Example #1
0
def make_bsa_2d(betas, theta=3., dmax=5., ths=0, thq=0.5, smin=0, 
                        nbeta=[0],method='simple'):
    """ Function for performing bayesian structural analysis on a set of images.
    """
    ref_dim = np.shape(betas[0])
    nbsubj = betas.shape[0]
    xyz = np.array(np.where(betas[:1])).T
    nvox = np.size(xyz, 0)
    
    # create the field strcture that encodes image topology
    Fbeta = ff.Field(nvox)
    Fbeta.from_3d_grid(xyz.astype(np.int), 18)

    # Get  coordinates in mm
    tal = xyz.astype(np.float)

    # get the functional information
    lbeta = np.array([np.ravel(betas[k]) for k in range(nbsubj)]).T

    # the voxel volume is 1.0
    g0 = 1.0/(1.0*nvox)
    bdensity = 1
    affine = np.eye(4)
    shape = (1,ref_dim[0],ref_dim[1])
    
    if method=='ipmi':
        group_map, AF, BF, likelihood = \
                   bsa.compute_BSA_ipmi(Fbeta, lbeta, tal, dmax,xyz, affine, 
                                               shape, thq,
                                        smin, ths, theta, g0, bdensity)
    if method=='simple':
        group_map, AF, BF, likelihood = \
                   bsa.compute_BSA_simple(Fbeta, lbeta, tal, dmax,xyz,
                                          affine, shape, thq, smin, ths, 
                                          theta, g0)
    if method=='dev':
        group_map, AF, BF, likelihood = \
                   bsa.compute_BSA_dev(Fbeta, lbeta, tal, dmax, xyz, affine, 
                                              shape, thq,
                                       smin, ths, theta, g0, bdensity)
    if method=='sbf':
        pval = 0.2
        group_map, AF, BF = sbf.Compute_Amers (Fbeta, lbeta, xyz, affine, 
                                                      shape,
                                               tal, dmax, theta, ths ,pval)
    return AF, BF
Example #2
0
def make_bsa_2d(betas, theta=3., dmax=5., ths=0, thq=0.5, smin=0, 
                        nbeta=[0], method='simple'):
    """
    Function for performing bayesian structural analysis on a set of images.

    Fixme: 'quick' is not tested
    """
    ref_dim = np.shape(betas[0])
    nbsubj = betas.shape[0]
    xyz = np.array(np.where(betas[:1])).T
    nvox = np.size(xyz, 0)
    
    # create the field strcture that encodes image topology
    Fbeta = ff.Field(nvox)
    Fbeta.from_3d_grid(xyz.astype(np.int), 18)

    # get the functional information
    lbeta = np.array([np.ravel(betas[k]) for k in range(nbsubj)]).T

    # the voxel volume is 1.0
    g0 = 1.0/(1.0*nvox)
    bdensity = 1
    dom = domain_from_array(np.ones(ref_dim))

    if method=='simple':
        group_map, AF, BF, likelihood = \
                   bsa.compute_BSA_simple(dom, lbeta, dmax, thq, smin, ths,
                                       theta, g0, bdensity)    
    if method=='ipmi':
        group_map, AF, BF, likelihood = \
                   bsa.compute_BSA_ipmi(dom, lbeta, dmax, thq, smin, ths,
                                       theta, g0, bdensity)
    if method=='sbf':
        pval = 0.2
        group_map, AF, BF = sbf.Compute_Amers (
            dom, lbeta, dmax, theta, ths, pval)
    return AF, BF
def make_bsa_2d(betas, theta=3., dmax=5., ths=0, thq=0.5, smin=0, 
                       method='simple',verbose = 0):
    """
    Function for performing bayesian structural analysis
    on a set of images.

