Color segmentation: feature based model learning

None

Paramètres

Input_ListOf_FeatImages: ListOf( Volume 3D ) ( input )

Input_ListOf_Segmented_Images: ListOf( ROI ) ( input )

Clustering_Method: Choice ( input )

Clustering_Threshold: Réel ( optional, input )

Clustering_Temperature_Min: Réel ( optional, input )

Clustering_Sigma_Ratio: Réel ( optional, input )

Clustering_Cluster_Max: Entier ( optional, input )

Clustering_Size_Min: Réel ( optional, input )

ListOf_Prior_Probabilities: ListOf( Réel ) ( optional, input )

Output_Model: Text file ( sortie )

Informations techniques

Toolbox : BrainRAT

Niveau d'utilisateur : 1

Identifiant : SegmentationModelLearning

Nom de fichier : brainvisa/toolboxes/brainrat/processes/agregatesegmentation/SegmentationModelLearning.py

Supported file formats :

Input_ListOf_FeatImages :

gz compressed NIFTI-1 image, Aperio svs, BMP image, DICOM image, Directory, ECAT i image, ECAT v image, FDF image, FreesurferMGH, FreesurferMGZ, GIF image, GIS image, Hamamatsu ndpi, Hamamatsu vms, Hamamatsu vmu, JPEG image, Leica scn, MINC image, NIFTI-1 image, PBM image, PGM image, PNG image, PPM image, SPM image, Sakura svslide, TIFF image, TIFF image, TIFF(.tif) image, TIFF(.tif) image, VIDA image, Ventana bif, XBM image, XPM image, Zeiss czi, gz compressed MINC image, gz compressed NIFTI-1 image

Input_ListOf_Segmented_Images :

Graph and data, Graph and data

Output_Model :

Text file, Text file