AIMC Topic: Radiographic Image Enhancement

Clear Filters Showing 101 to 106 of 106 articles

Detection of concealed cars in complex cargo X-ray imagery using Deep Learning.

Journal of X-ray science and technology Jan 1, 2017
BACKGROUND: Non-intrusive inspection systems based on X-ray radiography techniques are routinely used at transport hubs to ensure the conformity of cargo content with the supplied shipping manifest. As trade volumes increase and regulations become mo...
Radiography Radiographic Image Enhancement Security Measures Humans Machine Learning Automobiles
View on PubMed DOI

Comparison of Knowledge-based Iterative Model Reconstruction and Hybrid Reconstruction Techniques for Liver CT Evaluation of Hypervascular Hepatocellular Carcinoma.

Journal of computer assisted tomography Jan 1, 2016
OBJECTIVE: The purpose of this work was to evaluate the image quality, lesion conspicuity, and dose reduction provided by knowledge-based iterative model reconstruction (IMR) in computed tomography (CT) of the liver compared with hybrid iterative rec...
Humans Republic of Korea Female Reproducibility of Results Machine Learning Algorithms Computer Simulation Pattern Recognition, Automated Multidetector Computed Tomography Radiographic Image Enhancement Knowledge Bases Neovascularization, Pathologic Sensitivity and Specificity Aged Liver Neoplasms Male Adult Liver Carcinoma, Hepatocellular Middle Aged Radiographic Image Interpretation, Computer-Assisted Models, Biological
View on PubMed DOI

Histogram-Based Discrimination of Intravenous Contrast in Abdominopelvic Computed Tomography.

Journal of computer assisted tomography Jan 1, 2016
OBJECTIVE: The aim of this study was to evaluate the accuracy of fully automated machine learning methods for detecting intravenous contrast in computed tomography (CT) studies of the abdomen and pelvis.
Radiographic Image Enhancement Radiography, Abdominal Pelvis Administration, Intravenous Algorithms Tomography, X-Ray Computed Reproducibility of Results Logistic Models Contrast Media Humans Artificial Intelligence
View on PubMed DOI

Restricted Boltzmann machines based oversampling and semi-supervised learning for false positive reduction in breast CAD.

Bio-medical materials and engineering Jan 1, 2015
The false-positive reduction (FPR) is a crucial step in the computer aided detection system for the breast. The issues of imbalanced data distribution and the limitation of labeled samples complicate the classification procedure. To overcome these ch...
Supervised Machine Learning Reproducibility of Results Pattern Recognition, Automated Stochastic Processes Data Interpretation, Statistical False Positive Reactions Breast Neoplasms Sensitivity and Specificity Algorithms Radiographic Image Enhancement Radiographic Image Interpretation, Computer-Assisted Humans Models, Statistical Computer Simulation Neural Networks, Computer Sample Size Female
View on PubMed DOI

Sampling from Determinantal Point Processes for Scalable Manifold Learning.

Information processing in medical imaging : proceedings of the ... conference Jan 1, 2015
High computational costs of manifold learning prohibit its application for large datasets. A common strategy to overcome this problem is to perform dimensionality reduction on selected landmarks and to successively embed the entire dataset with the N...
Models, Statistical Sample Size Artificial Intelligence Data Interpretation, Statistical Humans Algorithms Signal Processing, Computer-Assisted Pattern Recognition, Automated Computer Simulation Tomography, X-Ray Computed Radiographic Image Interpretation, Computer-Assisted Sensitivity and Specificity Radiographic Image Enhancement Reproducibility of Results Imaging, Three-Dimensional Head and Neck Neoplasms
View on PubMed DOI

Multi-scale Convolutional Neural Networks for Lung Nodule Classification.

Information processing in medical imaging : proceedings of the ... conference Jan 1, 2015
We investigate the problem of diagnostic lung nodule classification using thoracic Computed Tomography (CT) screening. Unlike traditional studies primarily relying on nodule segmentation for regional analysis, we tackle a more challenging problem on ...
Radiographic Image Enhancement Pattern Recognition, Automated Radiography, Thoracic Tomography, X-Ray Computed Reproducibility of Results Sensitivity and Specificity Humans Lung Neoplasms Neural Networks, Computer Solitary Pulmonary Nodule Algorithms Radiographic Image Interpretation, Computer-Assisted
View on PubMed DOI
Popular Topics
Recent Journals