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Matlab Process Images

MathWorksOfficialCreated by MathWorks

  • Skill
  • Tool integration
  • Software development
  • BSD-2-Clause
  • No issues found

From the author

Load this first for any MATLAB task involving images, photos, scans, frames, volumes, or visual data — reading, writing, filtering, enhancing, denoising, sharpening, deblurring, thresholding, segmenting, masking, detecting, counting objects, removing backgrounds, measuring, registering, stitching, aligning, cropping, resizing, or transforming. Covers image I/O (TIFF, PNG, JPEG, RAW, HDR), histograms, edge detection, color, morphology, deep learning for images, quality assessment, and 3-D volumes. Also for image problems (dark, noisy, blurry, low contrast, uneven lighting, hazy, color cast) or data exploration (folder of images). Includes medical imaging I/O (DICOM, NIfTI, NRRD, medicalVolume) and large/blocked images (blockedImage, whole-slide). Skip if the user just wants you to look at an image, with no MATLAB work. Not for Simulink video-block models, C/C++/CUDA code generation or GPU deployment of a network, or point clouds. Hyperspectral and visual inspection have sibling skills.
— the description by MathWorks

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

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/matlab/matlab-process-images/download
  2. Unzip 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
TypeRangeWhen to use
uint80–255Display, file I/O, memory-efficient storage
uint160–65535High dynamic range sensors, medical. Camera RAW is n-bit in a uint16 container — rescale with rawinfo BlackLevel/WhiteLevel, not 65535
single / double0.0–1.0Arithmetic, filtering, computation
logical0 or 1Binary 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, regionprops centroids)

  • 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

  • imref2d and imref3d can 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×N or M×N×1

  • RGB: M×N×3

  • With 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.md first — 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, load matlab-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; radiomicsNo 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 exportmatlab-display-image
Displaying a 3-D volume: volshow, viewer3d, isosurface rendering, slice viewer, orthogonal viewsmatlab-display-volume
Hyperspectral or multispectral cubes, spectral indices, unmixing, ENVI, band selectionmatlab-analyze-spectral-images
Calling a PyTorch or ONNX vision model from MATLABmatlab-integrate-pytorch-vision
OCR, text recognition, reading text out of an imagematlab-recognize-text (requires Computer Vision Toolbox)
Factory-floor visual inspection: anomaly detection, defect/scratch/dent detection, gauging, pass/fail countingmatlab-use-visual-inspection (requires Visual Inspection Toolbox, R2026b+)
Barcode or QR reading, camera calibrationNo 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 analysismatlab-model-optics
In scope: pick the reference
If the task or the user's words involve...Illustrative functionsLoad
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 processingimfinfo, imhist, countEachLabelreferences/image-understanding.md
read, write, load, save, import, export, open an image, convert format, batch, folder of images, TIFF, PNG, JPEG, RAW, HDR, test patternimread, imwrite, imageDatastore, hdrreadreferences/image-io.md
video, footage, frames, read video, write video, VideoReader, VideoWriter, frame rate, video codec, MP4, AVIVideoReader, VideoWriter, readFrame, hasFrame, writeVideoreferences/video-io.md
smooth, blur an image, sharpen, denoise, noise, grainy, filter, kernel, padding, edge-preserving, bilateral, non-local means, frequency filterimgaussfilt, medfilt2, imsharpen, imbilatfilt, imnlmfilt, imdiffusefilt, imfilter, ordfilt2, locallapfilt, wiener2references/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 lightingimadjust, stretchlim, adapthisteq, histeq, rescale, mat2gray, im2single, imadd, imlincomb, imblend, imflatfieldreferences/preprocessing.md
erode, dilate, open, close, structuring element, 2-D morphology, fill holes, remove small objects, skeletonize, thin, tidy up a binary resultstrel, imerode, imdilate, imopen, imclose, imfill, bwareaopen, bwskel, imtophat, imreconstructreferences/morphological-ops.md
deblur, blurry, unfocused, out of focus, motion blur, camera shake, restore a blurred image, deconvolution, PSF estimationdeconvblind, deconvlucy, deconvwnr, deconvregreferences/deblurring.md
resize, rotate, crop, warp, scale, flip, straighten, affine, projective, perspective, undistort, lens distortion, spatial referencing, world coordinates, voxel coordinates, pixel coordinates, coordinate conversionimresize, imrotate, imcrop, imwarp, imtranslate, affinetform2d, imref2dreferences/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 segmentationimbinarize, graythresh, multithresh, adaptthresh, watershed, imsegkmeans, imsegkmeans3, imsegisodata, imsegsamreferences/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 UIssegmentAnythingModel, grabcut, activecontour, lazysnapping, imsegfmm, grayconnected, superpixelsreferences/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, analysisregionprops, bwconncomp, bwareafilt, bwpropfilt, bwlabel, edge, imfindcircles, hough, bwboundariesreferences/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, esfrChartreferences/quality-assessment.md
texture, repetitive pattern, frequency domain, Fourier, FFT, DCT, Radon, Gabor, co-occurrence, GLCMgraycomatrix, graycoprops, entropyfilt, fft2, dct2, radon, gaborreferences/texture-transforms.md
color space, LAB, HSV, hue, saturation, white balance, color cast, color correction, color balance, ICC profile, grayscale conversionrgb2lab, lab2rgb, rgb2hsv, chromadapt, illumwhite, makecform, deltaE, decorrstretchreferences/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 volumeimgaussfilt3, 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, augmentedImageDatastorereferences/deep-learning-imaging.md
build your own image app or GUI: sliders, live preview, parameter tuning, ROI widgetsuifigure, uigridlayout, uislider, drawrectanglereferences/app-building.md
DICOM, NIfTI, NRRD, DICOM-RT, patient metadata, anonymizing, medicalVolume, medicalImage, reading/writing medical formats, extractSlice, volume orientation, medical spatial referencingmedicalVolume, medicalImage, dicomreadVolume, dicomCollection, extractSlice, dicomContours, dicomanonreferences/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 adaptersblockedImage, apply, getBlock, getRegion, selectBlockLocations, blockedImageDatastorereferences/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, viewer2d

