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, DICOM image, Directory, ECAT i image, ECAT v image, FDF image, FreesurferMGH, FreesurferMGZ, GIS image, Hamamatsu ndpi, Hamamatsu vms, Hamamatsu vmu, JPEG image, Leica scn, MINC image, NIFTI-1 image, SPM image, Sakura svslide, TIFF image, TIFF image, TIFF(.tif) image, TIFF(.tif) image, Ventana bif, 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