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Item Comparison of edge detection algorithms in the framework of despeckling carotid ultrasound images based on bayesian estimation approach(Springer Verlag service@springer.de, 2018) Yamanakkanavar, Y.; Narasimhadhan, A.V.Common carotid artery (CCA) ultrasound with estimation of Intima Media Thickness (IMT) is the safe and non-invasive technique for predicting the cardiovascular risks. The precise quantification of IMT is useful for evaluating the risk of cardiovascular disease. The presence of speckle noise in carotid ultrasound image reduces the quality of the image and automatic human interpretation. Carotid ultrasound images have multiplicative speckle noise and it is difficult remove compared to the additive noises. The speckle removal filters have a greater restriction in edges and characteristics preservation. In this paper, we propose an extension of our earlier work with a fully automated Region of Interest (ROI) extraction and speckle denoising using optimized bayesian least square estimation (BLSE) approach followed by edge detection. The objective of the paper is to reduce the speckle noise in the extracted ROI of carotid ultrasound images using state-of-art denoising techniques and then followed by edge detection techniques and compared them with the edges extracted by these edge operators of ground truth image. The proposed algorithm experiments with 50 B-mode carotid ultrasound images. Experimental analysis shows that proposed method achieves better results as compared to other edge detection methods in terms of structural similarity Index Map (SSIM), correlation of coefficient (CoC), peak signal to noise ratio (PSNR) and mean square error (MSE) measures. Based on results, proposed work more effective in terms of visual inspection and detail preservation in carotid ultrasound images. © Springer Nature Singapore Pte Ltd. 2018.Item Carotid wall segmentation in longitudinal ultrasound images using structured random forest(Elsevier Ltd, 2018) Yamanakkanavar, Y.; Asha, C.S.; Teja A, H.S.; Narasimhadhan, A.V.Edge detection is a primary image processing technique used for object detection, data extraction, and image segmentation. Recently, edge-based segmentation using structured classifiers has been receiving increasing attention. The intima media thickness (IMT) of the common carotid artery is mainly used as a primitive indicator for the development of cardiovascular disease. For efficient measurement of the IMT, we propose a fast edge-detection technique based on a structured random forest classifier. The accuracy of IMT measurement is degraded owing to the speckle noise found in carotid ultrasound images. To address this issue, we propose the use of a state-of-the-art denoising method to reduce the speckle noise, followed by an enhancement technique to increase the contrast. Furthermore, we present a novel approach for an automatic region of interest extraction in which a pre-trained structured random forest classifier algorithm is applied for quantifying the IMT. The proposed method exhibits IMTmean ± standard deviation of 0.66mm ± 0.14, which is closer to the ground truth value 0.67mm ± 0.15 as compared to the state-of-the-art techniques. © 2018 Elsevier Ltd