.\" DO NOT MODIFY THIS FILE!  It was generated by help2man 1.47.4.
.TH BL "1" "September 2017" "bl 2.1.0" "User Commands"
.SH NAME
bl \- part of ANTS registration suite
.SH DESCRIPTION
Corrective Learning: Learning classifiers for correcting segmentation errors for a host segmentation method.
usage:
.IP
bl dim [options] AdaBoostOutputPrefix
.SS "required options:"
.TP
dim
Image dimension (2 or 3)
.HP
\fB\-ms\fR sub1_mseg.nii ... subN_mseg.nii Manual segmentations
.HP
\fB\-as\fR sub1_aseg.nii ... subN_aseg.nii Automatic segmentationds produced for the training images by the host segmentation method
.TP
\fB\-tl\fR Tlabel
Our method learns to correct one label at a time. This parameter specifies
the target label that is going to be learned by this learning task.
.TP
\fB\-rd\fR dilationR
This parameter specifies the region of interest (ROI) for this learning task.
A label's working ROI is defined as the region assigned with the Tlabel by the host
segmentation plus some dilation (unless a mask is provided, see below).
dilationR specifies the dilation radius.
The dilated ROI should cover most or all voxels that are manually assigned to Tlabel
Default: 2x2x2
.TP
\fB\-rf\fR featureRadius
Patch radius for feature extraction.
scalar or vector (AxBxC)
Default: 2x2x2
.TP
\fB\-rate\fR sampleRate
0<= sampleRate <=1. When the ROI is large or there are too many training images,
loadng every voxel in ROI as a training sample may be impossible due to the memory limit.
To address this problem, sampleRate specifies the percentage voxels from ROI that will be
problem, sampleRate specifies the percentage voxels from ROI that will be used
used. If sampleRate=0.01, 1 percent voxels will be used.
Default: 1
.TP
\fB\-i\fR iteration
Training iteration for AdaBoost learning.
Default: 100
.IP
options:
.TP
\fB\-c\fR featureChannel
Number of feature channels
.TP
\fB\-f\fR sub1_feature1.nii sub1_feature2.nii ... subN_feature1.nii subN_feature2.nii ...
Feature images for the training subjects. The number of feautres for each
subject should  equal featureChannel.
.TP
\fB\-m\fR mask1 ... maskN:
Specify labels' working ROI for the training images.
ROI will be derived by performing dilation on this mask then.