Guide

DICOM Medical Imaging Format: The Complete Guide

PC By Pablo Cirre • Updated May 7, 2026

What Is DICOM?

DICOM (Digital Imaging and Communications in Medicine) is the international standard for medical images and related information. Developed by the American College of Radiology (ACR) and the National Electrical Manufacturers Association (NEMA) since 1985 (initially as ACR-NEMA), DICOM PS3.x defines everything about how medical images are stored, transmitted, and displayed — from CT scans and MRIs to ultrasounds, X-rays, mammograms, and endoscopy images.

A DICOM file is not just an image — it is an information object combining pixel data with rich metadata: patient identity, acquisition parameters, institution information, equipment calibration data, and clinical context. This comprehensive metadata is what distinguishes DICOM from consumer image formats like JPEG or PNG, and what makes it medically and legally essential.

DICOM is ubiquitous in healthcare: every hospital PACS (Picture Archiving and Communication System), every radiology workstation, every radiotherapy planning system, and every modality (CT, MRI, PET, ultrasound) produces and consumes DICOM. The standard is maintained by the DICOM Standards Committee and updated continuously.

DICOM File Structure

File Meta Information (Preamble + DICOM Header)

Every DICOM file starts with:

Bytes 0–127:  128-byte preamble (zeros by convention, sometimes ASCII text)
Bytes 128–131: "DICM" magic (0x44 0x49 0x43 0x4D)
Bytes 132+:   File Meta Information dataset (Group 0002 attributes)

The File Meta Information contains:

(0002,0001) File Meta Information Version: 2 bytes, always 0x00 0x01
(0002,0002) Media Storage SOP Class UID: What type of image (CT Image, MR Image, etc.)
(0002,0003) Media Storage SOP Instance UID: Globally unique ID for this specific image
(0002,0010) Transfer Syntax UID: Encoding rules (byte order, compression)

Data Elements

After the File Meta Information, the DICOM dataset consists of data elements in Tag-Length-Value (TLV) format:

|-- 4 bytes --|-- 2 bytes --|-- 2 or 4 bytes --|-- N bytes --|
|  Tag (GG,EE)|  VR        |  Length          |  Value      |

Tag: Two 16-bit unsigned integers: Group (GG) and Element (EE). Tags are written as (GGGG,EEEE).
VR (Value Representation): 2-byte ASCII code describing data type
Length: Size of value in bytes
Value: The actual data

Example tags:

(0008,0060) CS  Modality                     = "CT"
(0008,0020) DA  Study Date                   = "20240315"
(0010,0010) PN  Patient Name                 = "Doe^John^A"
(0010,0020) LO  Patient ID                   = "PAT-123456"
(0010,0030) DA  Patient Birth Date           = "19750822"
(0010,0040) CS  Patient Sex                  = "M"
(0020,000D) UI  Study Instance UID           = "1.2.840.10008.5.1.4.1.1..."
(0028,0010) US  Rows                         = 512
(0028,0011) US  Columns                      = 512
(0028,0030) DS  Pixel Spacing                = "0.703125\0.703125"
(0028,0100) US  Bits Allocated               = 16
(0028,0103) US  Pixel Representation         = 0  (unsigned)
(7FE0,0010) OW  Pixel Data                   = [binary pixel data]

Value Representations (VR)

VR	Name	Description
`CS`	Code String	Short uppercase string (max 16 chars): "CT", "MR", "M", "F"
`DA`	Date	8-char date: "YYYYMMDD"
`DS`	Decimal String	Floating-point as string: "0.703125"
`DT`	DateTime	14-26 char datetime string
`IS`	Integer String	Integer as string: "256"
`LO`	Long String	64-char string (patient ID, series description)
`PN`	Person Name	"Family^Given^Middle^Prefix^Suffix"
`SQ`	Sequence	Nested dataset (for complex structures)
`UI`	Unique Identifier	UID string: "1.2.840.10008.1.2.1"
`US`	Unsigned Short	2-byte unsigned int (0–65535)
`SS`	Signed Short	2-byte signed int (−32768–32767)
`UL`	Unsigned Long	4-byte unsigned int
`OW`	Other Word	Binary pixel data
`OB`	Other Byte	Binary data