    Parameters
    ----------
    betas, array of shape (nsubj, dimx, dimy) the data used
           Note that it is assumed to be a t- or z-variate
    theta=3., float,
              first level threshold of betas
    dmax=5., float, expected between subject variability
    ths=0, float,
           null hypothesis for the prevalence statistic
    thq=0.5, float,
             p-value of the null rejection
    smin=0, int,
            threshold on the nu_mber of contiguous voxels 
            to make regions meaningful structures
    method= 'simple', string,
            estimation method used ; to be chosen among 
            'simple', 'dev', 'loo', 'ipmi'
    verbose=0, verbosity mode     

    Returns
    -------
    AF the landmark_regions instance describing the result
    BF: list of hroi instances describing the individual data
    """
    ref_dim = np.shape(betas[0])
    nsubj = betas.shape[0]
    xyz = np.array(np.where(betas[:1])).T.astype(np.int)
    nvox = np.size(xyz, 0)
    
    # create the field strcture that encodes image topology
    Fbeta = ff.Field(nvox)
    Fbeta.from_3d_grid(xyz, 18)

    # Get  coordinates in mm
    coord = xyz.astype(np.float)

    # get the functional information
    lbeta = np.array([np.ravel(betas[k]) for k in range(nsubj)]).T

    # the voxel volume is 1.0
    g0 = 1.0/(1.0*nvox)*1./np.sqrt(2*np.pi*dmax**2)
    affine = np.eye(4)
    shape = (1, ref_dim[0], ref_dim[1])
    lmax=0
    bdensity = 1
    if method=='ipmi':
        group_map, AF, BF, likelihood = \
                   bsa.compute_BSA_ipmi(Fbeta, lbeta, coord, dmax, xyz,
                                        affine, shape, thq,
                                        smin, ths, theta, g0, bdensity)
    
    
    if method=='simple':
        group_map, AF, BF, likelihood = \
                   bsa.compute_BSA_simple(Fbeta, lbeta, coord, dmax, xyz,
                                          affine, shape, thq, smin, ths,
                                          theta, g0)
    if method=='loo':
         mll, ll0 = bsa.compute_BSA_loo(Fbeta, lbeta, coord, dmax, xyz,
                                        affine, shape, thq, smin, ths,
                                        theta, g0)
         return mll, ll0
    if method=='dev':
        group_map, AF, BF, likelihood = \
                   bsa.compute_BSA_dev(Fbeta, lbeta, coord, dmax, xyz,
                                       affine, shape, thq,
                                      smin, ths, theta, g0, bdensity)
    if method=='simple_quick':
        likelihood = np.zeros(ref_dim)
        group_map, AF, BF, coclustering = \
                   bsa.compute_BSA_simple_quick(Fbeta, lbeta, coord, dmax, xyz,
                                          affine, shape, thq, smin, ths,
                                          theta, g0)
    if method=='sbf':
        likelihood = np.zeros(ref_dim)
        group_map, AF, BF = sbf.Compute_Amers (Fbeta, lbeta, xyz, affine, shape,
                                              coord, dmax=dmax, thr=theta,
                                              ths=ths , pval=thq)

        
    if method not in['loo', 'dev','simple','ipmi','simple_quick','sbf']:
        raise ValueError,'method is not ocrreactly defined'
    
    if verbose==0:
        return AF,BF
    
    if AF != None:
        lmax = AF.k+2
        AF.show()

    group_map.shape = ref_dim
    mp.figure()
    mp.subplot(1,3,1)
    mp.imshow(group_map, interpolation='nearest', vmin=-1, vmax=lmax)
    mp.title('Blob separation map')
    mp.colorbar()

    if AF != None:
        group_map = AF.map_label(coord,0.95,dmax)
        group_map.shape = ref_dim
    
    mp.subplot(1,3,2)
    mp.imshow(group_map, interpolation='nearest', vmin=-1, vmax=lmax)
    mp.title('group-level position 95% \n confidence regions')
    mp.colorbar()

    mp.subplot(1,3,3)
    likelihood.shape = ref_dim
    mp.imshow(likelihood, interpolation='nearest')
    mp.title('Spatial density under h1')
    mp.colorbar()

