens-Fresnel light propagation theorem, to extract the scattering coefficient of a specific region in an optical coherence tomography (OCT) image. The aim is to quantitatively analyze the OCT images. The algorithm is evaluated using a set of phantoms with different concentrations of scatterers, designed based on Mie theory. The algorithm is then used to analyze basal cell carcinoma and healthy eyelid tissues, demonstrating distinguishable differences in the scattering coefficient between these tissues. In this study, we have taken advantage of the simplification introduced by the utilization of a dynamic focus OCT system. This eliminates the need to deconvolve the reflectivity profile with the confocal gate profile, as the sensitivity of the OCT system is constant throughout the axial range.

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 μs, absorption coefficient μa, 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.

Investigation of basal cell carcinoma using dynamic focus optical coherence tomography

Optical coherence tomography (OCT) is becoming a popular modality for skin tumor diagnosis and assessment of tumor size and margin status. We conducted a number of imaging experiments on periocular basal cell carcinoma (BCC) specimens using an OCT configuration. This configuration employs a dynamic focus (DF) procedure where the coherence gate moves synchronously with the peak of the confocal gate, which ensures better signal strength and preservation of transversal resolution from all depths. A DF-OCT configuration is used to illustrate morphological differences between the BCC and its surrounding healthy skin in OCT images. The OCT images are correlated with the corresponding histology images. To the best of our knowledge, this is the first paper to look at DF-OCT imaging in examining periocular BCC.

Algorithm for Excitation Optimization of Fabry–Pérot Filters Used in Swept Sources

We investigate the improvement in the nonlinearity of a conventional wavelength swept laser source on the basis of a fiber Fabry-Pérot tunable filter using a well-established optimization method, simulated annealing (SA). The signal driving the filter is constructed from many short ramps of different slopes that are interconnected. The values of the slopes are optimized through the SA algorithm to achieve maximum amplitude for the Fourier transformed peaks of the photodetected interferometric signal.

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.

Sensor-less aberration correction in optical imaging systems using blind optimization

The imperfection of optical devices in an optical imaging system deteriorates wavefront which results in aberration. This reduces the optical signal to noise ratio of the imaging system and the quality of the produced images. Adaptive optics composed of wavefront sensor (WFS) and deformable mirror (DM) is a straightforward solution for this problem. The need for a WFS in an AO system, raises the cost of the overall system, and there are also instances when they cannot be used, such as in microscopy. Moreover stray reflections from lens surfaces affect the performance of the WFS. In this paper, we describe a blind optimization technique with an in-expensive electronics without using the WFS to correct the aberration in order to achieve better quality images. The correction system includes an electromagnetic DM from Imagine, Mirao52d, with 52 actuators which are controlled by particle swarm optimization (PSO) algorithm. The results of the application of simulated annealing (SA), and genetic algorithm (GA) techniques that we have implemented in the sensor-less AO are used for comparison.