AIMC Topic: Image Processing, Computer-Assisted

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Deep image reconstruction from human brain activity.

PLoS computational biology
The mental contents of perception and imagery are thought to be encoded in hierarchical representations in the brain, but previous attempts to visualize perceptual contents have failed to capitalize on multiple levels of the hierarchy, leaving it cha...

A proposal of prior probability-oriented clustering in feature encoding strategies.

PloS one
Codebook-based feature encodings are a standard framework for image recognition issues. A codebook is usually constructed by clusterings, such as the k-means and the Gaussian Mixture Model (GMM). A codebook size is an important factor to decide the t...

Dominant-Current Deep Learning Scheme for Electrical Impedance Tomography.

IEEE transactions on bio-medical engineering
OBJECTIVE: Deep learning has recently been applied to electrical impedance tomography (EIT) imaging. Nevertheless, there are still many challenges that this approach has to face, e.g., targets with sharp corners or edges cannot be well recovered when...

Image-based velocity estimation of rock using Convolutional Neural Networks.

Neural networks : the official journal of the International Neural Network Society
Digital images of rock samples have been using extensively in Digital Rock Physics (DRP) to evaluate physical parameters of rock such as permeability, P- and S-wave velocities and formation factor. The parameters are numerically computed by simulatio...

Blind Noisy Image Quality Assessment Using Sub-Band Kurtosis.

IEEE transactions on cybernetics
Noise that afflicts natural images, regardless of the source, generally disturbs the perception of image quality by introducing a high-frequency random element that, when severe, can mask image content. Except at very low levels, where it may play a ...

Nasopharyngeal carcinoma segmentation based on enhanced convolutional neural networks using multi-modal metric learning.

Physics in medicine and biology
Multi-modality examinations have been extensively applied in current clinical cancer management. Leveraging multi-modality medical images can be highly beneficial for automated tumor segmentation as they provide complementary information that could m...

Self-prior image-guided MRI reconstruction with dictionary learning.

Medical physics
PURPOSE: A novel method, named self-prior image-guided MRI reconstruction with dictionary learning (SPIDLE), is developed to improve the performance of MR imaging with high acceleration rates. "self-prior" means that the prior image is obtained from ...

MRI-based attenuation correction for brain PET/MRI based on anatomic signature and machine learning.

Physics in medicine and biology
Deriving accurate attenuation maps for PET/MRI remains a challenging problem because MRI voxel intensities are not related to properties of photon attenuation and bone/air interfaces have similarly low signal. This work presents a learning-based meth...

Identifying facial phenotypes of genetic disorders using deep learning.

Nature medicine
Syndromic genetic conditions, in aggregate, affect 8% of the population. Many syndromes have recognizable facial features that are highly informative to clinical geneticists. Recent studies show that facial analysis technologies measured up to the ca...

Artificial intelligence for the real-time classification of intrapapillary capillary loop patterns in the endoscopic diagnosis of early oesophageal squamous cell carcinoma: A proof-of-concept study.

United European gastroenterology journal
BACKGROUND: Intrapapillary capillary loops (IPCLs) represent an endoscopically visible feature of early squamous cell neoplasia (ESCN) which correlate with invasion depth - an important factor in the success of curative endoscopic therapy. IPCLs visu...