scholarly journals Quantification of Epicardial and Thoracic Adipose Tissue using WOA Optimized CNN

2019 ◽  
Vol 8 (9S2) ◽  
pp. 720-724
Keyword(s):  
Adipose Tissue ◽  
Optimization Algorithm ◽  
Selection Process ◽  
Heart Diseases ◽  
Epicardial Fat ◽  
Fat Depots ◽  
Krill Herd ◽  
Whale Optimization ◽  
Fully Automatic ◽  
Paracardial Fat

Cardiac fat depots are associated with the heart diseases. Epicardial fat and thoracic fat plays the major role in the development of cardiovascular disease. The increased thickness of the epicardial and thoracic fat leads to several diseases such as metabolic syndrome, coronary atherosclerosis, etc. It is necessary to quantify the epicardial adipose tissue and thoracic adipose tissue. There are different imaging and assessing techniques for epicardial and thoracic adipose tissue quantification. These tissues can be quantified automatically or manually from the CT and MRI cardiac scans. The quantification of the epicardial fat and thoracic fat requires segmentation of these fats by various segmentation methods and then they are quantified. This project proposes the fully automatic segmentation and quantification of the epicardial and thoracic adipose tissues from the cardiac CT scan images using the krill herd optimization algorithm and fuzzy c-means segmentation algorithm. The whale optimization algorithm performs the feature selection process. The fuzzy cmeans algorithm is used for the segmentation process by means of clustering which segments the epicardial fat and paracardial adipose tissue(EAT &PAT) from the input image. The segmented epicardial and paracardial fat region are then used for the quantification process which provides the epicardial and thoracic fat volume. The thoracic fat is the combination of the epicardial and paracardial fat. This proposed system is implemented by using the MATLAB code. The proposed system is simple, fully automatic and produces accurate results.

Estudo de algoritmos de otimização bio-inspirados aplica à segmentação de imagens

2020 ◽  
Author(s):  
◽  
N. T. Saito
Keyword(s):  
Optimization Algorithm ◽  
Differential Evolution Algorithm ◽  
Grey Wolf Optimizer ◽  
Extensive System ◽  
Bee Colony ◽  
Krill Herd ◽  
Grasshopper Optimization Algorithm ◽  
Whale Optimization ◽  
Nonadditive Entropy ◽  
Grasshopper Optimization

Image segmentation is one of the first steps within the framework for processing scenes. Among the main existing techniques, we highlight the histogram-based binarization, which due to the simplicity of understanding and low computational complexity is one of the most used methods. However, for a multi-threshold process, this method becomes computationally costly. To minimize this problem, optimization algorithms are used to find the best thresholds. Recently, several algorithms inspired by nature have been proposed in a generic way in the area of combinatorial optimization and obtained excellent results, among which we highlight the more traditional ones such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution Algorithm (DE), Artificial Bee Colony (ABC), Firefly Algorithm (FA) and Krill Herd (KH). This work shows a comparison between some of these algorithms and more recent algorithms, from 2014, as Grey Wolf Optimizer (GWO), Elephant Herding Optimization (EHO), Whale Optimization Algorithm (WOA), Grasshopper Optimization Algorithm (GOA) and Harris Hawks Optmization (HHO) . This work compared the thresholds obtained by 7 bio-inspired algorithms in a base composed of 100 images with 1 single object provided by the Weizmann Institute of Science (WIS). The comparison was made using consolidated metrics like Dice/Jaccard and PSNR, as well the recent Hxyz. In the experiments were used the extensive system as an objective function (Kapurs´ Method). Still in the proposal of this experiment, the extensive system was compared with a Tsallis nonadditive entropy, with the Super-extensive system being configured with q ? [0.1, 0.2, . . . 0.9] and the Sub-extensive system with q ? [1.1, 1.2, . . . 1.9]. The image Database contains 100 images with only 1 object on scene. The results show that the Krill Herd (KH) algorithm was the winning algorithm in 35% of executions according to the PSNR metric, 28% in the Dice/Jaccard metric and 35% on the Hxyz metric. The extensive system had the best overall performance and was responsible for the best threshold of 54 images according to the metric PSNR, 30 according to the metric Dice/Jaccard and 39 according to the Hxyz metric

The Role of Inflammation in Epicardial Adipose Tissue in Heart Diseases

2018 ◽  
Vol 24 (3) ◽  
pp. 297-309 ◽  
Author(s):  
Zdenek Matloch ◽  
Anna Cinkajzlova ◽  
Milos Mraz ◽  
Martin Haluzik
Keyword(s):  
Adipose Tissue ◽  
Epicardial Adipose Tissue ◽  
Heart Diseases ◽  
Left Ventricular ◽  
Epicardial Fat ◽  
Cellular Interactions ◽  
Endocrine Organ ◽  
Adipose Tissue Depot ◽  
Electrocardiographic Abnormalities

