Image Analysis: Mathematical morphology
The structure of this section could be: - Microarrays, gene clustering (SOM) - Phylogenetics - QTL I could mention the following packages: qtl (quantitative traits loci) bqtl GeneSOM Clustering genes using Self-Organizing Maps (SOMs). ape Analyses of Phylogenetics and Evolution sma micro-array analysis permax (DNA array) genetics PHYLOGR (devel) See also the Bioconductor, that mainly focuses on DNA microarrays. http://www.bioconductor.org/packages/devel/html/
"Mathematical morphology" refers to local image transformation algorithms -- "local" means that the new colour of a pixel depends on that of the neighbouring pixels.
For instance, the game of life is local transformation.
TODO: Explain, insert a few plots.
(The more rigorous definition, should you want it, refers to non-decreasing, involutive and local transformations.)
Introduction: explain what mathematical morphology is. Give a few examples of images one could want to process. http://www.mmorph.com/pymorph/ http://www.ensta.fr/~manzaner/Publis/These.ps.gz morphology.R http://zoonek.free.fr/Ecrits/2004_BioRet.pdf.bz2 Images (plot.image, plot3d.image) Packages: mainly EBImage, but also rimage, pixmap Basic operations: translations rotations Structuring elements (plot.structuring.element) Hit-and-Miss transform ("pattern matching") Intersection (inf), union (sup), complementary, difference Dilatation, Erosion Opening, closing Gradient, internal gradient, external gradient, laplacian Top hat Reconstruction: Hysteresis threshold, Hole filling Local maxima, max.loc.hys, regional maxima, geodesic dilatation, r.h.maxima skeleton, hairy or not Homotopic kernel (median axis)
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latest modification on Sat Jan 6 10:28:26 GMT 2007