DICOM Processing

What This Skill Does

Generates correct code for reading, writing, and manipulating DICOM (Digital Imaging and Communications in Medicine) files. Covers the pydicom Python library (primary), DCMTK command-line tools, and the DICOM data model including tags, VRs, transfer syntaxes, and SOP classes.

Prerequisites

Python 3.8+ with pydicom (pip install pydicom)
Optional: pillow or numpy for pixel data operations
Optional: pylibjpeg + pylibjpeg-libjpeg for compressed transfer syntaxes
Optional: DCMTK toolkit for command-line operations

# Core
pip install pydicom

# Pixel data handling
pip install pydicom[all]
# Or individually:
pip install numpy pillow pylibjpeg pylibjpeg-libjpeg pylibjpeg-openjpeg

# DCMTK (command-line tools)
# macOS
brew install dcmtk
# Ubuntu/Debian
apt-get install dcmtk

Quick Start

Read a DICOM File

import pydicom

ds = pydicom.dcmread("image.dcm")

# Access common attributes
print(ds.PatientName)        # Patient's name
print(ds.StudyDate)          # Study date (YYYYMMDD)
print(ds.Modality)           # CT, MR, US, CR, etc.
print(ds.StudyInstanceUID)   # Unique study identifier
print(ds.SeriesInstanceUID)  # Unique series identifier
print(ds.SOPInstanceUID)     # Unique instance identifier

Modify and Save

ds = pydicom.dcmread("image.dcm")
ds.PatientName = "ANONYMOUS"
ds.PatientID = "ANON001"
ds.save_as("modified.dcm")

Access Pixel Data

ds = pydicom.dcmread("image.dcm")
pixel_array = ds.pixel_array  # Returns numpy ndarray
print(pixel_array.shape)      # e.g., (512, 512) for a single frame
print(pixel_array.dtype)      # e.g., int16

DICOM Data Model

Common Tags Reference

| Tag | Keyword | VR | Description | |-----|---------|-----|-------------| | (0008,0020) | StudyDate | DA | Date the study started | | (0008,0030) | StudyTime | TM | Time the study started | | (0008,0050) | AccessionNumber | SH | RIS accession number | | (0008,0060) | Modality | CS | CT, MR, US, CR, XA, etc. | | (0008,0070) | Manufacturer | LO | Equipment manufacturer | | (0008,103E) | SeriesDescription | LO | Description of the series | | (0008,1030) | StudyDescription | LO | Description of the study | | (0010,0010) | PatientName | PN | Patient's full name | | (0010,0020) | PatientID | LO | Patient identifier | | (0010,0030) | PatientBirthDate | DA | Patient date of birth | | (0010,0040) | PatientSex | CS | M, F, or O | | (0020,000D) | StudyInstanceUID | UI | Unique study identifier | | (0020,000E) | SeriesInstanceUID | UI | Unique series identifier | | (0008,0018) | SOPInstanceUID | UI | Unique instance identifier | | (0008,0016) | SOPClassUID | UI | Type of DICOM object | | (0028,0010) | Rows | US | Image height in pixels | | (0028,0011) | Columns | US | Image width in pixels | | (0028,0100) | BitsAllocated | US | Bits per pixel (8, 16) | | (0028,0004) | PhotometricInterpretation | CS | MONOCHROME1, MONOCHROME2, RGB | | (7FE0,0010) | PixelData | OB/OW | The actual pixel data |

Value Representations (VRs)

VRs define the data type and format of a DICOM value:

| VR | Name | Python Type | Example | |----|------|-------------|---------| | CS | Code String | str | "CT", "MR" | | DA | Date | str | "20250115" (YYYYMMDD) | | DS | Decimal String | DSfloat/str | "1.5" | | IS | Integer String | IS/str | "512" | | LO | Long String | str | Max 64 chars | | PN | Person Name | PersonName | "Smith^John" | | SH | Short String | str | Max 16 chars | | TM | Time | str | "143025.000" (HHMMSS.FFFFFF) | | UI | Unique Identifier | UID | "1.2.840..." | | US | Unsigned Short | int | 512 | | OB | Other Byte | bytes | Binary data | | OW | Other Word | bytes | Binary data | | SQ | Sequence | Sequence | List of datasets |

Sequences

Sequences are nested datasets (like arrays of objects):

# Read a sequence
if "ReferencedStudySequence" in ds:
    for item in ds.ReferencedStudySequence:
        print(item.ReferencedSOPClassUID)
        print(item.ReferencedSOPInstanceUID)

