AIMC Topic: Image Processing, Computer-Assisted

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Deep convolutional neural networks for multi-modality isointense infant brain image segmentation.

NeuroImage
The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development in health and disease. In the isointense stage (approximately 6-8 month...

Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
OBJECTIVE: We aimed to investigate if early revascularization in patients with suspected coronary artery disease can be effectively predicted by integrating clinical data and quantitative image features derived from perfusion SPECT (MPS) by machine l...

A computational model of the short-cut rule for 2D shape decomposition.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
We propose a new 2D shape decomposition method based on the short-cut rule. The short-cut rule originates from cognition research, and states that the human visual system prefers to partition an object into parts using the shortest possible cuts. We ...

Terrain Classification From Body-Mounted Cameras During Human Locomotion.

IEEE transactions on cybernetics
This paper presents a novel algorithm for terrain type classification based on monocular video captured from the viewpoint of human locomotion. A texture-based algorithm is developed to classify the path ahead into multiple groups that can be used to...

Transfer learning improves supervised image segmentation across imaging protocols.

IEEE transactions on medical imaging
The variation between images obtained with different scanners or different imaging protocols presents a major challenge in automatic segmentation of biomedical images. This variation especially hampers the application of otherwise successful supervis...

Diagnostic performance of bone scintigraphy analyzed by three artificial neural network systems.

Annals of nuclear medicine
OBJECTIVE: The accuracy of bone scintigraphy analyzed by computer-assisted diagnosis (CAD) software involving multiple artificial neural network (ANN) systems has not been well established.

Manifold Adaptive Label Propagation for Face Clustering.

IEEE transactions on cybernetics
In this paper, a novel label propagation (LP) method is presented, called the manifold adaptive label propagation (MALP) method, which is to extend original LP by integrating sparse representation constraint into regularization framework of LP method...

Learning Multiscale Active Facial Patches for Expression Analysis.

IEEE transactions on cybernetics
In this paper, we present a new idea to analyze facial expression by exploring some common and specific information among different expressions. Inspired by the observation that only a few facial parts are active in expression disclosure (e.g., aroun...

Region based stellate features combined with variable selection using AdaBoost learning in mammographic computer-aided detection.

Computers in biology and medicine
In this paper, a new method is developed for extracting so-called region-based stellate features to correctly differentiate spiculated malignant masses from normal tissues on mammograms. In the proposed method, a given region of interest (ROI) for fe...

Machine learning models for the differential diagnosis of vascular parkinsonism and Parkinson's disease using [(123)I]FP-CIT SPECT.

European journal of nuclear medicine and molecular imaging
PURPOSE: The study's objective was to develop diagnostic predictive models using data from two commonly used [(123)I]FP-CIT SPECT assessment methods: region-of-interest (ROI) analysis and whole-brain voxel-based analysis.