Python DICOM医学影像处理教程：pydicom读取、转换与操作指南

pydicom by davila7/claude-code-templates

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GitHub

安装命令

npx skills add https://github.com/davila7/claude-code-templates --skill pydicom

Python Web框架图像处理数据处理

🇨🇳中文介绍

Pydicom

概述

Pydicom 是一个纯 Python 包，用于处理 DICOM 文件，即医学影像数据的标准格式。本技能提供了关于读取、写入和操作 DICOM 文件的指导，包括处理像素数据、元数据以及各种压缩格式。

何时使用此技能

在以下场景中使用此技能：

医学影像文件（CT、MRI、X 射线、超声、PET 等）
需要提取或修改元数据的 DICOM 数据集
从医学扫描中提取像素数据和进行图像处理
用于研究或数据共享的 DICOM 匿名化
将 DICOM 文件转换为标准图像格式
需要解压的压缩 DICOM 数据
DICOM 序列和结构化报告
多切片体积重建
PACS（影像归档和通信系统）集成

安装

安装 pydicom 和常用依赖项：

uv pip install pydicom
uv pip install pillow  # 用于图像格式转换
uv pip install numpy   # 用于像素数组操作
uv pip install matplotlib  # 用于可视化

要处理压缩的 DICOM 文件，可能需要额外的包：

uv pip install pylibjpeg pylibjpeg-libjpeg pylibjpeg-openjpeg  # JPEG 压缩
uv pip install python-gdcm  # 替代压缩处理器

核心工作流程

读取 DICOM 文件

使用 pydicom.dcmread() 读取 DICOM 文件：

import pydicom

# 读取 DICOM 文件
ds = pydicom.dcmread('path/to/file.dcm')

# 访问元数据
print(f"Patient Name: {ds.PatientName}")
print(f"Study Date: {ds.StudyDate}")
print(f"Modality: {ds.Modality}")

# 显示所有元素
print(ds)

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将 DICOM 转换为图像格式

使用提供的 dicom_to_image.py 脚本或手动转换：

from PIL import Image
import pydicom
import numpy as np

ds = pydicom.dcmread('input.dcm')
pixel_array = ds.pixel_array

# 归一化到 0-255 范围
if pixel_array.dtype != np.uint8:
    pixel_array = ((pixel_array - pixel_array.min()) /
                   (pixel_array.max() - pixel_array.min()) * 255).astype(np.uint8)

# 保存为 PNG
image = Image.fromarray(pixel_array)
image.save('output.png')

使用脚本：python scripts/dicom_to_image.py input.dcm output.png

修改 DICOM 数据元素：

import pydicom
from datetime import datetime

ds = pydicom.dcmread('input.dcm')

# 修改现有元素
ds.PatientName = "Doe^John"
ds.StudyDate = datetime.now().strftime('%Y%m%d')
ds.StudyDescription = "Modified Study"

# 添加新元素
ds.SeriesNumber = 1
ds.SeriesDescription = "New Series"

# 移除元素
if hasattr(ds, 'PatientComments'):
    delattr(ds, 'PatientComments')
# 或使用 del
if 'PatientComments' in ds:
    del ds.PatientComments

# 保存修改后的文件
ds.save_as('modified.dcm')

匿名化 DICOM 文件

移除或替换患者可识别信息：

import pydicom
from datetime import datetime

ds = pydicom.dcmread('input.dcm')

# 通常包含 PHI（受保护的健康信息）的标签
tags_to_anonymize = [
    'PatientName', 'PatientID', 'PatientBirthDate',
    'PatientSex', 'PatientAge', 'PatientAddress',
    'InstitutionName', 'InstitutionAddress',
    'ReferringPhysicianName', 'PerformingPhysicianName',
    'OperatorsName', 'StudyDescription', 'SeriesDescription',
]

# 移除或替换敏感数据
for tag in tags_to_anonymize:
    if hasattr(ds, tag):
        if tag in ['PatientName', 'PatientID']:
            setattr(ds, tag, 'ANONYMOUS')
        elif tag == 'PatientBirthDate':
            setattr(ds, tag, '19000101')
        else:
            delattr(ds, tag)

