Matlab Process Images
MathWorksOfficialCreated by MathWorks
- Skill
- Tool integration
- Software development
- BSD-2-Clause
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AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill serves as the primary entry point for MATLAB tasks involving images, scans, frames, and 3-D volumes. It provides core conventions for image data types, coordinate systems, and display practices, while routing users to specific references for tasks such as I/O, filtering, segmentation, and measurement. It ensures consistent use of Image Processing Toolbox functions and helps users navigate common pitfalls in image analysis workflows.
What it does
- Provides standardized conventions for image data types and coordinate systems.
- Routes users to specialized references for image I/O, filtering, and transformation tasks.
- Defines best practices for displaying images using the imageshow function.
- Offers guidance on handling 3-D volumetric data and large-scale images.
- Assists with selecting appropriate functions for segmentation and object measurement.
- Provides troubleshooting patterns for common image processing errors.
When to use it
- When performing image processing tasks in MATLAB, such as filtering, enhancing, or segmenting.
- When working with image formats like TIFF, PNG, JPEG, DICOM, or NIfTI.
- When needing to understand MATLAB-specific coordinate conventions for image data.
- When determining the correct toolbox function for a specific image analysis goal.
- When troubleshooting syntax or type-related errors in image processing scripts.
Advantages
- Ensures consistent coding patterns across different image processing tasks.
- Reduces errors by providing clear guidance on coordinate and data type conventions.
- Simplifies the selection of appropriate tools for complex workflows like 3-D volume processing.
- Promotes the use of modern display functions like imageshow over legacy alternatives.
- Provides a structured approach to identifying the correct documentation for specific functions.
Usage examples
- “How do I read a DICOM file and display it in MATLAB?”
- The agent will guide you to use the medical-data-io reference for reading the file and the imageshow function for display, following the core conventions.
- “I need to count the number of objects in a binary image.”
- The agent will provide the standard analysis pipeline using bwconncomp and regionprops, ensuring the input is correctly formatted as logical.
- “How do I perform 3-D filtering on a volume?”
- The agent will direct you to the 3-D volume processing reference to select the appropriate 3-D specific function, such as imgaussfilt3.
- “Why is my regionprops measurement returning only one object?”
- The agent will explain that the input must be a logical array and provide the code to convert your numeric array to logical.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/matlab/matlab-process-images/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip matlab-process-images.zip -d .agents/skills
Documentation by MathWorks
Image Processing
Process, analyze, and transform images using MATLAB Image Processing Toolbox (IPT). This skill routes you to the correct reference for your task and provides core conventions that apply across all IPT work.
When to Use
Loaded for general-purpose IPT work: file and batch I/O, filtering, restoration, morphology, segmentation, measurement, geometric transforms, color, texture, quality metrics, deep learning on images, and image apps. Non-medical microscopy — cell and particle counting on plain IPT — is in scope.
Hand off (and co-load) instead of answering alone: see the handoff table under ## Routing.
When NOT to Use
Training a network with no image-specific preprocessing — use Deep Learning Toolbox directly
Simulink or Stateflow video blocks — use Simulink directly
C/C++ code generation, MEX, GPU acceleration and deployment — use MATLAB Coder / GPU Coder directly
Point clouds, 3-D reconstruction, and video stabilization — use Computer Vision Toolbox directly
General MATLAB questions unrelated to images
Looking at an image yourself (e.g., "look at this screenshot") when no MATLAB work is requested — use your own vision instead
Core Conventions
Image Data Types
| Type | Range | When to use |
|---|---|---|
uint8 | 0–255 | Display, file I/O, memory-efficient storage |
uint16 | 0–65535 | High dynamic range sensors, medical. Camera RAW is n-bit in a uint16 container — rescale with rawinfo BlackLevel/WhiteLevel, not 65535 |
single / double | 0.0–1.0 | Arithmetic, filtering, computation |
logical | 0 or 1 | Binary masks, segmentation results |
Most IPT functions accept integer types directly — do not convert to floating-point unless required.
