Automated Tracking of the Carotid Artery in Ultrasound Image Sequences Using a Self Organizing Neural Network

2010 ◽  
Author(s):  
Jimmy C. Azar ◽  
Hamed Hamid Muhammed
Keyword(s):  
Neural Network ◽  
Carotid Artery ◽  
Ultrasound Image ◽  
Image Sequences ◽  
Automated Tracking ◽  
Self Organizing

Optimal Feature Selection for Carotid Artery Image Segmentation Using Evolutionary Computation

2014 ◽  
Vol 626 ◽  
pp. 79-86 ◽  
Author(s):  
I. Mohammed Farook ◽  
S. Dhanalakshmi ◽  
V. Manikandan ◽  
C. Venkatesh
Keyword(s):  
Neural Network ◽  
Carotid Artery ◽  
Ultrasound Image ◽  
Intima Media Thickness ◽  
Ultrasound Images ◽  
Gray Level ◽  
Genetic Search ◽  
Heart Attacks ◽  
Hilbert Huang Transform ◽  
Occurrence Matrix

Atherosclerosis is hardening of arteries due to high blood pressure and high cholesterol. It causes heart attacks, stroke and peripheral vascular disease and is the major cause of death. In this paper we have attempted a method to identify the presence of plaque in carotid artery from ultrasound images. The ultrasound image is segmented using improved spatial Fuzzy c means algorithm to identify the presence of plaque in carotid artery. Spatial wavelet, Hilbert Huang Transform (HHT), Moment of Gray Level Histogram (MGLH) and Gray Level Co-occurrence Matrix (GLCM) features are extracted from ultrasound images and the feature set is reduced using genetic search process. The intima media thickness is measured using the proposed method. The IMT values are measured from the segmented image and trained using MLBPNN neural network. The neural network classifies the images into normal and abnormal.

Nonrigid motion compensation in B-mode and contrast enhanced ultrasound image sequences of the carotid artery

2014 ◽  
Author(s):  
Diego D. B. Carvalho ◽  
Zeynettin Akkus ◽  
Johan G. Bosch ◽  
Stijn C. H. van den Oord ◽  
Wiro J. Niessen ◽  
...  
Keyword(s):  
Carotid Artery ◽  
Motion Compensation ◽  
Ultrasound Image ◽  
Image Sequences ◽  
Nonrigid Motion ◽  
Contrast Enhanced Ultrasound ◽  
Contrast Enhanced

scholarly journals An Intelligent Evaluation Method to Analyze the Competitiveness of Airlines

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jun Zhao ◽  
Xumei Chen
Keyword(s):  
Neural Network ◽  
Prior Knowledge ◽  
Evaluation Method ◽  
Standard Sample ◽  
Reference Value ◽  
The Self ◽  
Index Data ◽  
Som Neural Network ◽  
Simulation Results ◽  
Self Organizing

An intelligent evaluation method is presented to analyze the competitiveness of airlines. From the perspective of safety, service, and normality, we establish the competitiveness indexes of traffic rights and the standard sample base. The self-organizing mapping (SOM) neural network is utilized to self-organize and self-learn the samples in the state of no supervision and prior knowledge. The training steps of high convergence speed and high clustering accuracy are determined based on the multistep setting. The typical airlines index data are utilized to verify the effect of the self-organizing mapping neural network on the airline competitiveness analysis. The simulation results show that the self-organizing mapping neural network can accurately and effectively classify and evaluate the competitiveness of airlines, and the results have important reference value for the allocation of traffic rights resources.

scholarly journals Deep learning-based recognition of cell structures in fluorescence microscopy sequences with respect to their morphology on cells infected with Marburg virus

2020 ◽  
Vol 6 (3) ◽  
pp. 501-504
Author(s):  
Dennis Schmidt ◽  
Andreas Rausch ◽  
Thomas Schanze
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Infected Cell ◽  
Network Architecture ◽  
Image Sequences ◽  
Marburg Virus ◽  
Microscopic Image ◽  
Cell Structures ◽  
Fast Recognition ◽  
Subviral Particles

AbstractThe Institute of Virology at the Philipps-Universität Marburg is currently researching possible drugs to combat the Marburg virus. This involves classifying cell structures based on fluoroscopic microscopic image sequences. Conventionally, membranes of cells must be marked for better analysis, which is time consuming. In this work, an approach is presented to identify cell structures in images that are marked for subviral particles. It could be shown that there is a correlation between the distribution of subviral particles in an infected cell and the position of the cell’s structures. The segmentation is performed with a "Mask-R-CNN" algorithm, presented in this work. The model (a region-based convolutional neural network) is applied to enable a robust and fast recognition of cell structures. Furthermore, the network architecture is described. The proposed method is tested on data evaluated by experts. The results show a high potential and demonstrate that the method is suitable.

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