    
    mp.figure()
    if nsubj==10:
        for s in range(nsubj):
            mp.subplot(2, 5, s+1)
            lw = -np.ones(ref_dim)
            if BF[s]!=None:
                nls = BF[s].get_roi_feature('label')
                nls[nls==-1] = np.size(AF)+2
                for k in range(BF[s].k):
                    xyzk = BF[s].xyz[k].T 
                    lw[xyzk[1],xyzk[2]] =  nls[k]

            mp.imshow(lw, interpolation='nearest', vmin=-1, vmax=lmax)
            mp.axis('off')

    mp.figure()
    if nsubj==10:
        for s in range(nsubj):
            mp.subplot(2,5,s+1)
            mp.imshow(betas[s],interpolation='nearest',vmin=betas.min(),
                      vmax=betas.max())
            mp.axis('off')

    return AF, BF
Example #4
0
def make_bsa_image_with_output_paths(mask_images, betas, denspath, crpath,
                                     theta=3., dmax= 5., ths=0, thq=0.5, smin=0,
                                     method='simple'):
    """
    Deprecated : will be removed soon

    idem make_bsa_image but paths of the output are set explictly.
    Moreover the segmented regions are written in one single image 
    """
    # Sanity check
    if len(mask_images)!=len(betas):
        print len(mask_images),len(betas)        
        raise ValueError,"the number of masks and activation images\
        should be the same"
    nsubj = len(mask_images)
        
    # Read the referential information
    nim = load(mask_images[0])
    ref_dim = nim.get_shape()
    affine = nim.get_affine()
    
    # Read the masks and compute the "intersection"
    mask = intersect_masks(mask_images)
    xyz = np.array(np.where(mask)).T
    nvox = xyz.shape[0]

    # create the field strcture that encodes image topology
    Fbeta = ff.Field(nvox)
    Fbeta.from_3d_grid(xyz.astype(np.int),18)

    # Get  coordinates in mm
    xyz = np.hstack((xyz,np.ones((nvox,1))))
    coord = np.dot(xyz,affine.T)[:,:3]
    xyz = xyz.astype(np.int)
    
    # read the functional images
    lbeta = []
    for s in range(nsubj):
        rbeta = load(betas[s])
        beta = rbeta.get_data()
        beta = beta[mask]
        lbeta.append(beta)
    lbeta = np.array(lbeta).T
    lbeta = np.reshape(lbeta,(nvox,nsubj))

    # launch the method
    g0 = 1.0/(np.absolute(np.linalg.det(affine))*nvox)
    bdensity = 1
    crmap = np.zeros(nvox)
    p = np.zeros(nvox)
    AF = None
    BF = [None for s in range(nsubj)]

    if method=='ipmi':
        crmap,AF,BF,p = bsa.compute_BSA_ipmi(Fbeta, lbeta, coord, dmax, 
                        xyz[:,:3], affine, ref_dim, thq, smin, ths,
                        theta, g0, bdensity)
    if method=='dev':
        crmap,AF,BF,p = bsa.compute_BSA_dev  (Fbeta, lbeta, coord, 
                        dmax, xyz[:,:3], affine, ref_dim, 
                        thq, smin,ths, theta, g0, bdensity,verbose=1)
    if method=='simple':
        crmap,AF,BF,p = bsa.compute_BSA_simple (Fbeta, lbeta, coord, dmax, 
                        xyz[:,:3], affine, ref_dim, 
                        thq, smin, ths, theta, g0, verbose=0)
        
    if method=='simple_quick':
        crmap,AF,BF,co_clust = bsa.compute_BSA_simple_quick (Fbeta, lbeta, coord, dmax, 
                        xyz[:,:3], affine, ref_dim, 
                        thq, smin, ths, theta, g0, verbose=0)
        density = np.zeros(nvox)
        crmap = AF.map_label(coord,0.95,dmax)

    if method=='loo':
        crmap,AF,BF,p = bsa.compute_BSA_loo (Fbeta, lbeta, coord, dmax, 
                        xyz[:,:3], affine, ref_dim, 
                        thq, smin,ths, theta, g0, verbose=0)
    