Default Coordinate Conventions
MATLAB images are (row, col) — row is Y (down), column is X (right)
size(img)returns[height, width, channels]Spatial functions use (x, y) order for coordinates (e.g.,
imref2d,regionpropscentroids)Pixel centers are at integer intrinsic coordinates; in default world referencing, pixel (1,1) center is at world (1.0, 1.0) and the image edge starts at 0.5
imref2dandimref3dcan specify non-default spatial referencing to place images into world coordinate systems. By default they use the unit-spaced, integer-pixel-centered coordinate system described here, e.g.Rout = imref2d(imageSize). Functions that do not accept spatial referencing objects assume this intrinsic coordinate system for any operation requiring spatial referencing.
Multichannel Images
Grayscale:
M×NorM×N×1RGB:
M×N×3With alpha: read using
[img, ~, alpha] = imread(...)(alpha is the third output)Convert:
rgb2gray,im2gray(preferred, handles more input types)
Display Functions — use imageshow, not imshow
Always use imageshow instead of imshow in all generated code — scripts, apps, examples, and quick previews. The signatures are compatible for common cases: replace imshow(...) with imageshow(...). imageshow (R2024b+) handles sub-bit-depth containers (e.g., 12-bit in uint16) via DisplayRangeMode, supports OverlayData for label/mask overlays, and works with viewer2d. imshow lacks all of these capabilities and should not appear in new code.
Title convention: imageshow creates a viewer2d, not an axes — gca does not return it. To add a title, get the viewer from the Image object's parent:
obj = imageshow(im);
viewer = obj.Parent;
title(viewer, "Title Text");Do not use title("text") or title(gca, "text") after imageshow — these silently fail or create a stray axes title.
Code Execution
Execute code blocks via evaluate_matlab_code before presenting them to the user. If it errors, fix and re-run. This catches type mismatches, wrong argument orders, and nonexistent functions before the user sees them. If execution requires user data or toolboxes not available in the current session, present the code with a note that it should be run with the user's own data — do not loop trying to synthesize stand-in data.
Error Recovery
If code throws an error after execution, load the matlab-read-documentation skill and look up the function's doc page to verify syntax, required arguments, and supported input types before retrying.
When to Ask the User
Ask for clarification before proceeding when:
The image type/format is ambiguous and affects the processing approach
Multiple valid approaches exist with different tradeoffs (speed vs quality, lossy vs lossless)
The task requires choosing parameters that depend on domain knowledge (e.g., object size thresholds, noise characteristics)
The user's MATLAB release is unknown and the recommended function may not be available
Routing
Match your task in the tables below. Check the handoff table first. If the task belongs to another skill, load that skill in addition to this one — the Core Conventions above (data types, coordinate order, code execution) apply to all image data. Do not re-derive them from the sibling. If a row in both tables fits, the handoff table wins on workflow; this skill still owns conventions.
Unfamiliar data? Load
references/image-understanding.mdfirst — but only when class, range, or content is unknown.Starting from a function name, not a task? The function column is illustrative, not exhaustive. To locate any function:
grep -rln '<functionName>' references/. To look up syntax details, loadmatlab-read-documentation.Pipeline tasks: load several references; cap at three unless the task genuinely spans more.
Follow the patterns in loaded references. References contain specific code patterns and workflows — use them instead of improvising equivalent code from general knowledge.
No row matches? Name the owning product, state the closest IPT fallback, and say so — do not guess a reference.
Check first: is this another skill's job?