Epicardial adipose tissue is not only a specific adipose tissue depot but also an active endocrine organ producing numerous substances with an important role in the development of obesity-related heart diseases. It is located between myocardium and visceral pericardium and consists predominantly of adipocytes, immunocompetent cells, ganglia and interconnecting nerve branches. Several studies documented a positive correlation between pericardial and epicardial fat and left ventricular hypertrophy and septal thickening, leading to diastolic dysfunction, electrocardiographic abnormalities and facilitating cardiac failure. The cellular cross-talks between epicardial fat and myocardium may include both the vasocrine and the paracrine mechanisms. Adipokines secreted from epicardial adipose tissue, vascular and stromal cells diffuse into interstitial fluid crossing the adventitia, media and intima and modulate cardiac function and cardiomyocyte phenotype and survival. In this article, we review the significance of epicardial adipose tissue and its association with cardiovascular diseases, cellular interactions between epicardial fat and myocardium, secretions of adipokines and inflammatory mediators and a potential of epicardial fat as a therapeutic target for the prevention of obesity-related heart diseases.

scholarly journals Circulating CRP Levels Are Associated with Epicardial and Visceral Fat Depots in Women with Metabolic Syndrome Criteria

2019 ◽  
Vol 20 (23) ◽  
pp. 5981 ◽  
Author(s):  
Federico Carbone ◽  
Maria Stefania Lattanzio ◽  
Silvia Minetti ◽  
Anna Maria Ansaldo ◽  
Daniele Ferrara ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Adipose Tissue ◽  
Epicardial Fat ◽  
Enzyme Linked Immunosorbent Assay ◽  
P Value ◽  
Ectopic Fat ◽  
Men And Women ◽  
Fat Depots ◽  
Hs Crp

Sexual dimorphism accounts for significant differences in adipose tissue mass and distribution. However, how the crosstalk between visceral and ectopic fat depots occurs and which are the determinants of ectopic fat expansion and dysfunction remains unknown. Here, we focused on the impact of gender in the crosstalk between visceral and epicardial fat depots and the role of adipocytokines and high-sensitivity C-reactive protein (hs-CRP). A total of 141 outward patients (both men and women) with one or more defining criteria for metabolic syndrome (MetS) were consecutively enrolled. For all patients, demographic and clinical data were collected and ultrasound assessment of visceral adipose tissue (VFth) and epicardial fat (EFth) thickness was performed. Hs-CRP and adipocytokine levels were assessed by enzyme-linked immunosorbent assay (ELISA). Men were characterized by increased VFth and EFth (p-value < 0.001 and 0.014, respectively), whereas women showed higher levels of adiponectin and leptin (p-value < 0.001 for both). However, only in women VFth and EFth significantly correlated between them (p = 0.013) and also with leptin (p < 0.001 for both) and hs-CRP (p = 0.005 and p = 0.028, respectively). Linear regression confirmed an independent association of both leptin and hs-CRP with VFth in women, also after adjustment for age and MetS (p = 0.012 and 0.007, respectively). In conclusion, men and women present differences in epicardial fat deposition and systemic inflammation. An intriguing association between visceral/epicardial fat depots and chronic low-grade inflammation also emerged. In women Although a further validation in larger studies is needed, these findings suggest a critical role of sex in stratification of obese/dysmetabolic patients.

Performance Evaluation of Fiber Wireless (FiWi) Access Network using Position Optimization of ONUs

2020 ◽  
Vol 10 ◽  
Author(s):  
Nitin Chouhan ◽  
Uma Rathore Bhatt ◽  
Raksha Upadhyay
Keyword(s):  
Power Consumption ◽  
Optimization Algorithm ◽  
Optical Network ◽  
Access Network ◽  
Whale Optimization Algorithm ◽  
Wireless Access ◽  
Delivery Ratio ◽  
Packet Delivery ◽  
Whale Optimization ◽  
Wireless Access Network

: Fiber Wireless Access Network is the blend of passive optical network and wireless access network. This network provides higher capacity, better flexibility, more stability and improved reliability to the users at lower cost. Network component (such as Optical Network Unit (ONU)) placement is one of the major research issues which affects the network design, performance and cost. Considering all these concerns, we implement customized Whale Optimization Algorithm (WOA) for ONU placement. Initially whale optimization algorithm is applied to get optimized position of ONUs, which is followed by reduction of number of ONUs in the network. Reduction of ONUs is done such that with fewer number of ONUs all routers present in the network can communicate. In order to ensure the performance of the network we compute the network parameters such as Packet Delivery Ratio (PDR), Total Time for Delivering the Packets in the Network (TTDPN) and percentage reduction in power consumption for the proposed algorithm. The performance of the proposed work is compared with existing algorithms (deterministic and centrally placed ONUs with predefined hops) and has been analyzed through extensive simulation. The result shows that the proposed algorithm is superior to the other algorithms in terms of minimum required ONUs and reduced power consumption in the network with almost same packet delivery ratio and total time for delivering the packets in the network. Therefore, present work is suitable for developing cost-effective FiWi network with maintained network performance.