                    
    # Write the results
    Label = -2*np.ones(ref_dim,'int16')
    Label[mask] = crmap.astype('i')
    wim = Nifti1Image (Label, affine)
    wim.get_header()['descrip'] = 'group Level labels from bsa procedure'
    save(wim, crpath)

    density = np.zeros(ref_dim)
    density[mask] = p
    wim = Nifti1Image (density, affine)
    wim.get_header()['descrip'] = 'group-level spatial density of active regions'
    save(wim, denspath)

    if AF==None:
        default_idx = 0
    else:
        default_idx = AF.k+2
    
    # write everything in one image
    wdim = (ref_dim[0], ref_dim[1], ref_dim[2], nsubj+1)
    Label = -2*np.ones(wdim,'int16')
    Label[mask,0] = crmap.astype(np.int)
    for s in range(nsubj):
        Label[mask,s+1]=-1
        if BF[s]!=None:
            nls = BF[s].get_roi_feature('label')
            nls[nls==-1] = default_idx
            for k in range(BF[s].k):
                xyzk = BF[s].xyz[k].T 
                Label[xyzk[0],xyzk[1],xyzk[2],s+1] =  nls[k]
    wim = Nifti1Image (Label, affine)
    wim.get_header()['descrip'] = 'group Level and individual labels\
        from bsa procedure'
    save(wim, crpath)

    return AF,BF, maxc
Example #5
0
def make_bsa_image(mask_images, betas, theta=3., dmax= 5., ths=0, thq=0.5,
                   smin=0, swd="/tmp/", method='simple', subj_id=None,
                   nbeta='default', densPath=None, crPath=None, verbose=0):
    """
    main function for  performing bsa on a set of images.
    It creates the some output images in the given directory

    Parameters
    ------------
    mask_images: A list of image paths that yield binary images,
                 one for each subject
                 the number os subjects, nsubj, is taken as len(mask_images)
    betas: A list of image paths that yields the activation images,
           one for each subject
    theta=3., threshold used to ignore all the image data that si below
    dmax=5., prior width of the spatial model;
             corresponds to multi-subject uncertainty 
    ths=0: threshold on the representativity measure of the obtained
           regions
    thq=0.5: p-value of the representativity test:
             test = p(representativity>ths)>thq
    smin=0: minimal size (in voxels) of the extracted blobs
            smaller blobs are merged into larger ones
    swd='/tmp': writedir
    method='simple': applied region detection method; to be chose among
                     'simple', 'dev','ipmi'
    subj_id=None: list of strings, identifiers of the subjects.
                  by default it is range(nsubj)
    nbeta='default', string, identifier of the contrast
    densPath=None, string, path of the output density image
                   if False, no image is written
                   if None, the path is computed from swd, nbeta
    crPath=None,  string, path of the (4D) output label image
                  if False, no ime is written
                  if None, many images are written, 
                  with paths computed from swd, subj_id and nbeta
    Returns
    -------
    AF: an nipy.neurospin.spatial_models.structural_bfls.landmark_regions
        instance that describes the structures found at the group level
         None is returned if nothing has been found significant 
         at the group level
    BF : a list of nipy.neurospin.spatial_models.hroi.Nroi instances
       (one per subject) that describe the individual coounterpart of AF

    if method=='loo', the output is different:
        mll, float, the average likelihood of the data under H1 after cross validation
        ll0, float the log-likelihood of the data under the global null
  
    fixme: unique mask should be allowed
    """
    # Sanity check
    if len(mask_images)!=len(betas):
        raise ValueError,"the number of masks and activation images\
        should be the same"
    nsubj = len(mask_images)
    if subj_id==None:
        subj_id = [str[i] for i in range(nsubj)]
    
    # Read the referential information
    nim = load(mask_images[0])
    ref_dim = nim.get_shape()
    affine = nim.get_affine()
    
