Conference Papers

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    Performance analysis of despeckling filters for retinal optical coherence tomography images
    (Institute of Electrical and Electronics Engineers Inc., 2018) Gupta, P.K.; Lal, S.; Husain, F.
    This paper presents performance analysis of different despeckling filters used for denoising of the optical coherence tomography (OCT) Images. Currently OCT imaging is one of the best technique used in biomedical application to detect the abnormality in the human eye. OCT images normally suffer from granular patterns called speckle noise. Speckle noise is an inherent property of an OCT images which affects the visual quality of the images, hence difficult to diagnosis the patients. Therefore, speckle noise reduction from the OCT images is an important prerequisite, whenever OCT imaging is used for diagnosis. Here, a comparative analysis of different despeckling filters used for the denoising of OCT images is presented. The speckle noise intensity is depends on the various imaging system parameters and on the different structure representations used for the image tissues. A denoising technique is to be designed in such a way that it should be able to reduce the speckle noise from the OCT images while preserve the tissues and fine details of the images. © 2018 IEEE.
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    A Novel Deep Learning Approach for the Removal of Speckle Noise from Optical Coherence Tomography Images Using Gated Convolution–Deconvolution Structure
    (Springer Science and Business Media Deutschland GmbH, 2020) Menon, S.N.; Vineeth Reddy, V.B.; Yeshwanth, A.; Anoop, B.N.; Rajan, J.
    Optical coherence tomography (OCT) is an imaging technique widely used to image retina. Speckle noise in OCT images generally degrades the quality of the OCT images and makes the clinical diagnosis tedious. This paper proposes a new deep neural network despeckling scheme called gated convolution–deconvolution structure (GCDS). The robustness of the proposed method is evaluated on the publicly available OPTIMA challenge dataset and Duke dataset. The quantitative analysis based on PSNR shows that the results of the proposed method are superior to other state-of-the-art methods. The application of the proposed method for segmenting retinal cyst from OPTIMA challenge dataset was also studied. © 2020, Springer Nature Singapore Pte Ltd.