Transfer Syntaxes

The Transfer Syntax UID (0002,0010) determines byte order and compression:

UID	Name	Byte Order	Compression
1.2.840.10008.1.2	Implicit VR Little Endian	Little	None (legacy)
1.2.840.10008.1.2.1	Explicit VR Little Endian	Little	None (default)
1.2.840.10008.1.2.2	Explicit VR Big Endian	Big	None (retired)
1.2.840.10008.1.2.4.50	JPEG Baseline (8-bit)	Little	JPEG lossy
1.2.840.10008.1.2.4.70	JPEG Lossless	Little	JPEG lossless
1.2.840.10008.1.2.4.90	JPEG 2000 Lossless	Little	JPEG 2000
1.2.840.10008.1.2.4.91	JPEG 2000 Lossy	Little	JPEG 2000
1.2.840.10008.1.2.5	RLE Lossless	Little	RLE

Explicit VR Little Endian (1.2.840.10008.1.2.1) is the default for modern DICOM files. Implicit VR (where VR must be inferred from the data dictionary) is a legacy format still found in older archives.

CT and MRI Pixel Data

CT Hounsfield Units (HU)

CT images store pixel values in Hounsfield Units, a standardized radiodensity scale:

Tissue	HU Range
Air	−1000
Lung	−600 to −400
Fat	−120 to −90
Water	0
Soft tissue	+20 to +80
Bone	+400 to +1000
Dense bone/metal	+1000 to +3000

Pixel values are stored as 12 or 16-bit integers. The DICOM tags (0028,1052) Rescale Intercept and (0028,1053) Rescale Slope convert stored values to HU:

HU = stored_pixel_value × RescaleSlope + RescaleIntercept

Typical: slope=1, intercept=−1024 → stored value 1024 = 0 HU (water).

Window Center and Width

Displaying HU-range data on 8-bit screens requires windowing:

(0028,1050) Window Center: The HU value mapped to 50% brightness
(0028,1051) Window Width: The HU range mapped to 0–100% brightness

Standard window presets:

Abdomen: W=350, C=40
Lung: W=1500, C=−600
Brain: W=80, C=40
Bone: W=2000, C=300

DICOM Networking (DIMSE Services)

DICOM is also a network protocol. The DIMSE (DICOM Message Service Element) defines services:

C-STORE: Send an image from one system to another
C-FIND: Query for studies/series/instances (like a database query)
C-MOVE: Request a remote system to send images to a third system
C-GET: Request images directly (pull from server)
C-ECHO: Ping (verify connection)

Each DICOM network entity is an AE (Application Entity) identified by its AE Title (up to 16 characters). DICOM runs over TCP/IP, typically port 104 (or 11112 for non-root).

WADO-RS (RESTful DICOM)

The modern API for DICOM retrieval is WADO-RS (Web Access to DICOM Objects, RESTful):

# Retrieve a study
GET /wado/rs/studies/{StudyInstanceUID}

# Retrieve a series
GET /wado/rs/studies/{StudyUID}/series/{SeriesUID}

# Retrieve a specific instance
GET /wado/rs/studies/{StudyUID}/series/{SeriesUID}/instances/{SOPInstanceUID}

# Retrieve rendered JPEG (thumbnail)
GET /wado/rs/studies/{StudyUID}/series/{SeriesUID}/instances/{SOPInstanceUID}/rendered?accept=image/jpeg

Working with DICOM Files

pydicom (Python)

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

# Load a DICOM file
ds = pydicom.dcmread('CT_slice.dcm')

# Access metadata
print(ds.PatientName)        # "Doe^John"
print(ds.Modality)           # "CT"
print(ds.StudyDate)          # "20240315"
print(ds.Rows, ds.Columns)   # 512, 512
print(ds.PixelSpacing)       # [0.703125, 0.703125]

# Get pixel array (applies RescaleSlope/Intercept)
pixel_array = ds.pixel_array  # raw stored values
hu_array = pixel_array * ds.RescaleSlope + ds.RescaleIntercept  # HU

