AIMC Topic: Image Processing, Computer-Assisted

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Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks.

Medical physics
PURPOSE: Delineation of the clinical target volume (CTV) and organs at risk (OARs) is very important for radiotherapy but is time-consuming and prone to inter-observer variation. Here, we proposed a novel deep dilated convolutional neural network (DD...

A segmentation of brain MRI images utilizing intensity and contextual information by Markov random field.

Computer assisted surgery (Abingdon, England)
BACKGROUND AND OBJECTIVE: Image segmentation is a preliminary and fundamental step in computer aided magnetic resonance imaging (MRI) images analysis. But the performance of most current image segmentation methods is easily depreciated by noise in MR...

Web-Enabled Distributed Health-Care Framework for Automated Malaria Parasite Classification: an E-Health Approach.

Journal of medical systems
Web-enabled e-healthcare system or computer assisted disease diagnosis has a potential to improve the quality and service of conventional healthcare delivery approach. The article describes the design and development of a web-based distributed health...

Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank.

NeuroImage
UK Biobank is a large-scale prospective epidemiological study with all data accessible to researchers worldwide. It is currently in the process of bringing back 100,000 of the original participants for brain, heart and body MRI, carotid ultrasound an...

Automatic labeling of MR brain images through extensible learning and atlas forests.

Medical physics
PURPOSE: Multiatlas-based method is extensively used in MR brain images segmentation because of its simplicity and robustness. This method provides excellent accuracy although it is time consuming and limited in terms of obtaining information about n...

Deep neural networks for texture classification-A theoretical analysis.

Neural networks : the official journal of the International Neural Network Society
We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space f...

A parallel MR imaging method using multilayer perceptron.

Medical physics
PURPOSE: To reconstruct MR images from subsampled data, we propose a fast reconstruction method using the multilayer perceptron (MLP) algorithm.

Learning-based deformable registration for infant MRI by integrating random forest with auto-context model.

Medical physics
PURPOSE: Accurately analyzing the rapid structural evolution of human brain in the first year of life is a key step in early brain development studies, which requires accurate deformable image registration. However, due to (a) dynamic appearance and ...

Parzen neural networks: Fundamentals, properties, and an application to forensic anthropology.

Neural networks : the official journal of the International Neural Network Society
A novel, unsupervised nonparametric model of multivariate probability density functions (pdf) is introduced, namely the Parzen neural network (PNN). The PNN is intended to overcome the major limitations of traditional (either statistical or neural) p...

Melanoma segmentation based on deep learning.

Computer assisted surgery (Abingdon, England)
Malignant melanoma is one of the most deadly forms of skin cancer, which is one of the world's fastest-growing cancers. Early diagnosis and treatment is critical. In this study, a neural network structure is utilized to construct a broad and accurate...