医学图像处理案例（十五）——基于小波变换的医学图像融合

pip install PyWavelets

import pywt
import numpy as np
import SimpleITK as sitk


# This function does the coefficient fusing according to the fusion method
def fuseCoeff(cooef1, cooef2, method):
    if (method == 'mean'):
        cooef = (cooef1 + cooef2) / 2
    elif (method == 'min'):
        cooef = np.minimum(cooef1, cooef2)
    elif (method == 'max'):
        cooef = np.maximum(cooef1, cooef2)
    return cooef

# Params
FUSION_METHOD1 = 'mean'  # Can be 'min' || 'max || anything you choose according theory
FUSION_METHOD2 = 'max'
# Read the two image
I2_itk = sitk.ReadImage("Brats18_2013_1_1_flair.nii.gz", sitk.sitkInt16)
I1_itk = sitk.ReadImage("Brats18_2013_1_1_t1ce.nii.gz", sitk.sitkInt16)
I1 = sitk.GetArrayFromImage(I1_itk)
I2 = sitk.GetArrayFromImage(I2_itk)
# First: Do wavelet transform on each image
wavelet = 'db2'
"""
haar family: haar
db family: db1, db2, db3, db4, db5, db6, db7, db8, db9, db10, db11, db12, db13, db14, db15, db16, db17, db18, db19, db20, db21, db22, db23, db24, db25, db26, db27, db28, db29, db30, db31, db32, db33, db34, db35, db36, db37, db38
sym family: sym2, sym3, sym4, sym5, sym6, sym7, sym8, sym9, sym10, sym11, sym12, sym13, sym14, sym15, sym16, sym17, sym18, sym19, sym20
coif family: coif1, coif2, coif3, coif4, coif5, coif6, coif7, coif8, coif9, coif10, coif11, coif12, coif13, coif14, coif15, coif16, coif17
bior family: bior1.1, bior1.3, bior1.5, bior2.2, bior2.4, bior2.6, bior2.8, bior3.1, bior3.3, bior3.5, bior3.7, bior3.9, bior4.4, bior5.5, bior6.8
rbio family: rbio1.1, rbio1.3, rbio1.5, rbio2.2, rbio2.4, rbio2.6, rbio2.8, rbio3.1, rbio3.3, rbio3.5, rbio3.7, rbio3.9, rbio4.4, rbio5.5, rbio6.8
dmey family: dmey
gaus family: gaus1, gaus2, gaus3, gaus4, gaus5, gaus6, gaus7, gaus8
mexh family: mexh
morl family: morl
cgau family: cgau1, cgau2, cgau3, cgau4, cgau5, cgau6, cgau7, cgau8
shan family: shan
fbsp family: fbsp
cmor family: cmor
"""
cooef1 = pywt.wavedecn(I1[:, :], wavelet)
cooef2 = pywt.wavedecn(I2[:, :], wavelet)

# Second: for each level in both image do the fusion according to the desire option
fusedCooef = []
for i in range(len(cooef1)):
    # The first values in each decomposition is the apprximation values of the top level
    if i == 0:
        fusedCooef.append(fuseCoeff(cooef1[0], cooef2[0], FUSION_METHOD1))
    else:
        c1 = fuseCoeff(cooef1[i]['aad'], cooef2[i]['aad'], FUSION_METHOD2)
        c2 = fuseCoeff(cooef1[i]['ada'], cooef2[i]['ada'], FUSION_METHOD2)
        c3 = fuseCoeff(cooef1[i]['add'], cooef2[i]['add'], FUSION_METHOD2)
        c4 = fuseCoeff(cooef1[i]['daa'], cooef2[i]['daa'], FUSION_METHOD2)
        c5 = fuseCoeff(cooef1[i]['dad'], cooef2[i]['dad'], FUSION_METHOD2)
        c6 = fuseCoeff(cooef1[i]['dda'], cooef2[i]['dda'], FUSION_METHOD2)
        c7 = fuseCoeff(cooef1[i]['ddd'], cooef2[i]['ddd'], FUSION_METHOD2)
        dictobj = {'aad': c1, 'ada': c2, 'add': c3, 'daa': c4, 'dad': c5, 'dda': c6, 'ddd': c7}
        fusedCooef.append(dictobj)
# Third: After we fused the cooefficent we nned to transfor back to get the image
fusedImage = pywt.waverecn(fusedCooef, wavelet)
fusedImage = fusedImage.astype(np.int)
fused_itk = sitk.GetImageFromArray(fusedImage)
fused_itk.SetOrigin(I2_itk.GetOrigin())
fused_itk.SetSpacing(I2_itk.GetSpacing())
fused_itk.SetDirection(I2_itk.GetDirection())
sitk.WriteImage(fused_itk, 'fused_itk.mha')