Matlab course - Program

1. Matlab course - Program • Today • Basic matrix operations • Basic visualization. Images and plots • Thursday • Programming, scripts, more visualization • Friday • Graphical user interfaces and advanced visualizaton • Any suggestions wellcome, only today is fairly set

2. Matlab course - introduction • Goals • Visualize data • Develop programs • GUIs • Read other peoples programs (eg. SPM) • Hands on

3. Matlab as a language • Vector/matrix based • Many built in functions • Few lines of code will get you far • Extensive visualization capabilities • Might run slower depending on task

4. Lets start • Manipulating matrices and vectors • Basically needed for all programming tasks everything • Basic operations • Add noise to a simulated signal – an example (hemodynamic response function)

6. Visualizing data

7. Plotting data • The plot function • plot(y) • plot(x,y) • X and Y can be matrices • plot() will plot the columns • Use hold on to keep the old plot

8. Simulating the HRF Buxton: Introduction to FMRI hrtvectortor = hrt(timevector, tau, t0, n)

9. Excercise • Part 1 • Generate a timevector, L=10 s, interval=0.1 s • Use the hrf() function to generate a hemodynamic response • Use plot to verify the output • Part 2 • Imagine that you want to display 3 different hrf functions in a plot. Generate 3 functions and put them in a matrix columnwise. Use a single call to plot to view them • Part 3 • Use randn() to add noise to the curves. Use a std of XX • Plot again Example hrf parameters: timevec: from 0 to 20 secs tau: 1.2 t0: 4 s n: 3 Example noise parameters: Use a std of 0.1 – 0.3 - 0.5

11. Images=matrices

12. Imagescaling Min value Max value Full range Partial range

13. imagesc() + colorbar imagesc(data) imagesc(data,[0 5000])

14. Loading a dicom file • Matrix=dicomread(’filename’); • Matrix=double(Matrix); • imagesc(Matrix)

15. colormap(cmap_name)

16. hist(data(:),[bins])

17. Logical comparisons • Largevalues=matrix > 500 • Can be true or false, 0 or 1

18. Roipoly – will return a mask

19. Exercise • Load a dicom into a matrix • Use hist()( to identify noise and see if there is ghosting using imagesc() • ’Cut away’ the noise • Hint: use a comparison to make a noise mask and then .* • Use roipoly to mask away 1 hemisphere

20. Birgitte – how do we do stats on a mask?

21. Exercise • Use mean() to get a mean of an ROI selected using roipoly • Plot a histogram of your ROI

22. Saving work • save workspacename • save filename X saves only X. • Save filenename X Y Z saves X, Y, and Z.

23. The online docs • Very usefull • Good links • Good newsgroup • Tomorrow: structures – the dir command