AIMC Journal:
Medical image analysis
Showing 681 to 690 of 699 articles
Efficient multi-atlas abdominal segmentation on clinically acquired CT with SIMPLE context learning.
Medical image analysis
May 21, 2015
Abdominal segmentation on clinically acquired computed tomography (CT) has been a challenging problem given the inter-subject variance of human abdomens and complex 3-D relationships among organs. Multi-atlas segmentation (MAS) provides a potentially...
MR image synthesis by contrast learning on neighborhood ensembles.
Medical image analysis
May 18, 2015
Automatic processing of magnetic resonance images is a vital part of neuroscience research. Yet even the best and most widely used medical image processing methods will not produce consistent results when their input images are acquired with differen...
A hybrid method for airway segmentation and automated measurement of bronchial wall thickness on CT.
Medical image analysis
May 14, 2015
Inflammatory and infectious lung diseases commonly involve bronchial airway structures and morphology, and these abnormalities are often analyzed non-invasively through high resolution computed tomography (CT) scans. Assessing airway wall surfaces an...
Locality-constrained Subcluster Representation Ensemble for lung image classification.
Medical image analysis
Mar 24, 2015
In this paper, we propose a new Locality-constrained Subcluster Representation Ensemble (LSRE) model, to classify high-resolution computed tomography (HRCT) images of interstitial lung diseases (ILDs). Medical images normally exhibit large intra-clas...
Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the Lung Image Database Consortium and Image Database Resource Initiative dataset.
Medical image analysis
Feb 23, 2015
We present new pulmonary nodule segmentation algorithms for computed tomography (CT). These include a fully-automated (FA) system, a semi-automated (SA) system, and a hybrid system. Like most traditional systems, the new FA system requires only a sin...
Humans
Neural Networks, Computer
Regression Analysis
Data Interpretation, Statistical
Radiographic Image Interpretation, Computer-Assisted
Radiographic Image Enhancement
Pattern Recognition, Automated
Algorithms
Reproducibility of Results
Databases, Factual
Lung Neoplasms
Radiography, Thoracic
Sensitivity and Specificity
Tomography, X-Ray Computed
Solitary Pulmonary Nodule
Structured patch model for a unified automatic and interactive segmentation framework.
Medical image analysis
Jan 29, 2015
We present a novel interactive segmentation framework incorporating a priori knowledge learned from training data. The knowledge is learned as a structured patch model (StPM) comprising sets of corresponding local patch priors and their pairwise spat...
Learning-based prediction of gestational age from ultrasound images of the fetal brain.
Medical image analysis
Jan 3, 2015
We propose an automated framework for predicting gestational age (GA) and neurodevelopmental maturation of a fetus based on 3D ultrasound (US) brain image appearance. Our method capitalizes on age-related sonographic image patterns in conjunction wit...
Trainable COSFIRE filters for vessel delineation with application to retinal images.
Medical image analysis
Sep 3, 2014
Retinal imaging provides a non-invasive opportunity for the diagnosis of several medical pathologies. The automatic segmentation of the vessel tree is an important pre-processing step which facilitates subsequent automatic processes that contribute t...
A survey of deep-learning-based radiology report generation using multimodal inputs.
Medical image analysis
Jul 1, 2025
Automatic radiology report generation can alleviate the workload for physicians and minimize regional disparities in medical resources, therefore becoming an important topic in the medical image analysis field. It is a challenging task, as the comput...
Next-generation surgical navigation: Marker-less multi-view 6DoF pose estimation of surgical instruments.
Medical image analysis
Jul 1, 2025
State-of-the-art research of traditional computer vision is increasingly leveraged in the surgical domain. A particular focus in computer-assisted surgery is to replace marker-based tracking systems for instrument localization with pure image-based 6...
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