Stat4Ci A sensitive statistical test for smooth classification images.
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Transcript of Stat4Ci A sensitive statistical test for smooth classification images.
![Page 1: Stat4Ci A sensitive statistical test for smooth classification images.](https://reader035.fdocuments.net/reader035/viewer/2022062318/551d9dba497959293b8de514/html5/thumbnails/1.jpg)
Stat4Ci
A sensitive statistical test for smooth classification images
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Z=1.64, p=.05 Z=2.35, p=.01
Test Z
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Pour des images ?
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Gaussian Random field
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Seuil non corrigé
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Bonferroni Correction
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Exemples
Bonf t = 4.5228 Bonf t = 3.5463 Bonf t = 3.5463RFT t = 4.06
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Pixel test
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Résumé
• Seulement 2 paramètres– FWHM = taille du filtre de
lissage– p = seuil de confiance
• Comment choisir FWHM ?– Pour détecter un signal donné,
le meilleur filtre est un filtre de taille comparable
• Problèmes– Si le signal est diffus, le pic est
faible
• Solution– Prendre en compte la taille et
le Z score.
• Cluster test
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Cluster test
tz = 2.5
k = 350 pixels
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Pixel test
tz = 3.30
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La toolbox
• p=.05;
• tC=2.7; % threshold for 2D image (other test)
• FWHM=HalfMax(sigma_b);
• [Sci,h] = SmoothCi(Ci, sigma_b);
• ZSCi = ZTransCi(SCi, mean(vecCi(:)), std(vecCi(:)));
• [volumes,N]=CiVol(sum(mask(:)),D)
• [tP,k]=stat_threshold(volumes,N,FWHM,Inf,p,tC,p);
• tCi = DisplayCi(ZSCi,tC,k,tP,FWHM,p,RFTtest,background);
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ZTransCi
• ZSCi = ZTransCi(SCi, mean(vecCi(:)), std(vecCi(:)));
• In ZtransCi(Ci1, n1, Ci2, n2, sigmaNoise, smoothFilter),– Ci1 = sum of white noise fields that led to a type 1 response (e.g., correct)
– n1 = number of type 1 response
– Ci2 = sum of white noise fields that led to a type 2 response (e.g., incorrect)
– n2 = number of type 2 response
– sigmaNoise = standard deviation of white noise
– smoothFilter = Gaussian filter used to smooth the classification image
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stat_threshold
• [tP,k]=stat_threshold(volumes,N,FWHM,Inf,p,tC,p);
– t pixel
– k taille minimun
– volumes, num_voxels, FWHM
– df : Inf
– p_val_peak, ...
– cluster_threshold,
– p_val_extent
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DisplayCi
• tCi = DisplayCi(ZSCi,tC,k,tP,FWHM,p,RFTtest,background);
t size resels Zmax x y-----------------------------------------
C [2.70] 970 0.44 4.17 122 129[2.70] 917 0.41 3.95 162 129-----------------------------------------
P 3.30 -
p-value = 0.05FWHM = 47.1Minimum cluster size = 861.7
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t size resels Zmax x y-----------------------------------------
C [2.70] 970 0.44 4.17 122 129[2.70] 917 0.41 3.95 162 129-----------------------------------------
P 3.30 -
p-value = [0.05]FWHM = [47.1]Minimum cluster size = 861.7
t size resels Zmax x y-----------------------------------------
C [2.70] 1787 0.81 5.2 133208-----------------------------------------
P 3.30 -
p-value = [0.05]FWHM = [47.1]Minimum cluster size = 861.7
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