Conference Papers
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Item Assessment of speckle denoising in ultrasound carotid images using least square Bayesian estimation approach(Institute of Electrical and Electronics Engineers Inc., 2017) Yamanakkanavar, Y.; Asha, C.S.; Narasimhadhan, A.V.The ultrasound carotid images affected by speckle noise, which highly reduces the image quality and effects the human interpretation. Speckle removal is substantial and critical step for preprocessing of ultrasound carotid images. For robust diagnosis, the carotid images must be free of noise and clear in clinical practices. The carotid ultrasound images have multiplicative noise and is very difficult to remove as compared to additive noise. To address this issue we propose to use Bayesian least square estimation in the logarithmic space. The proposed algorithm is tested on 50 ultrasound B mode carotid images and the performance of the algorithm is compared with the existing algorithms like Median filter, Speckle Reducing Anisotropic Diffusion(SRAD), Non Local Mean (NLM) filter, Total Variation (TV), Detail Preserving Anisotropic Diffusion(DPAD) filter, Lee filter, Frost filter and Wavelet filter. Experimental result shows that proposed algorithm capable of achieving better results as compared to the other methods in terms of signal to noise ratio (SNR), peak signal to noise ratio (PSNR), Correlation of Coefficient (CoC), Structural Similarity Index Map (SSIM) and Image Quality Index(IQI) measures. As per visual inspection concerned the proposed approach is more effective in terms of suppression of noise and image enhancement. © 2016 IEEE.Item 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.Item 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.