Browsing by Author "Yamanakkanavar, Y."

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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. Â© 2016 IEEE.
Automatic Segmentation of Intima Media Complex in Carotid Ultrasound Images Using Support Vector Machine
(Springer Verlag, 2019) Yamanakkanavar, Y.; Hema Sai Teja, A.; Narasimhadhan, A.V.
The intima media thickness (IMT) of common carotid artery is a reliable measure of cardiovascular diseases. The quantification of IMT is the biomarker for clinical diagnosis of the risk of stroke. For robust measurement of IMT, the ultrasound carotid images must be free of speckle noise. To reduce the effect of speckle noise in the carotid ultrasound image, we propose to use Bayesian least square estimation filter. In addition, the enhancement step based on total variation-L1(TV-L1) norm is performed to improve the robustness. Further more, we present a fully automated region of interest and segmentation of intima media complex based on support vector machine. The quantitative evaluation is carried out on 49 carotid ultrasound images. The proposed algorithm is compared with gradient-based methods like model based, dynamic programming, snake algorithm, and classifier-based segmentation using a neural network algorithm. The performance of the experimental result shows that the proposed method is robust in quantifying the IMT in carotid ultrasound images. © 2018, King Fahd University of Petroleum & Minerals.
Automatic Segmentation of Intima Media Complex in Common Carotid Artery using Adaptive Wind Driven Optimization
(Institute of Electrical and Electronics Engineers Inc., 2019) Madipalli, P.; Kotta, S.; Dadi, H.; Yamanakkanavar, Y.; Asha, C.S.; Narasimhadhan, A.V.
Cardiovascular diseases have been one of the leading causes of death and have been increasing in much of the developing world. Atherosclerosis, the accumulation of plaque on artery walls is the major for cardiovascular diseases. This is diagnosed by measuring the thickness of IMC of common carotid artery (CCA) in ultrasound images. In this paper, we present a completely automatic technique for segmentation of IMC in ultrasound images of CCA. The image is segmented using adaptive wind driven optimization (AWDO) technique. The denoising filter based on Bayesian least square approach and a robust enhancement technique is used in the pre-processing stage. The proposed method is evaluated on 60 ultrasound images and is compared with the state-of-The-Art methods. The experimental results show that the proposed method yields better results as compared to other methods. Â© 2018 IEEE.
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
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. Â© Springer Nature Singapore Pte Ltd. 2018.
Segmentation of intima media complex from carotid ultrasound images using wind driven optimization technique
(Elsevier Ltd, 2018) Yamanakkanavar, Y.; Madipalli, P.; Rajan, J.; Kumar, P.K.; Narasimhadhan, A.V.
Cardiovascular diseases are the third leading cause of death worldwide. The primitive indication of the possible onset of a cardiovascular disease is atherosclerosis, which is the accumulation of plaque on the arterial wall. The intima-media thickness (IMT) of the common carotid artery is an early marker of the development of cardiovascular disease. The computation of the IMT and the delineation of the carotid plaque are significant predictors for the clinical diagnosis of the risk of stroke. For a robust diagnosis, carotid ultrasound images must be free from speckle noise. To address this problem, we use state-of-the-art despeckling and enhancement methods in this work. Many edge-based methods for IMT estimation have been proposed to overcome the limitations of manual segmentation. In this paper, we present a fully automated region-of-interest (ROI) extraction and a threshold-based segmentation of the intima media complex (IMC) using a wind driven optimization (WDO) technique. A quantitative evaluation is carried out on 90 carotid ultrasound images of two different datasets. The obtained results are compared with those of state-of-the-art techniques such as a model-based approach, a dynamic programming method, and a snake segmentation method. The experimental analysis shows that the proposed method is robust in measuring the IMT in carotid ultrasound images. © 2017 Elsevier Ltd