| If the task involves... | Also load |
|---|---|
| Segmenting anatomy — tumor, lesion, organ, vessel — in CT, MRI, PET, ultrasound; MedSAM; radiomics | No skill covers this — Medical Imaging Toolbox (segmentMedicalImage, groundTruthMedical); look up the doc page |
Displaying or viewing a 2-D image: imshow, imageshow, viewer2d, montage, mask overlay, annotation, colorbar, colormap, figure export | matlab-display-image |
Displaying a 3-D volume: volshow, viewer3d, isosurface rendering, slice viewer, orthogonal views | matlab-display-volume |
| Hyperspectral or multispectral cubes, spectral indices, unmixing, ENVI, band selection | matlab-analyze-spectral-images |
| Calling a PyTorch or ONNX vision model from MATLAB | matlab-integrate-pytorch-vision |
| OCR, text recognition, reading text out of an image | matlab-recognize-text (requires Computer Vision Toolbox) |
| Factory-floor visual inspection: anomaly detection, defect/scratch/dent detection, gauging, pass/fail counting | matlab-use-visual-inspection (requires Visual Inspection Toolbox, R2026b+) |
| Barcode or QR reading, camera calibration | No skill covers this — Computer Vision Toolbox (readBarcode, estimateCameraParameters); look up the doc page |
| Optical system design: lens import, prescription-side MTF, spot diagrams, ray tracing, tolerance analysis | matlab-model-optics |
In scope: pick the reference
| If the task or the user's words involve... | Illustrative functions | Load |
|---|---|---|
| inspect, understand, characterize, explore data, look at my images, what kind of image, what am I working with, what's in this folder, describe the data, summarize the dataset, what format, bit depth, class and range, indexed image, alpha channel, metadata, file info, dataset characterization, before I start processing | imfinfo, imhist, countEachLabel | references/image-understanding.md |
| read, write, load, save, import, export, open an image, convert format, batch, folder of images, TIFF, PNG, JPEG, RAW, HDR, test pattern | imread, imwrite, imageDatastore, hdrread | references/image-io.md |
| video, footage, frames, read video, write video, VideoReader, VideoWriter, frame rate, video codec, MP4, AVI | VideoReader, VideoWriter, readFrame, hasFrame, writeVideo | references/video-io.md |
| smooth, blur an image, sharpen, denoise, noise, grainy, filter, kernel, padding, edge-preserving, bilateral, non-local means, frequency filter | imgaussfilt, medfilt2, imsharpen, imbilatfilt, imnlmfilt, imdiffusefilt, imfilter, ordfilt2, locallapfilt, wiener2 | references/filtering.md |
| contrast, brighten, darken, too dark, washed out, enhance, improve, histogram, equalize, CLAHE, normalize, rescale, blend, arithmetic, type convert, flat-field, illumination correction, vignetting, uneven lighting | imadjust, stretchlim, adapthisteq, histeq, rescale, mat2gray, im2single, imadd, imlincomb, imblend, imflatfield | references/preprocessing.md |
| erode, dilate, open, close, structuring element, 2-D morphology, fill holes, remove small objects, skeletonize, thin, tidy up a binary result | strel, imerode, imdilate, imopen, imclose, imfill, bwareaopen, bwskel, imtophat, imreconstruct | references/morphological-ops.md |
| deblur, blurry, unfocused, out of focus, motion blur, camera shake, restore a blurred image, deconvolution, PSF estimation | deconvblind, deconvlucy, deconvwnr, deconvreg | references/deblurring.md |
| resize, rotate, crop, warp, scale, flip, straighten, affine, projective, perspective, undistort, lens distortion, spatial referencing, world coordinates, voxel coordinates, pixel coordinates, coordinate conversion | imresize, imrotate, imcrop, imwarp, imtranslate, affinetform2d, imref2d | references/geometric-transforms.md |
| align two images, stitch, mosaic, panorama, overlapping shots, seam, motion correction, multi-modal alignment, control points, non-rigid, deformable, homography (feature-based requires Computer Vision Toolbox) | imregister, imregtform, imregdemons, imregcorr, detectSIFTFeatures, matchFeatures, estgeotform2d (last three: Computer Vision Toolbox), imregdeform (Medical Imaging Toolbox) | references/registration.md |
