A fast-Fourier-transform method of topography and interferometry is proposed. By computer processing of a noncontour type of fringe pattern, automatic discrimination is achieved between elevation and depression of the object or wave-front form, which has not been possible by the fringe-contour-generation techniques. The method has advantages over moire topography and conventional fringe-contour interferometry in both accuracy and sensitivity. Unlike fringe-scanning techniques, the method is easy to apply because it uses no moving components.

Fourier-transform method of fringe-pattern analysis for computer-based topography and interferometry

Diffusion Tensor Imaging (DTI) studies are increasingly popular among clinicians and researchers as they provide unique insights into brain network connectivity. However, in order to optimize the use of DTI, several technical and methodological aspects must be factored in. These include decisions on: acquisition protocol, artifact handling, data quality control, reconstruction algorithm, and visualization approaches, and quantitative analysis methodology. Furthermore, the researcher and/or clinician also needs to take into account and decide on the most suited software tool(s) for each stage of the DTI analysis pipeline. Herein, we provide a straightforward hitchhiker's guide, covering all of the workflow's major stages. Ultimately, this guide will help newcomers navigate the most critical roadblocks in the analysis and further encourage the use of DTI.

/pdf/a-hitchhiker-s-guide-to-diffusion-tensor-imaging-2d68vywjx8.pdf

A hitchhiker's guide to diffusion tensor imaging

We propose an integral concept for tractography to describe crossing and splitting fibre bundles based on the fibre orientation distribution function (ODF) estimated from high angular resolution diffusion imaging (HARDI). We show that in order to perform accurate probabilistic tractography, one needs to use a fibre ODF estimation and not the diffusion ODF. We use a new fibre ODF estimation obtained from a sharpening deconvolution transform (SDT) of the diffusion ODF reconstructed from q-ball imaging (QBI). This SDT provides new insight into the relationship between the HARDI signal, the diffusion ODF, and the fibre ODF. We demonstrate that the SDT agrees with classical spherical deconvolution and improves the angular resolution of QBI. Another important contribution of this paper is the development of new deterministic and new probabilistic tractography algorithms using the full multidirectional information obtained through use of the fibre ODF. An extensive comparison study is performed on human brain datasets comparing our new deterministic and probabilistic tracking algorithms in complex fibre crossing regions. Finally, as an application of our new probabilistic tracking, we quantify the reconstruction of transcallosal fibres intersecting with the corona radiata and the superior longitudinal fasciculus in a group of eight subjects. Most current diffusion tensor imaging (DTI)-based methods neglect these fibres, which might lead to incorrect interpretations of brain functions.

/pdf/deterministic-and-probabilistic-tractography-based-on-2qapg3sv3g.pdf

Deterministic and Probabilistic Tractography Based on Complex Fibre Orientation Distributions

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Two Dimensional Phase Unwrapping Theory Algorithms And Software

A general framework for solving image inverse problems is introduced in this paper. The approach is based on Gaussian mixture models, estimated via a computationally efficient MAP-EM algorithm. A dual mathematical interpretation of the proposed framework with structured sparse estimation is described, which shows that the resulting piecewise linear estimate stabilizes the estimation when compared to traditional sparse inverse problem techniques. This interpretation also suggests an effective dictionary motivated initialization for the MAP-EM algorithm. We demonstrate that in a number of image inverse problems, including inpainting, zooming, and deblurring, the same algorithm produces either equal, often significantly better, or very small margin worse results than the best published ones, at a lower computational cost.

Solving Inverse Problems with Piecewise Linear Estimators: From Gaussian Mixture Models to Structured Sparsity

Hypergraphs.- Generalization of Two Hypergraphs. Algorithm of Calculation of the Greatest Sub-Hypergraph Common to Two Hypergraphs Annotated by Semantic Information.- Recognition and Detection.- Recognition of Polymorphic Patterns in Parameterized Graphs for 3D Building Reconstruction.- A Graph-Based Representation to Detect Linear Features.- Edge Detection as Finding the Minimum Cost Path in a Graph.- Matching.- Subgraph Transformations for the Inexact Matching of Attributed Relational Graphs.- Efficient Graph Matching for Video Indexing.- Isomorphism between Strong Fuzzy Relational Graphs Based on k-Formulae.- Segmentation.- A Graph Structure for Grey Value and Texture Segmentation.- Discrete Maps: a Framework for Region Segmentation Algorithms.- Image Sequence Segmentation by a Single Evolutionary Graph Pyramid.- Implementation Problems.- Dual Graph Contraction with LEDA.- Implementing Image Analysis with a Graph-Based Parallel Computing Model.- Representation.- The Frontier-Region Graph.- Optimization Techniques on Pixel Neighborhood Graphs for Image Processing.

/pdf/graph-based-representations-in-pattern-recognition-2o4wv1wflx.pdf

Graph-Based Representations in Pattern Recognition

http://www.cim.mcgill.ca/~shape/publications/medima11.pdf

Recent advances in diffusion MRI modeling: Angular and radial reconstruction

Color image quantization, the process of reducing the number of colors in a digital color image, has been widely studied for the last fifteen years. The different steps of clustering methods are studied. The methods are compared step by step and some optimizations of the algorithms and data structures are given. A new color space called H1H2H3 is introduced, which improves the quantization heuristics. A low-cost quantization scheme is proposed.

Comparison and optimization of methods of color image quantization

https://tschumperle.users.greyc.fr/publications/tschumperle_media09.pdf

Efficient and robust computation of PDF features from diffusion MR signal

This paper proposes a new neural network based on SPD manifold learning for skeleton-based hand gesture recognition. Given the stream of hand’s joint positions, our approach combines two aggregation processes on respectively spatial and temporal domains. The pipeline of our network architecture consists in three main stages. The first stage is based on a convolutional layer to increase the discriminative power of learned features. The second stage relies on different architectures for spatial and temporal Gaussian aggregation of joint features. The third stage learns a final SPD matrix from skeletal data. A new type of layer is proposed for the third stage, based on a variant of stochastic gradient descent on Stiefel manifolds. The proposed network is validated on two challenging datasets and shows state-of-the-art accuracies on both datasets.

/pdf/a-neural-network-based-on-spd-manifold-learning-for-skeleton-290f1v6rv9.pdf

Luc Brun

Papers

Graph-Based Representations in Pattern Recognition

Recent advances in diffusion MRI modeling: Angular and radial reconstruction

Comparison and optimization of methods of color image quantization

Efficient and robust computation of PDF features from diffusion MR signal

A Neural Network Based on SPD Manifold Learning for Skeleton-Based Hand Gesture Recognition