MATLAB Medical Imaging Toolbox

Expert skill for 3D medical image analysis using MATLAB's Medical Imaging Toolbox (MIT R2025b+).

Cross-Toolbox Note: For filtering, segmentation, and morphology operations, see matlab-image-processing-toolbox skill. MIT handles I/O, spatial referencing, registration, and medical-specific workflows.

When to Use This Skill

• Reading/writing DICOM series, NIfTI (.nii/.nii.gz), or NRRD files
• Creating medicalVolume objects with spatial referencing
• Converting between Patient (world) and Intrinsic (voxel) coordinates
• 3D volume visualization (volshow, sliceViewer)
• Medical image registration (rigid, affine, deformable, multimodal)
• Radiomics feature extraction (IBSI-compliant)
• AI-assisted segmentation with MedSAM or Cellpose
• PACS server integration for DICOM retrieval
• Multi-modality workflows (PET/CT fusion, MRI series)
• Ground truth labeling with Medical Image Labeler app

Read Before Coding

Task	Knowledge Card	Key Functions
Read DICOM series	file-io-dicom.md	medicalVolume, dicomread, dicomCollection
Load NIfTI/NRRD	file-io-nifti-nrrd.md	niftiread, nrrdread, niftiinfo
Create medical volume	medical-volume.md	medicalVolume, medicalImage, medicalref3d
Coordinate conversion	coordinate-systems.md	intrinsicToWorld, worldToIntrinsic
3D visualization	visualization-3d.md	volshow, sliceViewer, labelvolshow
Rigid registration	registration-rigid.md	imregmoment, imregicp, fitgeotform3d
Deformable registration	registration-deformable.md	imregdeform, imreggroupwise
Extract radiomics	radiomics-features.md	intensityFeatures, shapeFeatures, textureFeatures
Segment with MedSAM	segmentation-medsam.md	medicalSegmentAnythingModel
Cell detection	segmentation-cellpose.md	segmentCells2D, trainCellpose
Label ground truth	labeling-workflow.md	groundTruthMedical, Medical Image Labeler
PACS server access	pacs-integration.md	dicomConnection, dicomquery, dicomget
Apply filtering	cross-toolbox-ipt.md	→ See IPT skill
Threshold/segment	cross-toolbox-ipt.md	→ See IPT skill

Critical Rules

Rule 1: Coordinate Systems - The #1 Source of Errors

Medical images have TWO coordinate systems:

System	Units	Origin	Use Case
Intrinsic	Voxel indices (i,j,k)	Corner of first voxel	Array indexing, IPT functions
Patient/World	Physical units (mm)	DICOM-defined patient origin	Clinical measurements, registration

% WRONG: Mixing coordinate systems
pixel = V.Voxels(100, 200, 50);  % Intrinsic indexing
world_point = [100, 200, 50];    % Treating indices as world coords!

% CORRECT: Explicit conversion (separate coordinate arrays)
V = medicalVolume('scan.nii');
i = 100; j = 200; k = 50;  % Voxel indices
[x, y, z] = intrinsicToWorld(V.VolumeGeometry, i, j, k);
% x, y, z are now in mm, in patient coordinate system
world_point = [x, y, z];

% Convert back (also requires separate arrays)
[row, col, slice] = worldToSubscript(V.VolumeGeometry, x, y, z);

Rule 2: Always Use medicalVolume for Spatial Context

Never read raw voxels without spatial information:

% WRONG: Loses spatial referencing
data = niftiread('brain.nii');  % Just a 3D array, no spatial info

% CORRECT: Preserves geometry
V = medicalVolume('brain.nii');
% V.VolumeGeometry contains transformation matrix
% V.VoxelSpacing is in mm
% V.SpatialUnits documents the units

Rule 3: Check Orientation Before Slice Extraction

Slice orientation depends on acquisition, not array dimension:

V = medicalVolume('scan.dcm');