segment or threshold with no user input: binarize, Otsu, watershed, k-means or colour clustering, imsegsam automatic mode, deep-learning segmentation | imbinarize, graythresh, multithresh, adaptthresh, watershed, imsegkmeans, imsegkmeans3, imsegisodata, imsegsam | references/segmentation-automated.md |
| segment with user input: clicks, points, boxes, scribbles, seeds, prompted SAM, grabcut, active contours, region growing, lazy snapping, superpixels, App Designer segmentation UIs | segmentAnythingModel, grabcut, activecontour, lazysnapping, imsegfmm, grayconnected, superpixels | references/segmentation-interactive.md |
| measure, count objects, how many, area, perimeter, centroid, size, shape, width, thickness, gap, diameter, Feret, orientation, detect edges, circles, lines, boundary tracing, ROI, world units, metrology, region, region properties, analysis | regionprops, bwconncomp, bwareafilt, bwpropfilt, bwlabel, edge, imfindcircles, hough, bwboundaries | references/analysis-measurement.md |
| quality, PSNR, SSIM, sharpness, noise level, is it blurry, compare two images, difference, before/after, MTF (camera/chart SFR), test chart, artifact (register first — full-reference metrics need pixel correspondence) | psnr, ssim, multissim, immse, brisque, niqe, piqe, measureSharpness, esfrChart | references/quality-assessment.md |
| texture, repetitive pattern, frequency domain, Fourier, FFT, DCT, Radon, Gabor, co-occurrence, GLCM | graycomatrix, graycoprops, entropyfilt, fft2, dct2, radon, gabor | references/texture-transforms.md |
| color space, LAB, HSV, hue, saturation, white balance, color cast, color correction, color balance, ICC profile, grayscale conversion | rgb2lab, lab2rgb, rgb2hsv, chromadapt, illumwhite, makecform, deltaE, decorrstretch | references/color-processing.md |
| 3-D array, volume, volumetric, voxel, slice stack, z-stack — filtering, 3-D morphology, 3-D transforms, or measurement applied to a volume | imgaussfilt3, medfilt3, imresize3, regionprops3, bwvolumefilt (R2026b), bwpropfilt3 (R2026b) | references/3d-volume-processing.md |
| deep learning, neural network, train, U-Net, semantic segmentation networks, classify images, pretrained model, transfer learning, fine-tune, inference, augment, training dataset (requires DLT, often CVT) | unet, deeplabv3plus, trainnet, semanticseg, imageDataAugmenter, augmentedImageDatastore | references/deep-learning-imaging.md |
| build your own image app or GUI: sliders, live preview, parameter tuning, ROI widgets | uifigure, uigridlayout, uislider, drawrectangle | references/app-building.md |
DICOM, NIfTI, NRRD, DICOM-RT, patient metadata, anonymizing, medicalVolume, medicalImage, reading/writing medical formats, extractSlice, volume orientation, medical spatial referencing | medicalVolume, medicalImage, dicomreadVolume, dicomCollection, extractSlice, dicomContours, dicomanon | references/medical-data-io.md |
gigapixel, whole-slide, out-of-memory images, blockedImage, tiled workflows, image pyramids, .svs, .ndpi, .czi, TIFF > 10,000 px, parallel image processing, custom adapters | blockedImage, apply, getBlock, getRegion, selectBlockLocations, blockedImageDatastore | references/large-image-processing.md |
Toolbox Detection
When unsure what's available, check installed toolboxes:
hasIPT = ~isempty(ver('images'));
hasCVT = ~isempty(ver('vision'));
hasDLT = ~isempty(ver('nnet'));
hasMIT = ~isempty(ver('medical'));
hasWavelet = ~isempty(ver('wavelet'));Most functions in this skill require Image Processing Toolbox. The deep-learning-imaging reference also requires Deep Learning Toolbox. Feature-based registration (detectSIFTFeatures, matchFeatures, estgeotform2d) requires Computer Vision Toolbox.
Release-Gated Features
This skill targets R2024b+ but some features require newer releases. References annotate these with the required release. Key ones:
R2024b:
imageshow,imblend,viewer2dR2026a:
uidraw,uipaint,linkviewersR2026b:
bwvolumefilt,bwpropfilt3,uiannotate
If targeting an older release, check with isMATLABReleaseOlderThan("R2026a") and use the fallback patterns documented in the references. To check release notes for a specific function, load matlab-read-documentation.
Copyright 2026 The MathWorks, Inc.