# Apply windowing and display
window_center = 40   # abdomen
window_width  = 350
img_min = window_center - window_width / 2
img_max = window_center + window_width / 2
windowed = np.clip(hu_array, img_min, img_max)
windowed = (windowed - img_min) / (img_max - img_min) * 255

plt.imshow(windowed, cmap='gray')
plt.axis('off')
plt.savefig('ct_slice.png', bbox_inches='tight', pad_inches=0)

Converting DICOM to PNG/JPEG

import pydicom
from PIL import Image
import numpy as np

def dicom_to_png(dcm_path, png_path, window_center=40, window_width=400):
    ds = pydicom.dcmread(dcm_path)
    arr = ds.pixel_array.astype(float)

    # Apply rescale
    slope = getattr(ds, 'RescaleSlope', 1)
    intercept = getattr(ds, 'RescaleIntercept', 0)
    arr = arr * slope + intercept

    # Window
    img_min = window_center - window_width / 2
    img_max = window_center + window_width / 2
    arr = np.clip(arr, img_min, img_max)
    arr = ((arr - img_min) / (img_max - img_min) * 255).astype(np.uint8)

    Image.fromarray(arr).save(png_path)

dicom_to_png('CT_slice.dcm', 'output.png')

DCMTK Command-Line Tools

# Display DICOM tags
dcmdump CT_slice.dcm | head -50

# Convert DICOM to PNG (with auto windowing)
dcm2pnm --write-png CT_slice.dcm output.png

# Anonymize DICOM (remove patient data)
dcmodify -ie "(0010,0010)" -ie "(0010,0020)" -ie "(0010,0030)" CT_slice.dcm

# Change transfer syntax (compress with JPEG lossless)
dcmcjpeg CT_slice.dcm CT_compressed.dcm

Converting to NIfTI (Neuroimaging)

# dcm2niix — converts DICOM series to NIfTI for neuroimaging analysis
dcm2niix -z y -f "scan_%p_%t" -o ./output ./dicom_series/

Privacy and Anonymization

DICOM files contain extensive PHI (Protected Health Information). Before sharing DICOM files for research, education, or development, always anonymize:

Required removals (HIPAA Safe Harbor):

Patient name, ID, birth date, address
Study date (replace with days-since-epoch or offset)
Institution name, physician name
Device serial numbers
UIDs (replace with new generated UIDs)

Tools: DICOM Confidentiality Option (PS3.15), pydicom deidentify(), DCMTK dcmodify, Horos/OsiriX DICOM anonymizer, CTP (Clinical Trials Processor).

DICOM Viewers

Tool	Platform	Use Case
Horos	macOS	Clinical review, 3D reconstruction
OsiriX	macOS	Same (Horos fork)
3D Slicer	Cross-platform	Research, segmentation, 3D visualization
Weasis	Cross-platform (Java)	Web/desktop PACS viewer
dwv	Browser	JavaScript DICOM viewer
MicroDicom	Windows	Simple viewer
RadiAnt	Windows	Fast clinical viewer

DICOM's four-decade legacy as the universal medical imaging language — combined with active standardization — makes it essential knowledge for anyone working in health informatics, medical AI, or imaging system integration.

Related conversions

Frequent conversions across the catalogue:

Frequently Asked Questions

DICOM files (.dcm) require specialized software since standard image viewers cannot display them (they contain 16-bit pixel data and complex metadata). Free options: 3D Slicer (cross-platform, excellent for research), Horos or OsiriX (macOS), MicroDicom (Windows), Weasis (cross-platform Java). For programmatic access, pydicom (Python) is the standard library: `import pydicom; ds = pydicom.dcmread("scan.dcm"); print(ds.pixel_array.shape)`. Many DICOM files have no extension — any file with the "DICM" magic at byte offset 128 is a DICOM file.

DICOM arquivos (.dcm) require specialized software since padrão image viewers cannot display them (they contain 16-bit pixel data e complexo metadata). grátis options: 3D Slicer (cross-platform, excellent para research), Horos ou OsiriX (macOS), MicroDicom (Windows), Weasis (cross-platform Java). para programmatic access, pydicom (Python) is the padrão library: `import pydicom; ds = pydicom.dcmread("scan.dcm"); print(ds.pixel_array.shape)`. Many DICOM arquivos have no extension — any arquivo com the "DICM" magic at byte offset 128 is a DICOM file.

