The algorithms below are ready to be downloaded. Biomedical Imaging Group. EPFL

Available Algorithms

http://bigwww.epfl.ch/algorithms.html

http://www.google.com/codesearch/p?hl=fr&sa=N&cd=4&ct=rc#M0QGbzpICpo/kybic/thesis/&q=MRI%20mesh%20matlab

The algorithms below are ready to be downloaded. They are generally written in JAVA or in ANSI-C, either by students or by the members of the Biomedical Imaging Group.Please contact the author of the algorithms if you have a specific question.
JAVA: Plug-ins for ImageJ
JAVA classes are usually meant to be integrated into the public-domain software ImageJ.
bullet Drop Shape Analysis. New method based on B-spline snakes (active contours) for measuring high-accuracy contact angles of sessile drops.
bullet Extended Depth of Focus. The extended depth of focus is a image-processing method to obtain in focus microscopic images of 3D objects and organisms. We freely provide a software as a plugin of ImageJ to produce this in-focus image and the corresponding height map of z-stack images.
bullet Fractional spline wavelet transform. This JAVA package computes the fractional spline wavelet transform of a signal or an image and its inverse.
bullet Image Differentials. This JAVA class for ImageJ implements 6 operations based on the spatial differentiation of an image. It computes the pixel-wise gradient, Laplacian, and Hessian. The class exports public methods for horizontal and vertical gradient and Hessian operations (for those programmers who wish to use them in their own code).
bullet MosaicJ. This JAVA class for ImageJ performs the assembly of a mosaic of overlapping individual images, or tiles. It provides a semi-automated solution where the initial rough positioning of the tiles must be performed by the user, and where the final delicate adjustments are performed by the plugin.
bullet NeuronJ. This Java class for ImageJ was developed to facilitate the tracing and quantification of neurites in two-dimensional (2D) fluorescence microscopy images. The tracing is done interactively based on the specification of end points; the optimal path is determined on the fly from the optimization of a cost function using Dijkstra’s shortest-path algorithm. The procedure also takes advantage of an improved ridge detector implemented by means of a steerable filterbank.
bullet PixFRET. The ImageJ plug-in PixFRET allows to visualize the FRET between two partners in a cell or in a cell population by computing pixel by pixel the images of a sample acquired in three channels.
bullet Point Picker. This JAVA class for ImageJ allows the user to pick some points in an image and to save the list of pixel coordinates as a text file. It is also possible to read back the text file so as to restore the display of the coordinates.
bullet Resize. This ImageJ plugin changes the size of an image to any dimension using either interpolation, or least-squares approximation.
bullet SheppLogan. The purpose of this ImageJ plugin is to generate sampled versions of the Shepp-Logan phantom. Their size can be tuned.
bullet Snakuscule. The purpose of this ImageJ plugin is to detect circular bright blobs in images and to quantify them. It allows one to keep record of their location and size.
bullet SpotTracker Single particle tracking over noisy images sequence. SpotTracker is a robust and fast computational procedure for tracking fluorescent markers in time-lapse microscopy. The algorithm is optimized for finding the time-trajectory of single particles in very noisy image sequences. The optimal trajectory of the particle is extracted by applying a dynamic programming optimization procedure.
bullet StackReg. This JAVA class for ImageJ performs the recursive registration (alignment) of a stack of images, so that each slice acts as template for the next one. This plugin requires that TurboReg is installed.
bullet Steerable feature detectors. This ImageJ plugin implements a series of optimized contour and ridge detectors. The filters are steerable and are based on the optimization of a Canny-like criterion. They have a better orientation selectivity than the classical gradient or Hessian-based detectors.
bullet TurboReg. This JAVA class for ImageJ performs the registration (alignment) of two images. The registration criterion is least-squares. The geometric deformation model can be translational, conformal, affine, and bilinear.
bullet UnwarpJ. This JAVA class for ImageJ performs the elastic registration (alignment) of two images. The registration criterion includes a vector-spline regularization term to constrain the deformation to be physically realistic. The deformation model is made of cubic splines, which ensures smoothness and versatility.
ANSI C
Most often, the ANSI-C pieces of code are not a complete program, but rather an element in a library of routines.
bullet Affine transformation. This ANSI-C routine performs an affine transformation on an image or a volume. It proceeds by resampling a continuous spline model.
bullet Registration. This ANSI-C routine performs the registration (alignment) of two images or two volumes. The criterion is least-squares. The geometric deformation model can be translational, rotational, and affine.
bullet Shifted linear interpolation. This ANSI-C program illustrates how to perform shifted linear interpolation.
bullet Spline interpolation. This ANSI-C program illustrates how to perform spline interpolation, including the computation of the so-called spline coefficients.
bullet Spline pyramids. This software package implements the basic REDUCE and EXPAND operators for the reduction and enlargement of signals and images by factors of two based on polynomial spline representation of the signal.
Others
bullet E-splines. A Mathematica package is made available for the symbolic computation of exponential spline related quantities: B-splines, Gram sequence, Green function, and localization filter.
bullet Fractional spline wavelet transform. A MATLAB package is available for computing the fractional spline wavelet transform of a signal or an image and its inverse.
bullet Fractional spline and fractals. A MATLAB package is available for computing the fractional smoothing spline estimator of a signal and for generating fBms (fractional Brownian motion). This spline estimator provides the minimum mean squares error reconstruction of a fBm (or 1/f-type signal) corrupted by additive noise.
bullet Hex-splines : a novel spline family for hexagonal lattices. A Maple 7.0 worksheet is available for obtaining the analytical formula of any hex-spline (any order, regular, non-regular, derivatives, and so on).
bullet MLTL deconvolution : This Matlab package implements the MultiLevel Thresholded Landweber (MLTL) algorithm, an accelerated version of the TL algorithm that was specifically developped for deconvolution problems with a wavelet-domain regularization.
bullet OWT SURE-LET Denoising : This Matlab package implements the interscale orthonormal wavelet thresholding algorithm based on the SURE-LET (Stein’s Unbiased Risk Estimate/Linear Expansion of Thresholds) principle.
bullet WSPM : Wavelet-based statistical parametric mapping, a toolbox for SPM that incorporates powerful wavelet processing and spatial domain statistical testing for the analysis of fMRI data.

1 Response to “The algorithms below are ready to be downloaded. Biomedical Imaging Group. EPFL”



  1. 1 The algorithms below are ready to be downloaded. Biomedical … | Information Mining R&D Rétrolien sur 30 avril 2010 à 3:04

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