scholarly journals Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2628
Author(s):  
Mengxing Huang ◽  
Qianhao Zhai ◽  
Yinjie Chen ◽  
Siling Feng ◽  
Feng Shu
Keyword(s):  
Optimization Algorithm ◽  
Edge Computing ◽  
Solution Set ◽  
Whale Optimization Algorithm ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Multi Objective ◽  
Whale Optimization ◽  
Artificial Immune System Algorithm

Computation offloading is one of the most important problems in edge computing. Devices can transmit computation tasks to servers to be executed through computation offloading. However, not all the computation tasks can be offloaded to servers with the limitation of network conditions. Therefore, it is very important to decide quickly how many tasks should be executed on servers and how many should be executed locally. Only computation tasks that are properly offloaded can improve the Quality of Service (QoS). Some existing methods only focus on a single objection, and of the others some have high computational complexity. There still have no method that could balance the targets and complexity for universal application. In this study, a Multi-Objective Whale Optimization Algorithm (MOWOA) based on time and energy consumption is proposed to solve the optimal offloading mechanism of computation offloading in mobile edge computing. It is the first time that MOWOA has been applied in this area. For improving the quality of the solution set, crowding degrees are introduced and all solutions are sorted by crowding degrees. Additionally, an improved MOWOA (MOWOA2) by using the gravity reference point method is proposed to obtain better diversity of the solution set. Compared with some typical approaches, such as the Grid-Based Evolutionary Algorithm (GrEA), Cluster-Gradient-based Artificial Immune System Algorithm (CGbAIS), Non-dominated Sorting Genetic Algorithm III (NSGA-III), etc., the MOWOA2 performs better in terms of the quality of the final solutions.

scholarly journals An image denoising method based on BP neural network optimized by improved whale optimization algorithm

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Chunzhi Wang ◽  
Min Li ◽  
Ruoxi Wang ◽  
Han Yu ◽  
Shuping Wang
Keyword(s):  
Neural Network ◽  
Image Denoising ◽  
Optimization Algorithm ◽  
Bp Neural Network ◽  
Weight Coefficient ◽  
Whale Optimization Algorithm ◽  
Denoising Method ◽  
Training Time ◽  
Whale Optimization ◽  
City Construction

AbstractAs an important part of smart city construction, traffic image denoising has been studied widely. Image denoising technique can enhance the performance of segmentation and recognition model and improve the accuracy of segmentation and recognition results. However, due to the different types of noise and the degree of noise pollution, the traditional image denoising methods generally have some problems, such as blurred edges and details, loss of image information. This paper presents an image denoising method based on BP neural network optimized by improved whale optimization algorithm. Firstly, the nonlinear convergence factor and adaptive weight coefficient are introduced into the algorithm to improve the optimization ability and convergence characteristics of the standard whale optimization algorithm. Then, the improved whale optimization algorithm is used to optimize the initial weight and threshold value of BP neural network to overcome the dependence in the construction process, and shorten the training time of the neural network. Finally, the optimized BP neural network is applied to benchmark image denoising and traffic image denoising. The experimental results show that compared with the traditional denoising methods such as Median filtering, Neighborhood average filtering and Wiener filtering, the proposed method has better performance in peak signal-to-noise ratio.

Whale optimization algorithm based on lateral inhibition for image matching and vision-guided AUV docking

2020 ◽  
pp. 1-12
Author(s):  
Zheping Yan ◽  
Jinzhong Zhang ◽  
Jialing Tang
Keyword(s):  
Lateral Inhibition ◽  
Optimization Algorithm ◽  
Image Matching ◽  
Autonomous Underwater Vehicle ◽  
Optimal Solution ◽  
Search Space ◽  
Whale Optimization Algorithm ◽  
Inhibition Mechanism ◽  
Whale Optimization ◽  
Accuracy And Stability

The accuracy and stability of relative pose estimation of an autonomous underwater vehicle (AUV) and a target depend on whether the characteristics of the underwater image can be accurately and quickly extracted. In this paper, a whale optimization algorithm (WOA) based on lateral inhibition (LI) is proposed to solve the image matching and vision-guided AUV docking problem. The proposed method is named the LI-WOA. The WOA is motivated by the behavior of humpback whales, and it mainly imitates encircling prey, bubble-net attacking and searching for prey to obtain the globally optimal solution in the search space. The WOA not only balances exploration and exploitation but also has a faster convergence speed, higher calculation accuracy and stronger robustness than other approaches. The lateral inhibition mechanism can effectively perform image enhancement and image edge extraction to improve the accuracy and stability of image matching. The LI-WOA combines the optimization efficiency of the WOA and the matching accuracy of the LI mechanism to improve convergence accuracy and the correct matching rate. To verify its effectiveness and feasibility, the WOA is compared with other algorithms by maximizing the similarity between the original image and the template image. The experimental results show that the LI-WOA has a better average value, a higher correct rate, less execution time and stronger robustness than other algorithms. The LI-WOA is an effective and stable method for solving the image matching and vision-guided AUV docking problem.

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