Files
- ATTRIBUTION.mdAdded by AutoGineer1.1 KiB
- LICENSELicence file added by AutoGineer1.4 KiB
- SKILL.md15.8 KiB
- manifest.yaml811 B
- references/3d-volume-processing.md20.2 KiB
- references/analysis-measurement.md18.5 KiB
- references/app-building.md16.9 KiB
- references/blocked-3d-volumes.md9.9 KiB
- references/blocked-data-discovery.md22.1 KiB
- references/blocked-geospatial.md3.2 KiB
- references/blocked-specialized-workflows.md4.3 KiB
- references/blocked-training-and-labeling.md7.0 KiB
- references/blocked-visualization.md5.1 KiB
- references/blocked-whole-slide-images.md3.1 KiB
- references/color-processing.md9.0 KiB
- references/deblurring.md8.1 KiB
- references/deep-learning-imaging.md14.8 KiB
- references/dicom-anonymization.md3.3 KiB
- references/dicom-rt-workflows.md7.4 KiB
- references/filtering.md21.8 KiB
- references/geometric-transforms.md14.3 KiB
- references/image-io.md15.5 KiB
- references/image-understanding.md20.0 KiB
- references/ipt-reading-writing.md4.8 KiB
- references/large-image-processing.md12.3 KiB
- references/medical-data-io.md8.3 KiB
- references/mit-medical-volume.md8.2 KiB
- references/mit-spatial-referencing.md5.9 KiB
- references/morphological-ops.md18.0 KiB
- references/preprocessing.md14.4 KiB
- references/quality-assessment.md13.5 KiB
- references/registration.md24.7 KiB
- references/segmentation-automated.md9.7 KiB
- references/segmentation-interactive.md15.3 KiB
- references/texture-transforms.md10.4 KiB
- references/video-io.md3.4 KiB
references/blocked-3d-volumes.md
3D Blocked Images
If the
matlab-display-imageskill is available, load it for full volshow/viewer3d guidance. This file covers blockedImage-specific 3D patterns only.
blockedImage supports 3D volumes (e.g., MRI, CT, microscopy stacks). Load a volume directly:
bim = blockedImage(vol); % vol is a 3D or 4D numeric arrayFor single-file multi-page TIFF stacks, use a custom adapter with tiffreadVolume and PixelRegion for efficient sub-volume random access without loading the entire file. See the "Reference adapter: single multi-page TIFF volume via tiffreadVolume" section in blocked-data-discovery.md.
3D Visualization with volshow
volshow natively accepts blockedImage objects for efficient out-of-core 3D rendering. Parent it to a viewer3d for full camera and lighting control.
Intensity normalization — CRITICAL for non-uint8 data
volshow maps data to colormap/alphamap assuming the range [0, 1] for floating-point or [0, 255] for uint8. If data has a different range (common with single/double scientific data), the display will appear as a solid white or black cube. Always normalize before displaying:
vol = gather(bim);
lo = 0;
hi = double(prctile(vol(:), 99.5)); % clip outliers
volNorm = max(0, min(1, (double(vol) - lo) / (hi - lo)));
viewer = viewer3d(BackgroundColor="black", BackgroundGradient="off");
volshow(volNorm, Parent=viewer);For uint8 data, no normalization is needed.
Rendering styles
Choose a rendering style based on what the user wants to see:
"VolumeRendering"(default) — semi-transparent density view, good for overall structure. Requires a well-tunedAlphamap(see below)."MaximumIntensityProjection"— projects brightest voxel along each ray. No opacity issues. Best first choice for quick visualization."MinimumIntensityProjection"— highlights dark features (e.g., air cavities)"Isosurface"— surface at a threshold, good for segmentation boundaries"SlicePlanes"— interactive orthogonal slice planes for browsing inside the volume. Always works regardless of opacity settings."CinematicRendering"— photorealistic rendering with global illumination"GradientOpacity"— emphasizes edges/boundaries"LightScattering"— simulates subsurface scattering
Recommended starting point: Use "MaximumIntensityProjection" first (no alphamap tuning needed), then switch to "VolumeRendering" with a custom alphamap once you understand the intensity distribution. "SlicePlanes" is always reliable for browsing.
volshow(volNorm, RenderingStyle="MaximumIntensityProjection", Colormap=hot(256));Adjusting opacity and colormap
The default Alphamap ("cubic") often makes dense data (e.g., tissue volumes where most voxels are nonzero) appear as a solid opaque block. Build a custom alphamap that makes low/mid intensities transparent:
alpha = zeros(256, 1);
alpha(77:end) = linspace(0, 0.6, 180)'; % bottom 30% fully transparent, ramp to 60%
viewer = viewer3d(BackgroundColor="black", BackgroundGradient="off");
volshow(volNorm, Parent=viewer, ...
