tissue_analysis_tools.py 文件源码

def voxel_cell_vertex_extraction(img,**kwargs):

    shape = np.array(img.shape)
    neighborhood_img = []
    for x in np.arange(-1,2):
        for y in np.arange(-1,2):
            for z in np.arange(-1,2):
                neighborhood_img.append(img[1+x:shape[0]-1+x,1+y:shape[1]-1+y,1+z:shape[2]-1+z])
    neighborhood_img = np.sort(np.transpose(neighborhood_img,(1,2,3,0))).reshape((shape-2).prod(),27)
    neighborhoods = np.array(map(np.unique,neighborhood_img))
    neighborhood_size = np.array(map(len,neighborhoods)).reshape(shape[0]-2,shape[1]-2,shape[2]-2)
    neighborhoods = np.array(neighborhoods).reshape(shape[0]-2,shape[1]-2,shape[2]-2)

    vertex_coords = np.where(neighborhood_size==4)
    vertex_points = np.transpose(vertex_coords)+1
    vertex_cells = np.array([p for p in neighborhoods[vertex_coords]],int)

    unique_cell_vertices = array_unique(vertex_cells)
    vertices_matching = vq(vertex_cells,unique_cell_vertices)[0]
    unique_cell_vertex_points = np.array([np.mean(vertex_points[vertices_matching == v],axis=0) for v in xrange(len(unique_cell_vertices))])

    cell_vertex_dict = dict(zip([tuple(c) for c in unique_cell_vertices],list(unique_cell_vertex_points)))

    return cell_vertex_dict