Tim Holy's ImageView package is another one to look at. Performance is very good with the Gtk backend (streaming video works well).
On Tue, Dec 16, 2014 at 6:39 PM, Johan Sigfrids <[email protected]> wrote: > Have you tried Winston? > https://github.com/nolta/Winston.jl > > > On Tuesday, December 16, 2014 11:15:42 PM UTC+2, David Smith wrote: >> >> Thank you. >> >> I feel like Julia has matured enough finally to start migrating all of my >> MRI research over to it. So far I have found no barriers whatsoever. I >> recommend it enthusiastically to all of my colleagues. They are probably >> getting tired of hearing about it. ;-) >> >> My biggest wish-list item (as a medical imager) would be native Julia >> plotting that is similar to Matplotlib. I'd rather not have to require >> that people have Python alongside Julia. Makes Julia sound less mature. I >> tried Gadfly, but when most of what you plot is images, Gadfly makes less >> sense. (Maybe something is missing in the grammar that includes images as >> something separate from rectbins.) I also got bit pretty hard by the pair >> borking bug in Color.jl, which was very annoying. >> >> On Tuesday, December 16, 2014 1:42:16 PM UTC-6, Isaiah wrote: >> >>> This is exciting! Congratulations on the release. >>> >>> On Tue, Dec 16, 2014 at 1:50 PM, David Smith <[email protected]> wrote: >>>> >>>> A few of us around here do medical imaging research, so I'm announcing >>>> the release of DCEMRI.jl, a Julia module for processing dynamic contrast >>>> enhanced magnetic resonance imaging (MRI) data. >>>> >>>> http://github.com/davidssmith/DCEMRI.jl >>>> >>>> To install, >>>> >>>> julia> Pkg.add("DCEMRI") >>>> >>>> To run a quick demo, >>>> >>>> julia> using DCEMRI >>>> >>>> julia> demo() >>>> >>>> To rerun the validations, >>>> >>>> julia> validate() >>>> >>>> (Validation can take a while, because the phantoms use a ridiculously >>>> large number of time points, and the Levenberg-Marquardt fitting scales >>>> poorly with number of measurements.) >>>> >>>> When you run these functions, PyPlot will show the resulting images >>>> after the run is complete, and pdfs of the images will be saved in the >>>> module directory by default, or another place if you specify. >>>> >>>> The models included currently are the standard and extended Tofts-Kety, >>>> and both have been validated against the test phantoms provided by the >>>> Quantitative Imaging Biomarkers Association. The execution speed is the >>>> fastest of any code I've tried, by about an order of magnitude, on a >>>> per-processor basis. You can fit a typical slice of in vivo data in about >>>> 1-2 seconds on a decent machine. >>>> >>>> Several modes of operation are supported, including file-based >>>> processing and passing data as function arguments and parameters as kwargs. >>>> See the demo and the validation functions for examples of usage. Parallel >>>> processing is supported, using either function parameters or by starting >>>> julia with the '-p <n>' flag. I also have a command-line script and a >>>> (simplistic) Matlab interface function. >>>> >>>> The code currently uses PyPlot for plotting, so you need Matplotlib >>>> installed, and that is not handled automatically, but all of the Julia >>>> dependencies are. >>>> >>>> A paper on the code is in press at PeerJ (https://peerj.com/preprints/ >>>> 670/). >>>> >>>> Let me know what you think. >>>> >>>> Cheers, >>>> Dave >>>> >>>>
