采集到的dcm数据转换为nii格式，python源代码，直接可用

#!/usr/bin/env python # -*- coding:utf-8 -*- import os import sys sys.path.append('./') sys.path.append('../') import numpy as np import pydicom import nibabel from pathlib import Path #获取单个文件夹下所有dicom数据的头文件 #返回两个变量 #filelist存储所有数据文件的地址 #value_dict存储关键字 def GetHdrValueDcitByFolder(folderPath, SysFiles, tag_dict, type_dict, NoUseSequence): illegalSuffix = [ 'nii', 'gz', 'jpg', 'png' ] filelist = [] value_dict = {} for root, dirs, files in os.walk(folderPath): files = Except_system_file(files, SysFiles) for file in files: # print(root, file) strd = file.split('.') if strd[-1] not in illegalSuffix: if Path( os.path.join( root, file ) ).stat().st_size > 50 * 1024: hdr = pydicom.dcmread(os.path.join(root, file), stop_before_pixels = True) SeD = hdr.get(tag_dict["Series Description"]).value if SeD not in NoUseSequence: filelist.append(os.path.join(root, file)) for k, v in tag_dict.items(): if hdr.get(tag_dict[k]) is not None: tvalue = hdr.get(tag_dict[k]).value ttype = type_dict[hdr.get(tag_dict[k]).VR] if len(value_dict.keys()) != len(tag_dict.keys()): value_dict[k] = [np.array(tvalue).astype(ttype)] else: value_dict[k].append(np.array(tvalue).astype(ttype)) else: if k == "Slice Location": if hdr.get(tag_dict["Image Position Patient"]) is not None: ImagePositionPatient = hdr.get(tag_dict["Image Position Patient"]).value ttype = type_dict[hdr.get(tag_dict["Image Position Patient"]).VR] ImagePositionPatient = np.array(ImagePositionPatient).astype(ttype) ImageOrientationPatient = hdr.get(tag_dict["Image Orientation Patient"]).value ttype = type_dict[hdr.get(tag_dict["Image Orientation Patient"]).VR] ImageOrientationPatient = np.array(ImageOrientationPatient).astype(ttype) ImageOrientationPatientF = ImageOrientationPatient[: 3] ImageOrientationPatientS = ImageOrientationPatient[3:] LocationF = np.argmax(np.abs(ImageOrientationPatientF)) LocationS = np.argmax(np.abs(ImageOrientationPatientS)) LocationT = np.delete(np.array([0, 1, 2], dtype = np.int16), [LocationF, LocationS]) tvalue = ImagePositionPatient[LocationT][0] if len(value_dict.keys()) != len(tag_dict.keys()): value_dict[k] = [tvalue] else: value_dict[k].append(tvalue) else: tvalue = -1 if len(value_dict.keys()) != len(tag_dict.keys()): value_dict[k] = [tvalue] else: value_dict[k].append(tvalue) elif k == "Image Position Patient": tvalue = [-1, -1, -1] if len(value_dict.keys()) != len(tag_dict.keys()): value_dict[k] = [tvalue] else: value_dict[k].append(tvalue) elif k == 'Image Orientation Patient': tvalue = [1, 0, 0, 0, 1, 0] if len(value_dict.keys()) != len(tag_dict.keys()): value_dict[k] = [tvalue] else: value_dict[k].append(tvalue) else: tvalue = 1 if len(value_dict.keys()) != len(tag_dict.keys()): value_dict[k] = [tvalue] else: value_dict[k].append(tvalue) return filelist, value_dict def normalNii(hdr, aff, data): ori_shape = data.shape locationF, locationS, locationT = np.argmax( np.abs(aff[:, 0]) ), np.argmax( np.abs(aff[:, 1]) ), np.argmax( np.abs(aff[:, 2]) ) flipF, flipS, flipT = -1 if aff[locationF, 0] < 0 else 1, -1 if aff[locationS, 1] < 0 else 1, -1 if aff[locationT, 2] < 0 else 1 data = FlipByIndicator(data, [flipF, flipS, flipT]) affIndicator = [ locationF, locationS, locationT ] flipIndicator = [ flipF, flipS, flipT ] PixelSpacing = [ np.abs(aff[locationF, 0]), np.abs(aff[locationS, 1]) ] SlThickness = np.abs(aff[locationT, 2]) dim = len(data.shape) if dim == 4: if locationT == 0: data = np.transpose(data, [2, 0, 1, 3]) flipIndicator = np.array(flipIndicator)[ [2, 0, 1] ] elif locationT == 1: data = np.transpose(data, [0, 2, 1, 3]) flipIndicator = np.array(flipIndicator)[ [0, 2, 1] ] else: data = np.transpose(data, [0, 1, 2, 3]) flipIndicator = np.array(flipIndicator)[ [0, 1, 2] ] else: if locationT == 0: data = np.transpose(data, [2, 0, 1]) flipIndicator = np.array(flipIndicator)[ [2, 0, 1] ] elif locationT == 1: data = np.transpose(data, [0, 2, 1]) flipIndicator = np.array(flipIndicator)[ [0, 2, 1] ] else: data = np.transpose(data, [0, 1, 2]) flipIndicator = np.array(flipIndicator)[ [0, 1, 2] ] if flipT > 0: StartLocation = aff[locationT, 3] else: StartLocation = aff[locationT, 3] - SlThickness * ori_shape[2] ImagePositionPatient = aff[:, 3][: -1] hdr, aff = GenHdrAffine( data, ori_shape, affIndicator, [ flipF, flipS, flipT ], StartLocation, dim, PixelSpacing, ImagePositionPatient, SlThickness, 0, 0, 0, np.max(data), np.min(data) ) return hdr, aff, data def normalNiiFile(folderPath, savePath): for root, dirs, files in os.walk(folderPath): for file in files: if file.split('.')[-1] == 'nii' or file.split('.')[-1] == 'gz': hdr, aff, data = load_nii( os.path.join(root, file) ) hdr, aff, data = normalNii(hdr, aff, data) tpath = replaceIllegalChar( os.path.join(savePath, 'mask'), file ) tdir = os.path.split(tpath)[0] Make_Dir(tdir) save_nii( hdr, aff, data, tpath ) #对头文件进行排序 #排序规则为 #按不同的series description分类数据 #同类的数据先按照slice location进行数据从小到大排序 #slice location相同的情况下，按Instance Number从小到大排序 #函数返回数据字