# Create a sequence
from pydicom.dataset import Dataset
from pydicom.sequence import Sequence

item = Dataset()
item.ReferencedSOPClassUID = "1.2.840.10008.5.1.4.1.1.2"
item.ReferencedSOPInstanceUID = pydicom.uid.generate_uid()

ds.ReferencedStudySequence = Sequence([item])

Working with Pixel Data

Read Pixel Data as NumPy Array

import pydicom
import numpy as np

ds = pydicom.dcmread("ct_image.dcm")
pixels = ds.pixel_array  # numpy ndarray

# Apply rescale slope/intercept for CT (Hounsfield units)
if hasattr(ds, "RescaleSlope") and hasattr(ds, "RescaleIntercept"):
    hu = pixels * ds.RescaleSlope + ds.RescaleIntercept

Window/Level for Display

def apply_window(pixels, window_center, window_width):
    """Apply window/level to pixel data for display."""
    img_min = window_center - window_width // 2
    img_max = window_center + window_width // 2
    windowed = np.clip(pixels, img_min, img_max)
    windowed = ((windowed - img_min) / (img_max - img_min) * 255)
    return windowed.astype(np.uint8)

# Common CT windows
LUNG_WINDOW = (-600, 1500)      # center, width
BONE_WINDOW = (400, 1800)
SOFT_TISSUE = (40, 400)
BRAIN_WINDOW = (40, 80)

display = apply_window(hu, *SOFT_TISSUE)

Save as PNG

from PIL import Image

# For grayscale (CT, MR, CR)
img = Image.fromarray(display, mode="L")
img.save("output.png")

# For RGB (ultrasound, pathology)
if ds.PhotometricInterpretation == "RGB":
    img = Image.fromarray(pixels, mode="RGB")
    img.save("output.png")

Multi-frame Images

ds = pydicom.dcmread("multiframe.dcm")
pixels = ds.pixel_array  # Shape: (num_frames, rows, cols)

print(f"Frames: {ds.NumberOfFrames}")
print(f"Shape: {pixels.shape}")

# Access individual frames
frame_0 = pixels[0]

Transfer Syntaxes

Transfer syntaxes define how DICOM data is encoded (byte order, compression):

print(ds.file_meta.TransferSyntaxUID)

| UID | Name | Compression | |-----|------|-------------| | 1.2.840.10008.1.2 | Implicit VR Little Endian | None | | 1.2.840.10008.1.2.1 | Explicit VR Little Endian | None | | 1.2.840.10008.1.2.4.50 | JPEG Baseline | Lossy | | 1.2.840.10008.1.2.4.70 | JPEG Lossless | Lossless | | 1.2.840.10008.1.2.4.90 | JPEG 2000 Lossless | Lossless | | 1.2.840.10008.1.2.4.91 | JPEG 2000 | Lossy | | 1.2.840.10008.1.2.5 | RLE Lossless | Lossless |

Decompressing Pixel Data

# Install handlers for compressed transfer syntaxes
# pip install pylibjpeg pylibjpeg-libjpeg pylibjpeg-openjpeg

ds = pydicom.dcmread("compressed.dcm")
ds.decompress()  # Convert to uncompressed in-memory
pixels = ds.pixel_array

Converting Transfer Syntax

# Using DCMTK
# Decompress to Explicit VR Little Endian
dcmconv +te input.dcm output.dcm

# Compress to JPEG 2000 Lossless
dcmcjp2k +e2 input.dcm output.dcm

# Compress to JPEG Lossless
dcmcjpls +el input.dcm output.dcm

Creating DICOM Files

Create a DICOM File from Scratch

import pydicom
from pydicom.dataset import Dataset, FileDataset
from pydicom.uid import generate_uid, ExplicitVRLittleEndian
from pydicom.sequence import Sequence
import numpy as np
import datetime

# Create file dataset
filename = "new_image.dcm"
file_meta = pydicom.Dataset()
file_meta.MediaStorageSOPClassUID = "1.2.840.10008.5.1.4.1.1.2"  # CT
file_meta.MediaStorageSOPInstanceUID = generate_uid()
file_meta.TransferSyntaxUID = ExplicitVRLittleEndian

ds = FileDataset(filename, {}, file_meta=file_meta, preamble=b"\x00" * 128)

# Patient info
ds.PatientName = "Test^Patient"
ds.PatientID = "TEST001"
ds.PatientBirthDate = "19900101"
ds.PatientSex = "O"

# Study info
ds.StudyInstanceUID = generate_uid()
ds.StudyDate = datetime.date.today().strftime("%Y%m%d")
ds.StudyTime = datetime.datetime.now().strftime("%H%M%S")
ds.Modality = "CT"