RenderingStyle="VolumeRendering", ...
Alphamap=alpha, ...
Colormap=hot(256));Tune the ramp start (here 77/256 ≈ 30%) and max opacity (here 0.6) based on the data:
Dense tissue (most voxels nonzero): start ramp at 30-50%, max opacity 0.4-0.6
Sparse features (mostly background): start ramp at 5-10%, max opacity 0.8-1.0
Alphamap presets: "cubic" (default), "linear", "quadratic". Pass a custom 256-element column vector for fine control.
SlicePlanes for interactive browsing
"SlicePlanes" shows three orthogonal cutting planes (axial, coronal, sagittal) that the user can drag interactively. Always works regardless of opacity/intensity issues — use as a fallback when volume rendering looks wrong:
viewer = viewer3d;
volshow(volNorm, Parent=viewer, RenderingStyle="SlicePlanes", Colormap=gray(256));Side-by-side 3D comparison
Use a grid layout with multiple viewer3d panels. Use linkviewers to synchronize camera rotation/zoom. Normalize all volumes with the same intensity window so they are visually comparable:
% Compute shared normalization window across all volumes
vol1 = gather(bim1); vol2 = gather(bim2);
hi = max(prctile(vol1(:), 99.5), prctile(vol2(:), 99.5));
norm = @(v) max(0, min(1, double(v) / hi));
fig = uifigure('Position', [100 100 1200 600]);
g = uigridlayout(fig, [1 2]);
v1 = viewer3d(g, BackgroundColor="black", BackgroundGradient="off");
v1.Layout.Column = 1;
v2 = viewer3d(g, BackgroundColor="black", BackgroundGradient="off");
v2.Layout.Column = 2;
volshow(norm(vol1), Parent=v1, RenderingStyle="MaximumIntensityProjection", Colormap=hot(256));
volshow(norm(vol2), Parent=v2, RenderingStyle="MaximumIntensityProjection", Colormap=hot(256));
linkviewers([v1, v2]);Overlay segmentation on volume
volshow(bim, OverlayData=bSegmentation, OverlayRenderingStyle="LabelOverlay");OverlayRenderingStyle options: "LabelOverlay" (categorical labels), "VolumeOverlay" (continuous data), "GradientOverlay" (edge-weighted overlay).
Overview + detail for large 3D volumes
For very large volumes, rendering the full dataset at full resolution is slow or impossible. Use a two-panel layout: a coarse overview for orientation, and a cropped full-resolution detail view for inspection. Link the cameras so rotating one rotates both.
% Build a multi-level pyramid (overview generation is one-time cost)
bim.BlockSize = [4096 4096 20];
bimOverview = makeMultiLevel3D(bim, Scales=0.2, OutputLocation=tempname);
bPyramid = concatenateLevels(bim, bimOverview);
% Extract a detail sub-volume (virtual crop, no data copy)
bDetail = crop(bim, [r1 c1 z1], [r2 c2 z2]);
fig = uifigure(Position=[100 100 1400 700]);
g = uigridlayout(fig, [1 2]);
vOv = viewer3d(g, BackgroundColor="black", BackgroundGradient="off");
vOv.Layout.Column = 1;
vDt = viewer3d(g, BackgroundColor="black", BackgroundGradient="off");
vDt.Layout.Column = 2;
% Overview at coarse level, detail at full resolution
volshow(bPyramid, Parent=vOv, RenderingStyle="MaximumIntensityProjection");
volshow(bDetail, Parent=vDt, RenderingStyle="MaximumIntensityProjection");
linkviewers([vOv, vDt]);Key points:
The overview uses
makeMultiLevel3D+concatenateLevelssovolshowcan pick the coarse level automatically.The detail view uses
crop(virtual, instant) to restrict to a region of interest at full resolution.linkviewerssynchronizes camera rotation and zoom between the two panels.For multi-channel data, add
OverlayDataon both panels to keep the overlay consistent.