% Check orientation
disp(V.Orientation);        % 'transverse', 'sagittal', or 'coronal'
disp(V.PlaneMapping);       % Maps dimensions to anatomical planes

% Extract slices in patient coordinates (not array indices!)
slice = extractSlice(V, 50, 'transverse');  % 50th transverse slice

% For axial browsing regardless of acquisition:
sliceViewer(V);  % Automatically handles orientation

Rule 4: DICOM Metadata First

Always inspect metadata before processing:

% Get collection info for DICOM series
collection = dicomCollection('DICOM_folder');
disp(collection.Properties.VariableNames);

% Check modality, series description
info = dicominfo('slice001.dcm');
fprintf('Modality: %s\n', info.Modality);
fprintf('Series: %s\n', info.SeriesDescription);
fprintf('Spacing: %.2f x %.2f x %.2f mm\n', ...
    info.PixelSpacing(1), info.PixelSpacing(2), info.SliceThickness);

Rule 5: Memory Management for 3D Volumes

Large volumes (512×512×500+) require careful memory handling:

% Check volume size before loading
info = niftiinfo('large_scan.nii');
voxels = prod(info.ImageSize);
bytes_needed = voxels * info.BitsPerPixel / 8;
fprintf('Size: %.2f GB\n', bytes_needed / 1e9);

% Process slice-by-slice for huge volumes
for k = 1:V.NumTransverseSlices
    slice = extractSlice(V, k, 'transverse');
    % Process slice using IPT functions
    processed = imgaussfilt(slice, 1.5);  % IPT
    V = replaceSlice(V, processed, k, 'transverse');
end

Rule 6: Cross-Toolbox Workflow

MIT handles I/O and spatial referencing; IPT handles pixel-level processing:

% Load with MIT (preserves spatial info)
V = medicalVolume('scan.nii');

% Process with IPT (see matlab-image-processing-toolbox skill)
voxels = im2double(V.Voxels);
voxels = imgaussfilt3(voxels, 1.5);           % Gaussian smoothing
voxels = adapthisteq(voxels, 'NumTiles', [4 4 4]);  % CLAHE

% Segment with IPT
level = graythresh(voxels(:));
mask = voxels > level;
mask = imopen(mask, strel('sphere', 3));      % Morphological cleanup

% Create labeled volume with MIT (preserves spatial context)
labelVol = medicalVolume(uint8(mask), V.VolumeGeometry);
labelVol.Modality = 'SEG';
write(labelVol, 'segmentation.nii');

Quick Patterns

Load DICOM Series to medicalVolume

% From directory of DICOM files
V = medicalVolume('path/to/dicom_folder');

% From dicomCollection for multi-series
coll = dicomCollection('patient_folder', 'IncludeSubfolders', true);
disp(coll);  % Shows all series

% Load specific series
V = medicalVolume(coll, 'Rows', 2);  % 2nd series in collection

Extract Slice in Patient Coordinates

V = medicalVolume('brain.nii');

% Get middle slices in each plane
midT = round(V.NumTransverseSlices / 2);
midC = round(V.NumCoronalSlices / 2);
midS = round(V.NumSagittalSlices / 2);

transverse = extractSlice(V, midT, 'transverse');
coronal = extractSlice(V, midC, 'coronal');
sagittal = extractSlice(V, midS, 'sagittal');

% Display
figure;
subplot(1,3,1); imshow(transverse, []); title('Transverse');
subplot(1,3,2); imshow(coronal, []); title('Coronal');
subplot(1,3,3); imshow(sagittal, []); title('Sagittal');

Register Two Volumes

fixed = medicalVolume('pre_contrast.nii');
moving = medicalVolume('post_contrast.nii');

% Rigid registration (fast, for same-modality)
[registered, tform] = imregmoment(moving, fixed);

% Affine registration with optimization
[optimizer, metric] = imregconfig('monomodal');
tform = imregtform(moving.Voxels, moving.VolumeGeometry, ...
                   fixed.Voxels, fixed.VolumeGeometry, ...
                   'affine', optimizer, metric);

