python读取nii文件，并对4DfMRI与sMRI切片

第一次写博客，决定把最近新学习的如何用python读取nii.gz文件以及如何切片写上来，如有错误，请大家指正。
主要是使用nibabel库，nibabel官网有教程，可以自行搜索查看~
我的fMRI数据是.nii.gz文件。

4DfMRI沿时间轴的切片并保存为npy文件

最终保存的为[样本数，时间轴，X, Y, Z]

# -*- coding: utf-8 -*-
"""
Created on Thu Mar 12 19:54:27 2020

@author: Fsyuee
"""

import numpy as np
import nibabel as nib
from nilearn import image
import os



def axis_append(img_numpy, num, i):  #维度扩张，扩张为[样本数, 时间轴, X, Y, X]
    if i == 1:
        img_numpy = num
        img_numpy = img_numpy[np.newaxis,:,:,:]
        #print(img_numpy.shape,num.shape)
    else:
        num = num[np.newaxis,:,:,:]
        img_numpy = np.append(img_numpy,num,axis=0)
        print(img_numpy.shape,num.shape)
    return img_numpy

def nii_to_image(niifile):
    filenames = os.listdir(niifile) #读取nii文件夹
    img_numpy = np.array([])
    count = 0	#记录读取到第几个样本
    for f in filenames:
        if '.nii' in f :
            count = count + 1
            print(count)
            img_path = os.path.join(filepath, f)
            img = nib.load(img_path)    #读取nii
            (x,y,z,t) = image.load_img(img).shape
            print(f,image.load_img(img).shape)  #显示图像尺寸fmri_0040144.nii.gz (27, 32, 26, 150)
            img1 = img.slicer[:,:,:,0]
            num = img1.get_fdata()   #转为npy
            num = num[np.newaxis,:,:]   
            #print(11,num.shape)
            #num = np.array(img1)
            for i in range(1,t):   #延时间轴切片
                img1 = img.slicer[:,:,:,i]
                #print(img1.shape)
                img_fdata = img1.get_fdata()   #npy
                img_fdata = img_fdata[np.newaxis,:,:]
                #print(img_fdata.shape)
                #print(num.shape)
                num = np.append(num,img_fdata, axis = 0)
                #print(num.shape)
    
            img_numpy = axis_append(img_numpy, num, count)
            print(img_numpy.shape)
    np.save('img_numpy',img_numpy)
  
if __name__ == '__main__':
 filepath = r'C:\Users\Fsyuee\nilearn_data\cobre'
 imgfile = r'C:\Users\Fsyuee\nilearn_data\cobre'
 nii_to_image(filepath)

4D沿时间轴切片后还可以继续沿X,Y,Z轴进行切片，利用读取不同维度的numpy方式。

img_fdata2 = img_fdata[:,:,2] #二维

3DsMRI切片

import nibabel as nib
from nilearn import image
import os
import imageio   #转换成图像
def nii_to_image(niifile):
    filenames = os.listdir(niifile) #读取nii文件夹
    for f in filenames:
         img_path = os.path.join(filepath, f)
         img = nib.load(img_path)    #读取nii
         #print(image.load_img(img).shape) #显示图像尺寸（240,240,240）
         img_fdata = img.get_fdata()   #转为npy
         print(img_fdata.shape)
         (x,y,z) = img.shape
         for i in range(0,z):
             img_fdata2 = img_fdata[:,:,i]
             print(img_fdata2.shape)
             #print(img_fdata2)

     
if __name__ == '__main__':
 filepath = r'E:\fMRI\sMRI\new'
 imgfile = r'E:\fMRI\sMRI\new'
 nii_to_image(filepath)