Choosing between sliceViewer and volshow
Use
sliceViewerwhen: browsing individual 2D slices, measuring pixel values, the volume fits in memory, 2D inspection is the goal.Use
volshowwhen: 3D spatial context matters (shape, connectivity, depth), comparing structures across volumes, the data is a blockedImage (supports out-of-core), cinematic or publication-quality rendering is needed.Both can be used together:
sliceViewerfor detailed 2D inspection,volshowfor 3D overview.
Creating 3D overviews and pyramids
For large 3D volumes that don't fit in memory (or are slow to render at full resolution), create a downsampled overview with makeMultiLevel3D. Then combine it with the full-resolution data to create a multi-level pyramid for efficient visualization and processing.
Generate a downsampled overview
% Set BlockSize large for fewer, bigger reads during downsampling
bim.BlockSize = [4096 4096 20];
overviewDir = tempname; % or a folder on a fast SSD
bimOverview = makeMultiLevel3D(bim, Scales=0.2, ...
OutputLocation=overviewDir, UseParallel=false);Choose
Scalesso the overview fits in RAM. For an 8k x 6k x 4k volume, scale 0.1 yields ~800 x 600 x 400 (~400 MB for uint16).Set
UseParallel=truewith Parallel Computing Toolbox for faster generation.Use a large BlockSize for overview generation to minimize per-block overhead.
Load and inspect the overview
vOverview = gather(bimOverview);
imageshow(imadjust(vOverview(:,:,1)), Interpolation="bilinear");Build a multi-level pyramid
Combine the full-resolution blockedImage with the overview into a single multi-level representation:
bPyramid = concatenateLevels(bim, bimOverview);
bPyramid.Size % shows sizes at each levelDownstream tools (volshow, imageshow, apply) can then select the appropriate resolution level automatically.
Write a compressed copy for archival or faster I/O
Convert uncompressed folder-of-TIFFs to a compressed chunked format:
h5adapter = images.blocked.H5Blocks;
h5adapter.GZIPLevel = 2;
write(bim, "compressed_output", BlockSize=[1024 1024 1024], Adapter=h5adapter);Labeling with volumeSegmenter
The volumeSegmenter app can work directly with blockedImage, but each loaded block must fit in GPU/display memory. Set BlockSize so each block is at most 2048^3 voxels:
bim.BlockSize = [2048 2048 N]; % N = number of Z slices, capped so total <= 2048^3
volumeSegmenter(bim);3D inference with apply()
bSeg = apply(bim, @(bs) semanticseg(bs.Data, net, Classes=classNames), ...
BlockSize=[132 132 132 4], ...
BorderSize=[44 44 44 0], ... % (netInputSize - blockSize)/2
PadPartialBlocks=true, PadMethod="replicate", ...
BatchSize=1);BorderSize=(networkInputSize - networkOutputSize) / 2— ensures each output pixel has full receptive field context.BatchSize=1is typical for 3D to avoid GPU OOM; increase cautiously.Use
makeMultiLevel3Dto create 3D resolution pyramids.
3D training data
Use randomPatchExtractionDatastore to extract random 3D patches during training:
patchDS = randomPatchExtractionDatastore(bimDS, blabelDS, [132 132 132], PatchesPerImage=16);Copyright 2026 The MathWorks, Inc.
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Security
No issues found by our static scan· No issues found
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Attribution and licence
- Created by
- MathWorks
- Publisher
- MathWorks (Official)
- Source
- matlab/matlab-agentic-toolkit · skills-catalog/image-processing-and-computer-vision/matlab-process-images · commit 2a7f215
- Licence
- BSD 2-Clause License (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE.md)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download matlab-process-images.zip