% Apply transformation
registered = imwarp(moving.Voxels, tform, 'OutputView', ...
    imref3d(size(fixed.Voxels)));

Extract Radiomics Features

V = medicalVolume('tumor_scan.nii');
mask = medicalVolume('tumor_mask.nii');

% Resample to isotropic (required for some features)
V_iso = resample(V, [1 1 1]);  % 1mm isotropic
mask_iso = resample(mask, [1 1 1]);

% Create radiomics object first (required pattern)
R = radiomics(V_iso.Voxels, mask_iso.Voxels > 0);

% Extract IBSI-compliant features as methods of radiomics object
intensity = intensityFeatures(R);
shape = shapeFeatures(R);
texture = textureFeatures(R);

% Combine into table
features = [intensity, shape, texture];

Segment with MedSAM

% Load model (requires support package)
model = medicalSegmentAnythingModel;

% Load image and extract embeddings
V = medicalVolume('liver_ct.nii');
slice = extractSlice(V, 50, 'transverse');
embeddings = extractEmbeddings(model, slice);

% Get image size (required parameter)
imageSize = size(slice);

% Segment with bounding box prompt (imageSize is required)
bbox = [100, 100, 200, 150];  % [x, y, width, height]
[mask, score] = segmentObjectsFromEmbeddings(model, embeddings, imageSize, ...
    BoundingBox=bbox);

Function Quick Reference

File I/O

Function	Purpose	Example
medicalVolume	Load 3D medical image	V = medicalVolume('scan.nii')
medicalImage	Load 2D medical image series	I = medicalImage('us_video.dcm')
dicomread	Read single DICOM file	img = dicomread('slice.dcm')
dicominfo	Read DICOM metadata	info = dicominfo('slice.dcm')
dicomCollection	Catalog DICOM folders	coll = dicomCollection(folder)
niftiread	Read NIfTI file	data = niftiread('brain.nii')
nrrdread	Read NRRD file	data = nrrdread('scan.nrrd')
write	Write medicalVolume to NIfTI	write(V, 'output.nii')

Spatial Referencing

Function	Purpose	Example
medicalref3d	Create spatial reference	R = medicalref3d(volumeSize, voxelSpacing)
intrinsicToWorld	Voxel to patient coords	[X,Y,Z] = intrinsicToWorld(R, I, J, K)
worldToIntrinsic	Patient to voxel coords	[I,J,K] = worldToIntrinsic(R, X, Y, Z)
worldToSubscript	Patient coords to indices	[row,col,slice] = worldToSubscript(R, X, Y, Z)
extractSlice	Get slice in patient space	s = extractSlice(V, n, 'transverse')
resample	Resample to new resolution	V2 = resample(V, [1 1 1])

Visualization

Function	Purpose	Example
volshow	3D volume rendering	volshow(V) or volshow(V, OverlayData=L)
sliceViewer	Orthogonal slice browser	sliceViewer(V)
labelvolshow	REMOVED in R2025b	Use volshow(V, OverlayData=labels) instead
montage	2D slice montage	montage(V.Voxels)
implay	Video playback (2D series)	implay(I)

Registration

Function	Purpose	Example
imregmoment	Fast moment-based rigid	[tform, reg] = imregmoment(moving, fixed)
imregicp	Iterative closest point	[regSurf, tform] = imregicp(surf1, surf2)
imregdeform	Deformable registration	D = imregdeform(moving, fixed)
imreggroupwise	Groupwise registration	tforms = imreggroupwise(volumes)
fitgeotform3d	Landmark-based transform	tform = fitgeotform3d(pts1, pts2, 'affine')

Radiomics

Function	Purpose	Example
radiomics	Create radiomics object	R = radiomics(data, roi)
intensityFeatures	Histogram-based features	F = intensityFeatures(R)
shapeFeatures	3D shape descriptors	F = shapeFeatures(R)
textureFeatures	GLCM, GLRLM, etc.	F = textureFeatures(R)