    # Read the masks and compute the "intersection"
    mask = intersect_masks(mask_images)
    xyz = np.array(np.where(mask)).T
    nvox = xyz.shape[0]

    # create the field strcture that encodes image topology
    Fbeta = ff.Field(nvox)
    Fbeta.from_3d_grid(xyz.astype(np.int),18)

    # Get  coordinates in mm
    xyz = np.hstack((xyz,np.ones((nvox,1))))
    coord = np.dot(xyz,affine.T)[:,:3]
    xyz = xyz.astype(np.int)
    
    # read the functional images
    lbeta = []
    for s in range(nsubj):
        rbeta = load(betas[s])
        beta = rbeta.get_data()
        beta = beta[mask]
        lbeta.append(beta)
    lbeta = np.array(lbeta).T

    # launch the method
    g0 = 1.0/(np.absolute(np.linalg.det(affine))*nvox)
    bdensity = 1
    crmap = np.zeros(nvox)
    p = np.zeros(nvox)
    AF = None
    BF = [None for s in range(nsubj)]

    if method=='ipmi':
        crmap,AF,BF,p = bsa.compute_BSA_ipmi(Fbeta, lbeta, coord, dmax, 
                        xyz[:,:3], affine, ref_dim, thq, smin, ths,
                        theta, g0, bdensity, verbose=verbose)
    if method=='dev':
        crmap,AF,BF,p = bsa.compute_BSA_dev  (Fbeta, lbeta, coord, 
                        dmax, xyz[:,:3], affine, ref_dim, 
                        thq, smin,ths, theta, g0, bdensity, verbose=verbose)
    if method=='simple':
        crmap,AF,BF,p = bsa.compute_BSA_simple (Fbeta, lbeta, coord, dmax, 
                        xyz[:,:3], affine, ref_dim, 
                        thq, smin, ths, theta, g0, verbose=verbose)
        
    if method=='simple_quick':
        crmap,AF,BF,co_clust = bsa.compute_BSA_simple_quick(Fbeta, lbeta, coord, dmax, 
                        xyz[:,:3], affine, ref_dim, 
                        thq, smin, ths, theta, g0, verbose=verbose)
        density = np.zeros(nvox)
        crmap = AF.map_label(coord,0.95,dmax)

    if method=='loo':
         mll, ll0 = bsa.compute_BSA_loo (Fbeta, lbeta, coord, dmax, 
                                xyz[:,:3], affine, ref_dim, 
                                thq, smin,ths, theta, g0, verbose=verbose)
         return mll, ll0
    
                    
    # Write the results as images
    # the spatial density image
    if densPath != False:
        density = np.zeros(ref_dim)
        density[mask] = p
        wim = Nifti1Image (density, affine)
        wim.get_header()['descrip'] = 'group-level spatial density of active regions'
        if densPath==None:
            densPath = op.join(swd,"density_%s.nii"%nbeta)
        save(wim, densPath)
    
    if crPath==False:
        return AF, BF

    if AF==None:
        default_idx = 0
    else:
        default_idx = AF.k+2
    
    if crPath==None:
        # write a 3D image for group-level labels
        crPath = op.join(swd,"CR_%s.nii"%nbeta)
        Label = -2*np.ones(ref_dim,'int16')
        Label[mask] = crmap
        wim = Nifti1Image (Label, affine)
        wim.get_header()['descrip'] = 'group Level labels from bsa procedure'
        save(wim, crPath)

        #write 3d images for the subjects
        for s in range(nsubj):
            LabelImage = op.join(swd,"AR_s%s_%s.nii"%(subj_id[s],nbeta))
            Label = -2*np.ones(ref_dim,'int16')
            Label[mask]=-1
            if BF[s]!=None:
                nls = BF[s].get_roi_feature('label')
                nls[nls==-1] = default_idx
                for k in range(BF[s].k):
                    xyzk = BF[s].xyz[k].T 
                    Label[xyzk[0],xyzk[1],xyzk[2]] =  nls[k]
        