# 更新日期以保持时间关系
if hasattr(ds, 'StudyDate'):
    # 通过随机偏移量调整日期
    ds.StudyDate = '20000101'

# 保持像素数据完整
ds.save_as('anonymized.dcm')

使用提供的脚本：python scripts/anonymize_dicom.py input.dcm output.dcm

从头创建 DICOM 文件：

import pydicom
from pydicom.dataset import Dataset, FileDataset
from datetime import datetime
import numpy as np

# 创建文件元信息
file_meta = Dataset()
file_meta.MediaStorageSOPClassUID = pydicom.uid.generate_uid()
file_meta.MediaStorageSOPInstanceUID = pydicom.uid.generate_uid()
file_meta.TransferSyntaxUID = pydicom.uid.ExplicitVRLittleEndian

# 创建 FileDataset 实例
ds = FileDataset('new_dicom.dcm', {}, file_meta=file_meta, preamble=b"\0" * 128)

# 添加必需的 DICOM 元素
ds.PatientName = "Test^Patient"
ds.PatientID = "123456"
ds.Modality = "CT"
ds.StudyDate = datetime.now().strftime('%Y%m%d')
ds.StudyTime = datetime.now().strftime('%H%M%S')
ds.ContentDate = ds.StudyDate
ds.ContentTime = ds.StudyTime

# 添加图像特定元素
ds.SamplesPerPixel = 1
ds.PhotometricInterpretation = "MONOCHROME2"
ds.Rows = 512
ds.Columns = 512
ds.BitsAllocated = 16
ds.BitsStored = 16
ds.HighBit = 15
ds.PixelRepresentation = 0

# 创建像素数据
pixel_array = np.random.randint(0, 4096, (512, 512), dtype=np.uint16)
ds.PixelData = pixel_array.tobytes()

# 添加必需的 UID
ds.SOPClassUID = pydicom.uid.CTImageStorage
ds.SOPInstanceUID = file_meta.MediaStorageSOPInstanceUID
ds.SeriesInstanceUID = pydicom.uid.generate_uid()
ds.StudyInstanceUID = pydicom.uid.generate_uid()

# 保存文件
ds.save_as('new_dicom.dcm')

处理压缩的 DICOM 文件：

import pydicom

# 读取压缩的 DICOM 文件
ds = pydicom.dcmread('compressed.dcm')

# 检查传输语法
print(f"Transfer Syntax: {ds.file_meta.TransferSyntaxUID}")
print(f"Transfer Syntax Name: {ds.file_meta.TransferSyntaxUID.name}")

# 解压并保存为未压缩格式
ds.decompress()
ds.save_as('uncompressed.dcm', write_like_original=False)

# 或在保存时压缩（需要适当的编码器）
ds_uncompressed = pydicom.dcmread('uncompressed.dcm')
ds_uncompressed.compress(pydicom.uid.JPEGBaseline8Bit)
ds_uncompressed.save_as('compressed_jpeg.dcm')

常见传输语法：

ExplicitVRLittleEndian - 未压缩，最常见
JPEGBaseline8Bit - JPEG 有损压缩
JPEGLossless - JPEG 无损压缩
JPEG2000Lossless - JPEG 2000 无损
RLELossless - 游程编码无损

完整列表请参阅 references/transfer_syntaxes.md。

处理嵌套数据结构：

import pydicom

ds = pydicom.dcmread('file.dcm')

# 访问序列
if 'ReferencedStudySequence' in ds:
    for item in ds.ReferencedStudySequence:
        print(f"Referenced SOP Instance UID: {item.ReferencedSOPInstanceUID}")

# 创建序列
from pydicom.sequence import Sequence

sequence_item = Dataset()
sequence_item.ReferencedSOPClassUID = pydicom.uid.CTImageStorage
sequence_item.ReferencedSOPInstanceUID = pydicom.uid.generate_uid()

ds.ReferencedImageSequence = Sequence([sequence_item])

处理多个相关的 DICOM 文件：

import pydicom
import numpy as np
from pathlib import Path

# 读取目录中的所有 DICOM 文件
dicom_dir = Path('dicom_series/')
slices = []

for file_path in dicom_dir.glob('*.dcm'):
    ds = pydicom.dcmread(file_path)
    slices.append(ds)

# 按切片位置或实例号排序
slices.sort(key=lambda x: float(x.ImagePositionPatient[2]))
# 或: slices.sort(key=lambda x: int(x.InstanceNumber))