DICOM Dateien (.dcm) require specialized Software since Standard image viewers cannot display them (they contain 16-bit pixel data und complex metadata). kostenlos options: 3D Slicer (cross-platform, excellent für research), Horos oder OsiriX (macOS), MicroDicom (Windows), Weasis (cross-platform Java). für programmatic access, pydicom (Python) is the Standard library: `import pydicom; ds = pydicom.dcmread("scan.dcm"); print(ds.pixel_array.shape)`. Many DICOM Dateien have no extension — any Datei mit the "DICM" magic at byte offset 128 is a DICOM file.

DICOM archivos (.dcm) require specialized software since estándar image viewers cannot display them (they contain 16-bit pixel data y complex metadata). gratis options: 3D Slicer (cross-platform, excellent para research), Horos o OsiriX (macOS), MicroDicom (Windows), Weasis (cross-platform Java). para programmatic access, pydicom (Python) is the estándar library: `import pydicom; ds = pydicom.dcmread("scan.dcm"); print(ds.pixel_array.shape)`. Many DICOM archivos have no extension — any archivo con the "DICM" magic at byte offset 128 is a DICOM file.

On KaijuConverter every file is processed inside an isolated container, encrypted in transit (TLS 1.3) and at rest, and automatically deleted after 60 minutes with multi-pass overwrite. We never train on, share, or analyze user content. For maximum privacy on extremely sensitive material, prefer offline tools (ImageMagick, FFmpeg, LibreOffice) that you control end-to-end.

Yes, but you lose critical information. DICOM stores 12–16 bit pixel data (CT Hounsfield Units, MRI signal intensities) that must be "windowed" to display correctly on an 8-bit screen. The conversion requires choosing a window center and window width — different clinical contexts use different windows (brain, lung, bone, abdomen). Use pydicom + PIL in Python, or the DCMTK tool `dcm2pnm --write-png`. Never use converted images for clinical diagnosis; they may hide clinically relevant features outside the chosen window.

Sim, mas you lose critical information. DICOM stores 12–16 bit pixel data (CT Hounsfield Units, MRI signal intensities) that must be "windowed" to display correctly on an 8-bit screen. The conversion requires choosing a window center e window width — different clinical contexts usar different windows (brain, lung, bone, abdomen). usar pydicom + PIL in Python, ou the DCMTK tool `dcm2pnm --write-png`. Never usar convertido images para clinical diagnosis; they may hide clinically relevant features outside the chosen window.

Ja, aber you lose critical information. DICOM stores 12–16 bit pixel data (CT Hounsfield Units, MRI signal intensities) that must be "windowed" to display correctly on an 8-bit screen. The conversion requires choosing a window center und window width — different clinical contexts verwenden different windows (brain, lung, bone, abdomen). verwenden pydicom + PIL in Python, oder the DCMTK tool `dcm2pnm --write-png`. Never verwenden umgewandelt images für clinical diagnosis; they may hide clinically relevant features outside the chosen window.

Sí, pero you lose critical information. DICOM stores 12–16 bit pixel data (CT Hounsfield Units, MRI signal intensities) that must be "windowed" to display correctly on an 8-bit screen. The conversion requires choosing a window center y window width — different clinical contexts usar different windows (brain, lung, bone, abdomen). usar pydicom + PIL in Python, o the DCMTK tool `dcm2pnm --write-png`. Never usar convertido images para clinical diagnosis; they may hide clinically relevant features outside the chosen window.

For 95% of use cases, yes — server-side ImageMagick, FFmpeg and LibreOffice produce identical output to the same tools on your laptop. Desktop software wins for: extremely large files (multi-GB), batch jobs of thousands of files, scripted pipelines, or content too sensitive to upload. KaijuConverter caps at 25 MB per file on the free tier (up to 2 GB on paid plans).

