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In this paper, we present a fully automatic method to quantify Tree-in-Bud (TIB) patterns for respiratory tract infections. The proposed quantification method is based on our previous effort to detect and track TIB patterns with a computer assisted detection (CAD) system [9]. In addition to accurately identifying TIB on CT, quantifying TIB is important for measuring the volume of affected lung as...
In this paper we propose a novel skeletal muscle segmentation method driven from discrete optimization. We introduce a graphical model that is able to automatically determine appropriate seed positions with respect to the different muscle classes. This is achieved by taking into account the expected local visual and geometric properties of the seeds through a pair-wise Markov Random Field. The outcome...
Dynamic images of functional activity in the brain offer the potential to measure connectivity between regions of interest. We want to measure causal activity between regions of interest (ROIs) with signals recorded from multiple channels or voxels in each ROI. Previous methods, such as Granger causality, look for causality between individual time series; hence, they suffer from local interactions...
In this paper, we present a novel method that fuses, through a graph matching, segmentation of the blood vessels in contrast-enhanced images with segmentation of the guide-wires in the fluoroscopic images. This is achieved through a bottom up approach that first extracts local geometric primitives of interest in both images. Fusion between two graphs built with these primitives is performed through...
In this paper, we present an automatic method to segment the kidney in 3D contrast-enhanced ultrasound (CEUS) images. This modality has lately benefited of an increasing interest for diagnosis and intervention planning, as it allows to visualize blood flow in real-time harmlessly for the patient. Our method is composed of two steps: first, the kidney is automatically localized by a novel robust ellipsoid...
The application of multi-tag protocols in fluorescence microscopy allows the visualization of a large number (> 10) of molecules (i. e. proteins) in a sample (like a tissue section). However, the analysis of such high dimensional bioimages is a difficult task for most of the labs, since software solutions for particular data mining steps are difficult to use or just not available. In this paper...
Paradigm-free mapping enables to map the hæmodynamic response in space and time without prior knowledge of the timing of the underlying neuronal events (i.e., no stimulation paradigm). Such deconvolution approach can take advantage of modern sparsity-promoting regularization. Here we extend this concept using structured sparsity approaches in order to gain robustnesss against model mismatch. Specifically,...
We present a color segmentation approach based on a two-dimensional color map derived from the input image. Pathologists stain tissue biopsies with various colored dyes to see the expression of biomarkers. In these images, because of color variation due to inconsistencies in experimental procedures and lighting conditions, the segmentation used to analyze biological features is usually ad-hoc. Many...
Standard Bayesian analysis of event-related functional Magnetic Resonance Imaging (fMRI) data usually assumes that all delivered stimuli possibly generate a BOLD response everywhere in the brain although activation is likely to be induced by only some of them in specific brain areas. Criteria are not always available to select the relevant conditions or stimulus types (e.g. visual, auditory, etc.)...
This paper proposes a novel pose-invariant segmentation approach for left ventricle in 3D CT images. The proposed formulation is modular with respect to the image support (i.e. landmarks, edges and regional statistics). The prior is represented as a third-order Markov Random Field (MRF) where triplets of points result to a low-rank statistical prior while inheriting invariance to global transformations...
Expert physicians are able to attain good Alzheimer's Disease (AD) diagnostic accuracy, relying on visual inspection of Positron Emission Tomography (PET) images only. Nevertheless, computerized methods have been implemented with similar or even better performance. We investigate the potential of the physician's experienced visual inspection to guide feature selection, in an automatic classification...
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