ANTsPy医学图像配准

import os
import ants
import numpy as np
import time

fix_path = ‘img_fix.png‘
move_path = ‘img_move.png‘

# 配准所使用的各种方法 各方法耗时：https://www.cnblogs.com/JunzhaoLiang/p/12308200.html
types = [‘Translation‘, ‘Rigid‘, ‘Similarity‘, ‘QuickRigid‘, ‘DenseRigid‘, ‘BOLDRigid‘, ‘Affine‘, ‘AffineFast‘, ‘BOLDAffine‘,
         ‘TRSAA‘, ‘ElasticSyN‘, ‘SyN‘, ‘SyNRA‘, ‘SyNOnly‘, ‘SyNCC‘, ‘SyNabp‘, ‘SyNBold‘, ‘SyNBoldAff‘, ‘SyNAggro‘, ‘TVMSQ‘]

# 保存为png只支持unsigned char & unsigned short，因此读取时先进行数据类型转换
fix_img = ants.image_read(fix_path, pixeltype=‘unsigned char‘)
move_img = ants.image_read(move_path, pixeltype=‘unsigned char‘)

for t in types:
    start = time.time()

    out = ants.registration(fix_img, move_img, type_of_transform=t)
    reg_img = out[‘warpedmovout‘] # 获取配准结果

    reg_img.to_file(t+‘.png‘)

    print(t+‘ : ‘, time.time()-start, ‘\n‘)

import os
import ants
import numpy as npimport time

move_path = ‘img.png‘
move_img = ants.image_read(move_path, pixeltype=‘unsigned char‘)

n4_img = ants.n4_bias_field_correction(move_img)
n4_img = ants.from_numpy(np.array(n4_img.numpy(), dtype=‘uint8‘))
# png只接受 unsigned char & unsigned short
# 保存之前需转换类型

n4_img.to_file(‘TRSAA_n4.png‘)