AIMC Topic: Image Enhancement

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Radiomics features on non-contrast-enhanced CT scan can precisely classify AVM-related hematomas from other spontaneous intraparenchymal hematoma types.

European radiology Oct 10, 2018
OBJECTIVE: To investigate the classification ability of quantitative radiomics features extracted on non-contrast-enhanced CT (NECT) image for discrimination of AVM-related hematomas from those caused by other etiologies.
Hematoma Brain Diseases Image Enhancement Male Middle Aged Adolescent Child Machine Learning Tomography, X-Ray Computed Female Reproducibility of Results Aged Aged, 80 and over Young Adult Adult Humans
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Application of fractal theory and fuzzy enhancement in ultrasound image segmentation.

Medical & biological engineering & computing Oct 9, 2018
The manuscript describes an ultrasound image segmentation technique based on the fractional Brownian motion (FBM) model. Here, the ultrasound images are first enhanced using a fuzzy-based technique, and later the FBM model is employed to obtain the f...
Humans Image Enhancement Carotid Arteries Algorithms Fractals Ultrasonography Gallbladder Fuzzy Logic Liver
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Aiding the Digital Mammogram for Detecting the Breast Cancer Using Shearlet Transform and Neural Network.

Asian Pacific journal of cancer prevention : APJCP Sep 26, 2018
Objective: Breast Cancer is the most invasive disease and fatal disease next to lung cancer in human. Early detection of breast cancer is accomplished by X-ray mammography. Mammography is the most effective and efficient technique used for detection ...
Neural Networks, Computer Algorithms Databases, Factual Female Humans Image Interpretation, Computer-Assisted Image Enhancement Wavelet Analysis Diagnostic Errors Breast Neoplasms Diagnosis, Computer-Assisted Mammography
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Scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction: Database-free deep learning for fast imaging.

Magnetic resonance in medicine Sep 18, 2018
PURPOSE: To develop an improved k-space reconstruction method using scan-specific deep learning that is trained on autocalibration signal (ACS) data.
Magnetic Resonance Imaging Brain Mapping Heart Image Interpretation, Computer-Assisted Male Middle Aged Deep Learning Image Processing, Computer-Assisted Image Enhancement Radionuclide Imaging Phantoms, Imaging Humans Neural Networks, Computer Algorithms Databases, Factual Brain Female Young Adult Adult
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Machine learning improves classification of preclinical models of pancreatic cancer with chemical exchange saturation transfer MRI.

Magnetic resonance in medicine Sep 17, 2018
PURPOSE: We sought to assess whether machine learning-based classification approaches can improve the classification of pancreatic tumor models relative to more simplistic analysis methods, using T relaxation, CEST, and DCE MRI.
Image Interpretation, Computer-Assisted Mice, SCID Image Enhancement Normal Distribution Principal Component Analysis Area Under Curve Hypoxia Reproducibility of Results Sensitivity and Specificity Animals Cell Line, Tumor Image Processing, Computer-Assisted Mice Magnetic Resonance Imaging Pancreatic Neoplasms Humans Machine Learning Algorithms Female
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Machine learning based on multi-parametric magnetic resonance imaging to differentiate glioblastoma multiforme from primary cerebral nervous system lymphoma.

European journal of radiology Sep 14, 2018
PURPOSE: To evaluate the performance of a machine learning method based on texture features in multi-parametric magnetic resonance imaging (MRI) to differentiate a glioblastoma multiforme (GBM) from a primary cerebral nervous system lymphoma (PCNSL).
Aged Adult Brain Neoplasms Male Magnetic Resonance Imaging Diagnosis, Differential Middle Aged Glioblastoma ROC Curve Machine Learning Image Enhancement Logistic Models Humans Brain Lymphoma Retrospective Studies Female
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Neural multi-atlas label fusion: Application to cardiac MR images.

Medical image analysis Jul 31, 2018
Multi-atlas segmentation approach is one of the most widely-used image segmentation techniques in biomedical applications. There are two major challenges in this category of methods, i.e., atlas selection and label fusion. In this paper, we propose a...
Image Interpretation, Computer-Assisted Pattern Recognition, Automated Image Enhancement Algorithms Magnetic Resonance Imaging Humans Anatomic Landmarks Heart Ventricles Neural Networks, Computer
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Medical breast ultrasound image segmentation by machine learning.

Ultrasonics Jul 18, 2018
Breast cancer is the most commonly diagnosed cancer, which alone accounts for 30% all new cancer diagnoses for women, posing a threat to women's health. Segmentation of breast ultrasound images into functional tissues can aid tumor localization, brea...
Pattern Recognition, Automated Image Enhancement Humans Breast Neoplasms Neural Networks, Computer Imaging, Three-Dimensional Machine Learning Ultrasonography, Mammary Algorithms Image Interpretation, Computer-Assisted Female
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Improving resolution of MR images with an adversarial network incorporating images with different contrast.

Medical physics May 18, 2018
PURPOSE: The routine MRI scan protocol consists of multiple pulse sequences that acquire images of varying contrast. Since high frequency contents such as edges are not significantly affected by image contrast, down-sampled images in one contrast may...
Neural Networks, Computer Machine Learning Magnetic Resonance Imaging Image Enhancement
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Differential Data Augmentation Techniques for Medical Imaging Classification Tasks.

AMIA ... Annual Symposium proceedings. AMIA Symposium Apr 16, 2018
Data augmentation is an essential part of training discriminative Convolutional Neural Networks (CNNs). A variety of augmentation strategies, including horizontal flips, random crops, and principal component analysis (PCA), have been proposed and sho...
Radiology Information Systems Mammography Data Visualization Diagnostic Imaging Humans Datasets as Topic Neural Networks, Computer Image Enhancement Deep Learning
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