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Single-particle tracking is computationally a challenging problem, and usually solved with local methods. Local methods suffer from defects in the image data or in the detection of particles, such as temporal disappearing of particles. A particle tracking method has to provide a solution also to real disappearing and appearing of particles as a result of merging and splitting. Here, we present an...
We propose a computational model to simulate the physical processes implied in the Magnetic Resonance Images formation in vascularised tissues. A combined model of MRI acquisition and blood flow is presented. The blood flow patterns are modeled using the Lattice Boltzmann Method, and the magnetic resonance experiments follow the Bloch equation. A new algorithm has been developed to compute the local...
The random walker method [1] has many nice characteristics for 3D image segmentation. However, it is computational expensive and slow due to a massive linear system to be solved. In this work, we take advantage of the probabilistic output from the random walker method applied to a small downsampled image. By using the original image and seeds information, a novel edge-preserving method is introduced...
Multivariate analysis of structural and functional brain imaging data can be used to produce network models of interaction or similarity between different brain structures. Graph partitioning methods can then be used to identify distinct subnetworks that may provide insight into the organization of the human brain. Although several efficient partitioning algorithms have been proposed, and their properties...
In this paper we present a dictionary-based framework for the reconstruction of a field of ensemble average propagators (EAPs), given a high angular resolution diffusion MRI data set. Existing techniques often consider voxel-wise reconstruction of the EAP field thereby leading to a noisy reconstruction across the field. We present a dictionary learning framework for achieving a smooth EAP reconstruction...
A weakly supervised image classification framework is presented in this paper. Given reference images marked by clinicians as relevant or irrelevant, we learn to automatically detect relevant patterns, i.e. patterns that only appear in relevant images. After training, relevant patterns are sought in unseen images in order to classify each image as relevant or irrelevant. No manual segmentations are...
The disruption of normal function and connectivity of neural circuits is common across many diseases and disorders of the brain. This disruptive effect can be studied and analyzed using the brain's complex functional and structural connectivity network. Complex network measures from the field of graph theory have been used for this purpose in the literature. In this paper we have introduced a new...
This article presents a segmentation approach based on random walk (RW) method to delineate tumors having inhomogeneous activity distributions of 18FDG on Positron Emission Tomography (PET) images. Based on the original algorithm of RW [1], we propose an improved approach using an adaptive parameter instead of a fixed one and integrating probability densities of label into the system of linear equations...
Thin-plate splines are probably the most often used technique for landmark-based registration with radial basis functions. However, a disadvantage is that the locality of the transformation cannot be controlled. We introduce an extension of thin-plate splines which enables to control the spatial influence of acting forces and thus the locality of the transformation. Our approach is based on a new...
In this paper, we propose a new graph-based approach to address the problem of cytological computer-aided diagnostic. Such approach uses our previously introduced formalism of PdEs-based morphology and geometric diffusion on graphs. The approach is illustrated through two applications in cytopathology (involving Feulgen and Papanicolaou colorations), with examples of nucleus extraction and classification.
Current label fusion methods enhance multi-atlas segmentation by locally weighting the contribution of the atlases according to their similarity to the target volume after registration. However, these methods cannot handle voxel intensity inconsistencies between the atlases and the target image, which limits their application across modalities or even across MRI datasets due to differences in image...
Sulcal folds (sulci) on the cortical surface are important landmarks of interest for investigating brain development and disease. Accurate and automatic delineation of the sulci is a challenging problem due to substantial variability in their shapes across populations. We present a geodesic curvature flow method for an automatic and accurate delineation of sulcal curves. We assume as input an atlas...
Gradient Vector Flow has become a popular method to recover medial information in medical imaging, in particular for vessels centerline extraction. This renewed interest has been motivated by its ability to process gray-scale images without prior segmentation. However, another interesting property lies in the diffusion process used to solve the underlying variational problem. We propose a method to...
Diffusion-weighted images are increasingly being employed in conjunction with T2-weighted images for accurate diagnosis of cervical cancer. However, these images suffer from eddy-current related and magnetic field inhomogeneity induced distortions. This paper presents an investigation into the reverse gradient technique to correct distortions in diffusion-weighted echo-planar images of human cervix...
Histologic imaging plays an important role in discriminating cancerous tissues of several body organs. However, the human histopathological examinations may be subjective and error prone, because of the complexity of the appearances of the histologic texture. These limitations can be overcome by adopting quantitative computational methods with human histopathological examination routines. This study...
Many biomedical applications require the enhancement of coherent flow-like curvilinear structures in images. This can be accomplished in a natural way by adopting anisotropic diffusion filtering to local texture analysis by means of the structure tensor. Here we propose a contrast independent anisotropic diffusion filtering of curvilinear structures based on a novel concept: the Phase Congruency Tensor...
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