# Series info
ds.SeriesInstanceUID = generate_uid()
ds.SeriesNumber = 1

# Instance info
ds.SOPClassUID = "1.2.840.10008.5.1.4.1.1.2"
ds.SOPInstanceUID = file_meta.MediaStorageSOPInstanceUID
ds.InstanceNumber = 1

# Image info
ds.Rows = 512
ds.Columns = 512
ds.BitsAllocated = 16
ds.BitsStored = 16
ds.HighBit = 15
ds.PixelRepresentation = 1  # signed
ds.SamplesPerPixel = 1
ds.PhotometricInterpretation = "MONOCHROME2"

# Pixel data
pixel_data = np.zeros((512, 512), dtype=np.int16)
ds.PixelData = pixel_data.tobytes()

ds.save_as(filename)

Bulk Operations

Iterate DICOM Files in a Directory

from pathlib import Path
import pydicom

def iter_dicom_files(directory: str):
    """Yield (path, dataset) for all DICOM files in a directory tree."""
    for path in Path(directory).rglob("*"):
        if path.is_file():
            try:
                ds = pydicom.dcmread(str(path), stop_before_pixels=True)
                yield path, ds
            except pydicom.errors.InvalidDicomError:
                continue

# Extract metadata from all files
for path, ds in iter_dicom_files("/data/studies"):
    print(f"{path}: {ds.PatientName} | {ds.Modality} | {ds.StudyDate}")

Group Files by Study/Series

from collections import defaultdict

studies = defaultdict(lambda: defaultdict(list))

for path, ds in iter_dicom_files("/data/incoming"):
    study_uid = ds.StudyInstanceUID
    series_uid = ds.SeriesInstanceUID
    studies[study_uid][series_uid].append(path)

for study_uid, series in studies.items():
    print(f"Study {study_uid}: {len(series)} series")
    for series_uid, files in series.items():
        print(f"  Series {series_uid}: {len(files)} instances")

Read Metadata Only (Fast)

# stop_before_pixels=True skips pixel data -- much faster for metadata-only operations
ds = pydicom.dcmread("large_image.dcm", stop_before_pixels=True)

Extract Specific Tags

# Read only specific tags (fastest for large datasets)
ds = pydicom.dcmread("image.dcm", specific_tags=[
    "PatientName", "PatientID", "StudyDate", "Modality",
    "StudyInstanceUID", "SeriesInstanceUID",
])

DCMTK Command-Line Tools

Common Commands

# Dump DICOM metadata
dcmdump image.dcm

# Dump specific tags
dcmdump +P "0010,0010" +P "0008,0060" image.dcm

# Modify tags
dcmodify -m "(0010,0010)=ANONYMOUS" image.dcm

# Convert transfer syntax
dcmconv +te input.dcm output.dcm    # To Explicit VR LE

# Validate DICOM conformance
dcmpschk image.dcm

# Send via C-STORE
storescu -v -aec REMOTE_AE host port image.dcm

# Query via C-FIND
findscu -v -aec REMOTE_AE host port -k "0008,0060=CT" -k "0010,0010=Smith*"

# Retrieve via C-MOVE
movescu -v -aec REMOTE_AE -aem MY_AE host port -k "0020,000D=1.2.3..."

Gotchas

PatientName uses ^ as separator: "Family^Given^Middle^Prefix^Suffix". Use str(ds.PatientName) for display, ds.PatientName.family_name for components.
Dates are strings, not date objects: StudyDate is "20250115", not a Python date. Parse with datetime.strptime(ds.StudyDate, "%Y%m%d").
UIDs must be globally unique: Always use pydicom.uid.generate_uid() when creating new studies/series/instances. Never reuse UIDs.
Pixel data may be compressed: Always handle the case where ds.pixel_array raises an error due to missing decompression handlers. Install pylibjpeg packages.
Private tags: Vendor-specific data uses odd group numbers (e.g., (0009,xxxx)). Access with ds[0x0009, 0x0010].
Encoding: DICOM defaults to ISO-IR 100 (Latin-1). Check SpecificCharacterSet for non-Latin text. pydicom handles decoding automatically.
File vs dataset: Use pydicom.dcmread() to read files. The returned FileDataset includes file meta information. For in-memory datasets, use Dataset() directly.
Modifying PixelData: If you modify pixel data, update Rows, Columns, BitsAllocated, BitsStored, HighBit, PixelRepresentation, and PhotometricInterpretation to match.

"DICOM Processing"