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.
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.
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.
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