# 创建 3D 体积
volume = np.stack([s.pixel_array for s in slices])
print(f"Volume shape: {volume.shape}")  # (num_slices, rows, columns)

# 获取间距信息以进行适当缩放
pixel_spacing = slices[0].PixelSpacing  # [row_spacing, col_spacing]
slice_thickness = slices[0].SliceThickness
print(f"Voxel size: {pixel_spacing[0]}x{pixel_spacing[1]}x{slice_thickness} mm")

此技能在 scripts/ 目录中包含实用脚本：

通过移除或替换受保护的健康信息（PHI）来匿名化 DICOM 文件。

python scripts/anonymize_dicom.py input.dcm output.dcm

将 DICOM 文件转换为常见图像格式（PNG、JPEG、TIFF）。

python scripts/dicom_to_image.py input.dcm output.png
python scripts/dicom_to_image.py input.dcm output.jpg --format JPEG

以可读格式提取和显示 DICOM 元数据。

python scripts/extract_metadata.py file.dcm
python scripts/extract_metadata.py file.dcm --output metadata.txt

详细参考信息可在 references/ 目录中找到：

common_tags.md : 按类别（患者、研究、序列、图像等）组织的常用 DICOM 标签的完整列表
transfer_syntaxes.md : DICOM 传输语法和压缩格式的完整参考

常见问题与解决方案

问题："无法解码像素数据"

解决方案：安装额外的压缩处理器：uv pip install pylibjpeg pylibjpeg-libjpeg python-gdcm

问题：访问标签时出现 "AttributeError"

解决方案：使用 hasattr(ds, 'AttributeName') 检查属性是否存在，或使用 ds.get('AttributeName', default)

问题：图像显示不正确（太暗/太亮）

解决方案：应用 VOI LUT 窗位窗宽：apply_voi_lut(pixel_array, ds) 或使用 WindowCenter 和 WindowWidth 手动调整

问题：大型序列的内存问题

解决方案：迭代处理文件，使用内存映射数组，或对图像进行下采样

在访问属性之前始终检查必需属性，使用 hasattr() 或 get()
修改文件时保留文件元数据，使用 save_as() 并设置 write_like_original=True
使用传输语法 UID 在处理像素数据前了解压缩格式
处理来自不受信任来源的文件时处理异常
为医学图像可视化应用适当的窗位窗宽（VOI LUT）
处理 3D 体积时保持空间信息（像素间距、切片厚度）
在共享医学数据之前彻底验证匿名化
正确使用 UID - 创建新实例时生成新的 UID，修改时保留它们

🇺🇸English

Pydicom

Overview

Pydicom is a pure Python package for working with DICOM files, the standard format for medical imaging data. This skill provides guidance on reading, writing, and manipulating DICOM files, including working with pixel data, metadata, and various compression formats.

When to Use This Skill

Use this skill when working with:

Medical imaging files (CT, MRI, X-ray, ultrasound, PET, etc.)
DICOM datasets requiring metadata extraction or modification
Pixel data extraction and image processing from medical scans
DICOM anonymization for research or data sharing
Converting DICOM files to standard image formats
Compressed DICOM data requiring decompression
DICOM sequences and structured reports
Multi-slice volume reconstruction
PACS (Picture Archiving and Communication System) integration

Installation

Install pydicom and common dependencies:

uv pip install pydicom
uv pip install pillow  # For image format conversion
uv pip install numpy   # For pixel array manipulation
uv pip install matplotlib  # For visualization

For handling compressed DICOM files, additional packages may be needed:

uv pip install pylibjpeg pylibjpeg-libjpeg pylibjpeg-openjpeg  # JPEG compression
uv pip install python-gdcm  # Alternative compression handler

Core Workflows

Reading DICOM Files

Read a DICOM file using pydicom.dcmread():

import pydicom

# Read a DICOM file
ds = pydicom.dcmread('path/to/file.dcm')

# Access metadata
print(f"Patient Name: {ds.PatientName}")
print(f"Study Date: {ds.StudyDate}")
print(f"Modality: {ds.Modality}")

# Display all elements
print(ds)

Key points:

dcmread() returns a Dataset object
Access data elements using attribute notation (e.g., ds.PatientName) or tag notation (e.g., ds[0x0010, 0x0010])
Use ds.file_meta to access file metadata like Transfer Syntax UID
Handle missing attributes with getattr(ds, 'AttributeName', default_value) or hasattr(ds, 'AttributeName')