            wim = Nifti1Image (Label, affine)
            wim.get_header()['descrip'] = 'Individual label image from bsa procedure'
            save(wim, LabelImage)
    else:
        # write everything in a single 4D image
        wdim = (ref_dim[0], ref_dim[1], ref_dim[2], nsubj+1)
        Label = -2*np.ones(wdim,'int16')
        Label[mask,0] = crmap
        for s in range(nsubj):
            Label[mask,s+1]=-1
            if BF[s]!=None:
                nls = BF[s].get_roi_feature('label')
                nls[nls==-1] = default_idx
                for k in range(BF[s].k):
                    xyzk = BF[s].xyz[k].T 
                    Label[xyzk[0],xyzk[1],xyzk[2],s+1] =  nls[k]
        wim = Nifti1Image (Label, affine)
        wim.get_header()['descrip'] = 'group Level and individual labels\
            from bsa procedure'
        save(wim, crPath)
        
    return AF,BF
Example #6
0
def make_bsa_image(
    mask_images, betas, theta=3., dmax= 5., ths=0, thq=0.5, smin=0, swd=None,
    method='simple', subj_id=None, nbeta='default', densPath=None,
    crPath=None, verbose=0, reshuffle=False):
    """
    main function for  performing bsa on a set of images.
    It creates the some output images in the given directory

    Parameters
    ------------
    mask_images: A list of image paths that yield binary images,
                 one for each subject
                 the number os subjects, nsubj, is taken as len(mask_images)
    betas: A list of image paths that yields the activation images,
           one for each subject
    theta=3., threshold used to ignore all the image data that si below
    dmax=5., prior width of the spatial model;
             corresponds to multi-subject uncertainty 
    ths=0: threshold on the representativity measure of the obtained
           regions
    thq=0.5: p-value of the representativity test:
             test = p(representativity>ths)>thq
    smin=0: minimal size (in voxels) of the extracted blobs
            smaller blobs are merged into larger ones
    swd: string, optional
        if not None, output directory
    method='simple': applied region detection method; to be chose among
                     'simple', 'ipmi'
    subj_id=None: list of strings, identifiers of the subjects.
                  by default it is range(nsubj)
    nbeta='default', string, identifier of the contrast
    densPath=None, string, path of the output density image
                   if False, no image is written
                   if None, the path is computed from swd, nbeta
    crPath=None,  string, path of the (4D) output label image
                  if False, no ime is written
                  if None, many images are written, 
                  with paths computed from swd, subj_id and nbeta
    reshuffle: bool, optional
               if true, randomly swap the sign of the data
    
    Returns
    -------
    AF: an nipy.neurospin.spatial_models.structural_bfls.landmark_regions
        instance that describes the structures found at the group level
         None is returned if nothing has been found significant 
         at the group level
    BF : a list of nipy.neurospin.spatial_models.hroi.Nroi instances
       (one per subject) that describe the individual coounterpart of AF

    if method=='loo', the output is different:
        mll, float, the average likelihood of the data under the model
        after cross validation
        ll0, float the log-likelihood of the data under the global null
  
    fixme: unique mask should be allowed
    """
    # Sanity check
    if len(mask_images)!=len(betas):
        raise ValueError,"the number of masks and activation images\
        should be the same"
    nsubj = len(mask_images)
    if subj_id==None:
        subj_id = [str(i) for i in range(nsubj)]
    
    # Read the referential information
    nim = load(mask_images[0])
    ref_dim = nim.get_shape()[:3]
    affine = nim.get_affine()
    
    # Read the masks and compute the "intersection"
    mask = np.reshape(intersect_masks(mask_images), ref_dim)
 
    # encode it as a domain
    dom = domain_from_image(Nifti1Image(mask, affine), nn=18)
    nvox = dom.size
    
    # read the functional images
    lbeta = []
    for s in range(nsubj):
        rbeta = load(betas[s])
        beta = np.reshape(rbeta.get_data(), ref_dim)
        lbeta.append(beta[mask])
    lbeta = np.array(lbeta).T

    if reshuffle:
        rswap = 2*(np.random.randn(nsubj)>0.5)-1
        lbeta = np.dot(lbeta, np.diag(rswap))
        