AI Segmentation

Function	Purpose	Example
medicalSegmentAnythingModel	Load MedSAM	model = medicalSegmentAnythingModel
extractEmbeddings	Compute image embeddings	emb = extractEmbeddings(model, img)
segmentObjectsFromEmbeddings	Segment with prompts	mask = segmentObjectsFromEmbeddings(...)
segmentCells2D*	Cellpose 2D segmentation	L = segmentCells2D(img, 'cyto')
segmentCells3D*	Cellpose 3D segmentation	L = segmentCells3D(V, 'nuclei')
trainCellpose*	Train custom Cellpose	model = trainCellpose(ds, opts)

* Cellpose functions require the "Deep Learning Toolbox Model for Cellpose" support package. Install via Add-On Explorer.

PACS Integration

Function	Purpose	Example
dicomConnection	Connect to PACS	conn = dicomConnection(ip, port)
dicomquery	Query PACS for studies	results = dicomquery(conn, 'Study')
dicomget	Retrieve images	dicomget(conn, results, folder)
dicomstore	Send images to PACS	dicomstore(conn, folder)

Knowledge Cards

Detailed documentation organized by topic:

File I/O:

• knowledge/cards/file-io-dicom.md - DICOM reading, metadata, series handling
• knowledge/cards/file-io-nifti-nrrd.md - NIfTI and NRRD formats

Core Concepts:

• knowledge/cards/medical-volume.md - medicalVolume class and properties
• knowledge/cards/coordinate-systems.md - Patient vs Intrinsic coordinates (CRITICAL)
• knowledge/cards/visualization-3d.md - volshow, sliceViewer, rendering options

Registration:

• knowledge/cards/registration-rigid.md - Rigid/affine registration methods
• knowledge/cards/registration-deformable.md - Deformable and groupwise registration

Analysis:

• knowledge/cards/radiomics-features.md - IBSI-compliant radiomics extraction
• knowledge/cards/segmentation-medsam.md - Medical Segment Anything Model
• knowledge/cards/segmentation-cellpose.md - Cellpose for microscopy

Workflows:

• knowledge/cards/labeling-workflow.md - Medical Image Labeler app
• knowledge/cards/pacs-integration.md - PACS server communication
• knowledge/cards/cross-toolbox-ipt.md - Using IPT functions with MIT

Cross-Toolbox Integration

For filtering, segmentation, and morphology → matlab-image-processing-toolbox

MIT Task	IPT Function	IPT Knowledge Card
Denoise volume slices	imgaussfilt, medfilt2, wiener2	filtering-denoising.md
Enhance contrast	adapthisteq, imadjust	filtering-denoising.md
Threshold volume	graythresh, imbinarize	segmentation-thresholding.md
Clean binary masks	imopen, imclose, imfill, bwareaopen	morphology-binary.md
Measure regions	regionprops3, bwconncomp	feature-regions.md
Detect edges	edge, imgradient	feature-edges.md
Data type conversion	im2double, im2uint8	data-types.md

For wavelet-based denoising → matlab-wavelet-toolbox

% Combined workflow: MIT + IPT + Wavelet
V = medicalVolume('noisy_mri.nii');           % MIT: Load with spatial info

% Process each slice (IPT + Wavelet)
for k = 1:size(V.Voxels, 3)
    slice = im2double(V.Voxels(:,:,k));       % IPT: Convert type
    slice = wdenoise2(slice);                  % Wavelet: Denoise
    slice = adapthisteq(slice);                % IPT: Enhance
    V.Voxels(:,:,k) = slice;
end

write(V, 'enhanced_mri.nii');                  % MIT: Write with spatial info

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Source: MathWorks Medical Imaging Toolbox Documentation (R2025b)
User Guide: 468 pages | Function Reference: 362 pages