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These are lecture notes which I used to study my course related to image processing. I found these medical image processing lecture notes very useful while studying for the subjects even at the last minute. They helped me in overcoming the fear related to image processing . These notes are meant for all those people who are just beginners and know nothing related to medical image processing. They are well framed and collected
Do download the notes from links given below
Recent Advances in Biomedical Imaging and Signal Analysis
M. Unser Proceedings of the Eighteenth European Signal Processing Conference (EUSIPCO’10), Ålborg, Denmark, August 23-27, 2010, EURASIP Fellow inaugural lecture.
Wavelets have the remarkable property of providing sparse representations of a wide variety of “natural” images. They have been applied successfully to biomedical image analysis and processing since the early 1990s.
In the first part of this talk, we explain how one can exploit the sparsifying property of wavelets to design more effective algorithms for image denoising and reconstruction, both in terms of quality and computational performance. This is achieved within a variational framework by imposing some ?1-type regularization in the wavelet domain, which favors sparse solutions. We discuss some corresponding iterative skrinkage-thresholding algorithms (ISTA) for sparse signal recovery and introduce a multi-level variant for greater computational efficiency. We illustrate the method with two concrete imaging examples: the deconvolution of 3-D fluorescence micrographs, and the reconstruction of magnetic resonance images from arbitrary (non-uniform) k-space trajectories.
In the second part, we show how to design new wavelet bases that are better matched to the directional characteristics of images. We introduce a general operator-based framework for the construction of steerable wavelets in any number of dimensions. This approach gives access to a broad class of steerable wavelets that are self-reversible and linearly parameterized by a matrix of shaping coefficients; it extends upon Simoncelli’s steerable pyramid by providing much greater wavelet diversity. The basic version of the transform (higher-order Riesz wavelets) extracts the partial derivatives of order N of the signal (e.g., gradient or Hessian). We also introduce a signal-adapted design, which yields a PCA-like tight wavelet frame. We illustrate the capabilities of these new steerable wavelets for image analysis and processing (denoising).
Slide of the presentation (PDF 17.3 Mb)
MATLAB is a numerical computing environment and programming language. Created by The MathWorks.
FUNCTIONS OF MATLAB
- easy matrix manipulation,
- plotting of functions and data implementation of algorithm
- creation of user interfaces,
- implementation of algorithms,
HISTORY OF MATLAB
Short for “matrix laboratory”, MATLAB was invented in the late 1970s by Cleve Moler, then chairman of the computer science department at the University of New Mexico. He designed it to give his students access to LINPACK and EISPACK without having to learn Fortran. It soon spread to other universities and found a strong audience within the applied mathematics community.