Extensive PHI (Protected Health Information): patient name, date of birth, patient ID, study date and time, referring physician name, institution name, equipment serial numbers, and unique identifiers traceable to the patient. Before sharing DICOM files publicly (research, education, demos), they must be anonymized. DICOM PS3.15 defines the Confidentiality Option for standardized de-identification. In Python, pydicom's deidentify module and tools like DCMTK dcmodify can remove PHI fields systematically.

Extensive PHI (Protected Health Information): patient name, date of birth, patient ID, study date e time, referring physician name, institution name, equipment serial numbers, e unique identifiers traceable para o patient. antes compartilhando DICOM arquivos publicly (research, education, demos), they must be anonymized. DICOM PS3.15 defines the Confidentiality Option para padrãoized de-identification. In Python, pydicom's deidentify module e ferramentas like DCMTK dcmodify can remove PHI fields systematically.

Extensive PHI (Protected Health Information): patient name, date von birth, patient ID, study date und time, referring physician name, institution name, equipment serial numbers, und unique identifiers traceable zum patient. vor sharing DICOM Dateien publicly (research, education, demos), they must be anonymized. DICOM PS3.15 defines the Confidentiality Option für Standardized de-identification. In Python, pydicom's deidentify module und Werkzeuge like DCMTK dcmodify can remove PHI fields systematically.

Extensive PHI (Protected Health Information): patient name, date de birth, patient ID, study date y time, referring physician name, institution name, equipment serial numbers, y unique identifiers traceable al patient. antes sharing DICOM archivos publicly (research, education, demos), they must be anonymized. DICOM PS3.15 defines the Confidentiality Option para estándarized de-identification. In Python, pydicom's deidentify module y herramientas like DCMTK dcmodify can remove PHI fields systematically.

Most format conversions are lossy by design — JPG, MP3, MP4, WebP all discard perceptual data to save bytes. Going through a lossy intermediate compounds the loss. To minimize visible/audible drift: convert from the original master, choose a higher quality setting, and avoid converting back and forth between lossy formats.

DICOM is the clinical standard — used in hospitals, PACS systems, and medical devices. Each DICOM file is one image slice with full metadata. NIfTI (.nii, .nii.gz) is the research standard for neuroimaging — a single file containing an entire 3D brain volume with a simple header. NIfTI is used in neuroimaging analysis tools (FSL, FreeSurfer, SPM). Convert DICOM brain series to NIfTI with `dcm2niix`. For clinical workflows use DICOM; for research analysis pipelines use NIfTI.

DICOM is the clinical padrão — used in hospitals, PACS systems, e medical devices. Each DICOM arquivo é one image slice com full metadata. NIfTI (.nii, .nii.gz) is the research padrão para neuroimaging — a single arquivo containing an entire 3D brain volume com a simples header. NIfTI is used in neuroimaging analysis ferramentas (FSL, grátisSurfer, SPM). converter DICOM brain series to NIfTI com `dcm2niix`. para clinical workflows usar DICOM; para research analysis pipelines usar NIfTI.

DICOM is the clinical Standard — used in hospitals, PACS systems, und medical devices. Each DICOM Datei is one image slice mit full metadata. NIfTI (.nii, .nii.gz) is the research Standard für neuroimaging — a single Datei containing an entire 3D brain volume mit a einfach header. NIfTI is used in neuroimaging analysis Werkzeuge (FSL, kostenlosSurfer, SPM). umwandeln DICOM brain series to NIfTI mit `dcm2niix`. für clinical workflows verwenden DICOM; für research analysis pipelines verwenden NIfTI.

DICOM is the clinical estándar — used in hospitals, PACS systems, y medical devices. Each DICOM archivo is one image slice con full metadata. NIfTI (.nii, .nii.gz) is the research estándar para neuroimaging — a single archivo containing an entire 3D brain volume con a simple header. NIfTI is used in neuroimaging analysis herramientas (FSL, gratisSurfer, SPM). convertir DICOM brain series to NIfTI con `dcm2niix`. para clinical workflows usar DICOM; para research analysis pipelines usar NIfTI.

Yes — KaijuConverter accepts multiple files in a single drop and returns a ZIP. For very large batches (thousands of files) consider command-line tools or our API: <code>find . -name "*.heic" -exec magick {} {.}.jpg \;</code> or similar one-liners scale to millions of files when run locally.