Working with Pixel Data

Extract and manipulate image data from DICOM files:

import pydicom
import numpy as np
import matplotlib.pyplot as plt

# Read DICOM file
ds = pydicom.dcmread('image.dcm')

# Get pixel array (requires numpy)
pixel_array = ds.pixel_array

# Image information
print(f"Shape: {pixel_array.shape}")
print(f"Data type: {pixel_array.dtype}")
print(f"Rows: {ds.Rows}, Columns: {ds.Columns}")

# Apply windowing for display (CT/MRI)
if hasattr(ds, 'WindowCenter') and hasattr(ds, 'WindowWidth'):
    from pydicom.pixel_data_handlers.util import apply_voi_lut
    windowed_image = apply_voi_lut(pixel_array, ds)
else:
    windowed_image = pixel_array

# Display image
plt.imshow(windowed_image, cmap='gray')
plt.title(f"{ds.Modality} - {ds.StudyDescription}")
plt.axis('off')
plt.show()

Working with color images:

# RGB images have shape (rows, columns, 3)
if ds.PhotometricInterpretation == 'RGB':
    rgb_image = ds.pixel_array
    plt.imshow(rgb_image)
elif ds.PhotometricInterpretation == 'YBR_FULL':
    from pydicom.pixel_data_handlers.util import convert_color_space
    rgb_image = convert_color_space(ds.pixel_array, 'YBR_FULL', 'RGB')
    plt.imshow(rgb_image)

Multi-frame images (videos/series):

# For multi-frame DICOM files
if hasattr(ds, 'NumberOfFrames') and ds.NumberOfFrames > 1:
    frames = ds.pixel_array  # Shape: (num_frames, rows, columns)
    print(f"Number of frames: {frames.shape[0]}")

    # Display specific frame
    plt.imshow(frames[0], cmap='gray')

Converting DICOM to Image Formats

Use the provided dicom_to_image.py script or convert manually:

from PIL import Image
import pydicom
import numpy as np

ds = pydicom.dcmread('input.dcm')
pixel_array = ds.pixel_array

# Normalize to 0-255 range
if pixel_array.dtype != np.uint8:
    pixel_array = ((pixel_array - pixel_array.min()) /
                   (pixel_array.max() - pixel_array.min()) * 255).astype(np.uint8)

# Save as PNG
image = Image.fromarray(pixel_array)
image.save('output.png')

Use the script: python scripts/dicom_to_image.py input.dcm output.png

Modifying Metadata

Modify DICOM data elements:

import pydicom
from datetime import datetime

ds = pydicom.dcmread('input.dcm')

# Modify existing elements
ds.PatientName = "Doe^John"
ds.StudyDate = datetime.now().strftime('%Y%m%d')
ds.StudyDescription = "Modified Study"

# Add new elements
ds.SeriesNumber = 1
ds.SeriesDescription = "New Series"

# Remove elements
if hasattr(ds, 'PatientComments'):
    delattr(ds, 'PatientComments')
# Or using del
if 'PatientComments' in ds:
    del ds.PatientComments

# Save modified file
ds.save_as('modified.dcm')

Anonymizing DICOM Files

Remove or replace patient identifiable information:

import pydicom
from datetime import datetime

ds = pydicom.dcmread('input.dcm')

# Tags commonly containing PHI (Protected Health Information)
tags_to_anonymize = [
    'PatientName', 'PatientID', 'PatientBirthDate',
    'PatientSex', 'PatientAge', 'PatientAddress',
    'InstitutionName', 'InstitutionAddress',
    'ReferringPhysicianName', 'PerformingPhysicianName',
    'OperatorsName', 'StudyDescription', 'SeriesDescription',
]

# Remove or replace sensitive data
for tag in tags_to_anonymize:
    if hasattr(ds, tag):
        if tag in ['PatientName', 'PatientID']:
            setattr(ds, tag, 'ANONYMOUS')
        elif tag == 'PatientBirthDate':
            setattr(ds, tag, '19000101')
        else:
            delattr(ds, tag)

# Update dates to maintain temporal relationships
if hasattr(ds, 'StudyDate'):
    # Shift dates by a random offset
    ds.StudyDate = '20000101'

