Speckle noise removal in ultrasound images using Particle Swarm Optimization technique

2011 ◽  
Author(s):  
S. Mohamed Mansoor Roomi ◽  
R. B. Jayanthi Rajee
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Speckle Noise ◽  
Noise Removal ◽  
Ultrasound Images ◽  
Swarm Optimization ◽  
Particle Swarm Optimization Technique

scholarly journals Assessment of Joint Space in Knee Osteoarthritis using Particle Swarm Optimization Technique

2019 ◽  
Vol 8 (12) ◽  
pp. 5818-5823
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Automated Analysis ◽  
Joint Space ◽  
Noise Removal ◽  
Swarm Optimization ◽  
Overweight People ◽  
Joint Disorder ◽  
Particle Swarm Optimization Technique

Osteoarthritis (OA) is one of the most common joint disorder which is debility seen in elderly & overweight people which affects the cartilage of bone joints like knee, feet, hip, and spine. In OA usually, cartilage is ruptured due to the kneading of bones with each other which will end up causing severe pain. In this condition, it is necessary to analyze the severity of OA which involves various medical imaging and clinical examination techniques. In this paper, automated analysis and detection of OA are proposed by calculating the thickness of cartilage which also helps to effectively detect and analyze the abnormalities in bone structures. Where we have considered various knee X-ray images. Initially, preprocessing and noise removal is performed. Further by implementing Particle Swarm Optimization (PSO) segmentation and thresholding, the specified knee region is cropped and analyzed to calculate the thickness of cartilage to detect the presence of OA.

scholarly journals Assessment of Joint Space in Knee Osteoarthritis using Particle Swarm Optimization Technique

2019 ◽  
Vol 9 (1) ◽  
pp. 502-507
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Automated Analysis ◽  
Joint Space ◽  
Noise Removal ◽  
Swarm Optimization ◽  
Overweight People ◽  
Joint Disorder ◽  
Particle Swarm Optimization Technique

Osteoarthritis (OA) is one of the most common joint disorder which is debility seen in elderly & overweight people which affects the cartilage of bone joints like knee, feet, hip, and spine. In OA usually, cartilage is ruptured due to the kneading of bones with each other which will end up causing severe pain. In this condition, it is necessary to analyze the severity of OA which involves various medical imaging and clinical examination techniques. In this paper, automated analysis and detection of OA are proposed by calculating the thickness of cartilage which also helps to effectively detect and analyze the abnormalities in bone structures. Where we have considered various knee X-ray images. Initially, preprocessing and noise removal is performed. Further by implementing Particle Swarm Optimization (PSO) segmentation and thresholding, the specified knee region is cropped and analyzed to calculate the thickness of cartilage to detect the presence of OA.

Materialized View Selection Using Swap Operator Based Particle Swarm Optimization

2021 ◽  
Vol 13 (1) ◽  
pp. 58-73
Author(s):  
Amit Kumar ◽  
T. V. Vijay Kumar
Keyword(s):  
Particle Swarm Optimization ◽  
Data Warehouse ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Selection Problem ◽  
Data Repository ◽  
Swarm Optimization ◽  
View Selection ◽  
Particle Swarm Optimization Technique ◽  
Swap Operator

The data warehouse is a key data repository of any business enterprise that stores enormous historical data meant for answering analytical queries. These queries need to be processed efficiently in order to make efficient and timely decisions. One way to achieve this is by materializing views over a data warehouse. An n-dimensional star schema can be mapped into an n-dimensional lattice from which Top-K views can be selected for materialization. Selection of such Top-K views is an NP-Hard problem. Several metaheuristic algorithms have been used to address this view selection problem. In this paper, a swap operator-based particle swarm optimization technique has been adapted to address such a view selection problem.

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