    # launch the method
    crmap = np.zeros(nvox)
    p = np.zeros(nvox)
    AF = None
    BF = [None for s in range(nsubj)]

    if method=='ipmi':
        crmap,AF,BF,p = bsa.compute_BSA_ipmi(dom, lbeta, dmax, thq, smin,
                                             ths, theta, verbose=verbose)
    if method=='simple':
        crmap,AF,BF,p = bsa.compute_BSA_simple(
            dom, lbeta, dmax, thq, smin, ths, theta, verbose=verbose)
        
    if method=='quick':
        crmap, AF, BF, co_clust = bsa.compute_BSA_quick(
            dom, lbeta, dmax, thq, smin, ths, theta, verbose=verbose)
            
        density = np.zeros(nvox)
        crmap = AF.map_label(dom.coord, 0.95, dmax)

    if method=='loo':
         mll, ll0 = bsa.compute_BSA_loo (
             dom, lbeta, dmax, thq, smin, ths, theta, verbose=verbose)
         
         return mll, ll0
    
                    
    # Write the results as images
    # the spatial density image
    if densPath != False:
        density = np.zeros(ref_dim)
        density[mask] = p
        wim = Nifti1Image (density, affine)
        wim.get_header()['descrip'] = 'group-level spatial density \
                                       of active regions'
        if densPath==None:
            densPath = op.join(swd,"density_%s.nii"%nbeta)
        save(wim, densPath)
    
    if crPath==False:
        return AF, BF

    
    default_idx = AF.k+2

    if crPath==None and swd==None:
        return AF, BF

    if crPath==None:
        # write a 3D image for group-level labels
        crPath = op.join(swd, "CR_%s.nii"%nbeta)
        Label = -2*np.ones(ref_dim, 'int16')
        Label[mask] = crmap
        wim = Nifti1Image (Label, affine)
        wim.get_header()['descrip'] = 'group Level labels from bsa procedure'
        save(wim, crPath)

        # write a prevalence image
        crPath = op.join(swd, "prevalence_%s.nii"%nbeta)
        # prev = AF.prevalence_density()
        prev = np.zeros(ref_dim)
        prev[mask] = AF.prevalence_density()
        wim = Nifti1Image (prev, affine)
        wim.get_header()['descrip'] = 'Weighted prevalence image'
        save(wim, crPath)
            
        #write 3d images for the subjects
        for s in range(nsubj):
            LabelImage = op.join(swd,"AR_s%s_%s.nii"%(subj_id[s],nbeta))
            Label = -2*np.ones(ref_dim, 'int16')
            Label[mask] = -1
            if BF[s] != None:
                nls = BF[s].get_roi_feature('label')
                nls[nls==-1] = default_idx
                lab = BF[s].label
                lab[lab>-1] = nls[lab[lab>-1]]
                Label[mask] = lab
               
        
            wim = Nifti1Image (Label, affine)
            wim.get_header()['descrip'] = \
                'Individual label image from bsa procedure'
            save(wim, LabelImage)
    else:
        # write everything in a single 4D image
        wdim = (ref_dim[0], ref_dim[1], ref_dim[2], nsubj+1)
        Label = -2*np.ones(wdim,'int16')
        Label[mask, 0] = crmap
        for s in range(nsubj):
            Label[mask,s+1]=-1
            if BF[s]!=None:
                nls = BF[s].get_roi_feature('label')
                nls[nls==-1] = default_idx
                lab = BF[s].label
                lab[lab>-1] = nls[lab[lab>-1]]
                Label[mask, s+1] = lab
                #for k in range(BF[s].k):
                #    Label[mask, s+1][BF[s].label==k] =  nls[k]
        wim = Nifti1Image (Label, affine)
        wim.get_header()['descrip'] = 'group Level and individual labels\
            from bsa procedure'
        save(wim, crPath)
        
    return AF,BF