# Keep pixel data intact
ds.save_as('anonymized.dcm')

Use the provided script: python scripts/anonymize_dicom.py input.dcm output.dcm

Writing DICOM Files

Create DICOM files from scratch:

import pydicom
from pydicom.dataset import Dataset, FileDataset
from datetime import datetime
import numpy as np

# Create file meta information
file_meta = Dataset()
file_meta.MediaStorageSOPClassUID = pydicom.uid.generate_uid()
file_meta.MediaStorageSOPInstanceUID = pydicom.uid.generate_uid()
file_meta.TransferSyntaxUID = pydicom.uid.ExplicitVRLittleEndian

# Create the FileDataset instance
ds = FileDataset('new_dicom.dcm', {}, file_meta=file_meta, preamble=b"\0" * 128)

# Add required DICOM elements
ds.PatientName = "Test^Patient"
ds.PatientID = "123456"
ds.Modality = "CT"
ds.StudyDate = datetime.now().strftime('%Y%m%d')
ds.StudyTime = datetime.now().strftime('%H%M%S')
ds.ContentDate = ds.StudyDate
ds.ContentTime = ds.StudyTime

# Add image-specific elements
ds.SamplesPerPixel = 1
ds.PhotometricInterpretation = "MONOCHROME2"
ds.Rows = 512
ds.Columns = 512
ds.BitsAllocated = 16
ds.BitsStored = 16
ds.HighBit = 15
ds.PixelRepresentation = 0

# Create pixel data
pixel_array = np.random.randint(0, 4096, (512, 512), dtype=np.uint16)
ds.PixelData = pixel_array.tobytes()

# Add required UIDs
ds.SOPClassUID = pydicom.uid.CTImageStorage
ds.SOPInstanceUID = file_meta.MediaStorageSOPInstanceUID
ds.SeriesInstanceUID = pydicom.uid.generate_uid()
ds.StudyInstanceUID = pydicom.uid.generate_uid()

# Save the file
ds.save_as('new_dicom.dcm')

Compression and Decompression

Handle compressed DICOM files:

import pydicom

# Read compressed DICOM file
ds = pydicom.dcmread('compressed.dcm')

# Check transfer syntax
print(f"Transfer Syntax: {ds.file_meta.TransferSyntaxUID}")
print(f"Transfer Syntax Name: {ds.file_meta.TransferSyntaxUID.name}")

# Decompress and save as uncompressed
ds.decompress()
ds.save_as('uncompressed.dcm', write_like_original=False)

# Or compress when saving (requires appropriate encoder)
ds_uncompressed = pydicom.dcmread('uncompressed.dcm')
ds_uncompressed.compress(pydicom.uid.JPEGBaseline8Bit)
ds_uncompressed.save_as('compressed_jpeg.dcm')

Common transfer syntaxes:

ExplicitVRLittleEndian - Uncompressed, most common
JPEGBaseline8Bit - JPEG lossy compression
JPEGLossless - JPEG lossless compression
JPEG2000Lossless - JPEG 2000 lossless
RLELossless - Run-Length Encoding lossless

See references/transfer_syntaxes.md for complete list.

Working with DICOM Sequences

Handle nested data structures:

import pydicom

ds = pydicom.dcmread('file.dcm')

# Access sequences
if 'ReferencedStudySequence' in ds:
    for item in ds.ReferencedStudySequence:
        print(f"Referenced SOP Instance UID: {item.ReferencedSOPInstanceUID}")

# Create a sequence
from pydicom.sequence import Sequence

sequence_item = Dataset()
sequence_item.ReferencedSOPClassUID = pydicom.uid.CTImageStorage
sequence_item.ReferencedSOPInstanceUID = pydicom.uid.generate_uid()

ds.ReferencedImageSequence = Sequence([sequence_item])

Processing DICOM Series

Work with multiple related DICOM files:

import pydicom
import numpy as np
from pathlib import Path

# Read all DICOM files in a directory
dicom_dir = Path('dicom_series/')
slices = []

for file_path in dicom_dir.glob('*.dcm'):
    ds = pydicom.dcmread(file_path)
    slices.append(ds)

# Sort by slice location or instance number
slices.sort(key=lambda x: float(x.ImagePositionPatient[2]))
# Or: slices.sort(key=lambda x: int(x.InstanceNumber))

# Create 3D volume
volume = np.stack([s.pixel_array for s in slices])
print(f"Volume shape: {volume.shape}")  # (num_slices, rows, columns)

# Get spacing information for proper scaling
pixel_spacing = slices[0].PixelSpacing  # [row_spacing, col_spacing]
slice_thickness = slices[0].SliceThickness
print(f"Voxel size: {pixel_spacing[0]}x{pixel_spacing[1]}x{slice_thickness} mm")

Helper Scripts

This skill includes utility scripts in the scripts/ directory:

anonymize_dicom.py

Anonymize DICOM files by removing or replacing Protected Health Information (PHI).

python scripts/anonymize_dicom.py input.dcm output.dcm

dicom_to_image.py

Convert DICOM files to common image formats (PNG, JPEG, TIFF).

python scripts/dicom_to_image.py input.dcm output.png
python scripts/dicom_to_image.py input.dcm output.jpg --format JPEG

extract_metadata.py

Extract and display DICOM metadata in a readable format.

python scripts/extract_metadata.py file.dcm
python scripts/extract_metadata.py file.dcm --output metadata.txt

Reference Materials

Detailed reference information is available in the references/ directory:

common_tags.md : Comprehensive list of commonly used DICOM tags organized by category (Patient, Study, Series, Image, etc.)
transfer_syntaxes.md : Complete reference of DICOM transfer syntaxes and compression formats

Common Issues and Solutions

Issue: "Unable to decode pixel data"

Solution: Install additional compression handlers: uv pip install pylibjpeg pylibjpeg-libjpeg python-gdcm

Issue: "AttributeError" when accessing tags

Solution: Check if attribute exists with hasattr(ds, 'AttributeName') or use ds.get('AttributeName', default)

Issue: Incorrect image display (too dark/bright)

Solution: Apply VOI LUT windowing: apply_voi_lut(pixel_array, ds) or manually adjust with WindowCenter and WindowWidth

Issue: Memory issues with large series

Solution: Process files iteratively, use memory-mapped arrays, or downsample images

Best Practices

Always check for required attributes before accessing them using hasattr() or get()
Preserve file metadata when modifying files by using save_as() with write_like_original=True
Use Transfer Syntax UIDs to understand compression format before processing pixel data
Handle exceptions when reading files from untrusted sources
Apply proper windowing (VOI LUT) for medical image visualization
Maintain spatial information (pixel spacing, slice thickness) when processing 3D volumes
Verify anonymization thoroughly before sharing medical data
Use UIDs correctly - generate new UIDs when creating new instances, preserve them when modifying

Documentation

Official pydicom documentation: https://pydicom.github.io/pydicom/dev/

User Guide: https://pydicom.github.io/pydicom/dev/guides/user/index.html
Tutorials: https://pydicom.github.io/pydicom/dev/tutorials/index.html
API Reference: https://pydicom.github.io/pydicom/dev/reference/index.html
Examples: https://pydicom.github.io/pydicom/dev/auto_examples/index.html

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FastAPI官方技能：Python Web开发最佳实践与CLI工具使用指南

1,100 周安装

Python DICOM医学影像处理教程：pydicom读取、转换与操作指南

🇨🇳中文介绍

Pydicom

概述

何时使用此技能

安装

核心工作流程

读取 DICOM 文件

相关 Skills

处理像素数据

将 DICOM 转换为图像格式

修改元数据

匿名化 DICOM 文件

写入 DICOM 文件

压缩与解压缩

处理 DICOM 序列

处理 DICOM 序列

辅助脚本

anonymize_dicom.py

dicom_to_image.py

extract_metadata.py

参考资料

常见问题与解决方案

最佳实践

文档

🇺🇸English

Pydicom

Overview

When to Use This Skill

Installation

Core Workflows

Reading DICOM Files

Working with Pixel Data

Converting DICOM to Image Formats

Modifying Metadata

Anonymizing DICOM Files

Writing DICOM Files

Compression and Decompression

Working with DICOM Sequences

Processing DICOM Series

Helper Scripts

anonymize_dicom.py

dicom_to_image.py

extract_metadata.py

Reference Materials

Common Issues and Solutions

Best Practices

Documentation

最新 Skills