  • R2026a: uidraw, uipaint, linkviewers

  • R2026b: 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

references/segmentation-automated.md

Automated Image Segmentation

Decision guidance for segmentation methods that require no user interaction — thresholding, clustering, watershed, automatic SAM, and deep learning pipelines.

Often loaded with: morphological-ops.md (cleaning up masks), analysis-measurement.md (measuring segmented regions), segmentation-interactive.md (when automatic methods fail and user input is available).

Method Selection

Image typeTaskConstraintsAlgorithm
2-D grayBinary, high contrastAutomaticimbinarize (global)
2-D grayBinary, uneven lightingAutomaticimbinarize("adaptive")
2-D gray/colorMulti-level thresholdAutomaticmultithresh + imquantize
2-D gray/colorMulti-region, known KAutomaticimsegkmeans
2-D gray/colorMulti-region, unknown KAutomaticimsegisodata (R2024b+)
2-D/3-DSplitting touching convex objects (cells, particles)Have binary maskMarker-controlled watershed
2-DComplex scene, zero-shotNo training dataimsegsam (CVT+DLT) (R2024b+)
2-D/3-DPixel-level classesHave labeled datasemanticseg pipeline (CVT+DLT)
2-DInstance (per-object masks)Have labeled datamaskrcnn or solov2 (CVT+DLT)
3-D volumeMulti-regionAutomaticimsegkmeans3

When uncertain: prefer simpler method first; escalate if it fails.

Thresholding

Global
BW = imbinarize(I);           % Otsu's method (default)
BW = imbinarize(I, 0.45);    % Manual threshold
Adaptive (Uneven Illumination)
BW = imbinarize(I, "adaptive", Sensitivity=0.4);
Multi-Level
thresh = multithresh(I, 3);       % 3 thresholds → 4 regions
L = imquantize(I, thresh);

Marker-Controlled Watershed — Critical Pattern

When to use: Use watershed segmentation to separate touching objects or to segment regions from an image gradient. To reduce oversegmentation, modify the distance or gradient image using suitable foreground and background markers before applying watershed.

When NOT to use: For general scene segmentation, use SAM (imsegsam) or clustering. Superpixels over-segment uniformly (don't solve the "split touching blobs" problem).

Applying watershed directly to an unmodified gradient image can cause severe oversegmentation. For gradient-based watershed segmentation, first compute suitable foreground and background markers, then use imimposemin to constrain the minima before applying watershed (see gradient-based example below). For distance-based watershed, use imhmin to suppress shallow minima:

% 1. Create binary mask
BW = imbinarize(I);
BW = imfill(BW, "holes");

% 2. Distance transform (peaks at object centers)
D = -bwdist(~BW);

% 3. Suppress shallow minima (prevents over-segmentation)
D = imhmin(D, 2);

% 4. Watershed
L = watershed(D);
L(~BW) = 0;  % Remove background label

Tuning h in imhmin(D, h): Choose h based on the scale of D and the depths of the minima that represent unwanted regions. Increasing h generally reduces oversegmentation, but a value that is too large can merge distinct objects. Tune using representative images and verify the resulting object boundaries.

Alternative: Gradient-Based with imimposemin

When you don't have a clean binary mask:

gmag = imgradient(im2double(I));
markers = imextendedmin(imcomplement(im2double(I)), 0.1);
gmag2 = imimposemin(gmag, markers);
L = watershed(gmag2);
L(L == 0) = 1;  % Assign watershed ridge pixels to region 1

Clustering — imsegkmeans vs imsegisodata

% Known number of regions (grayscale uint8 input works directly)
L = imsegkmeans(I, 4);

% Multichannel input — double must be converted to single; uint8/uint16/int16 work directly
L = imsegkmeans(single(I), 4);  % Use when I is double or single

% Unknown number of regions (auto-determines K)
[L, centers] = imsegisodata(I);  % R2024b+

Color images: For color-based segmentation, convert to L*a*b* before clustering — do not cluster in RGB:

lab = rgb2lab(I);
L = imsegkmeans(single(lab), 4);

With texture features (Gabor filter bank):

gBank = gabor(2.^(2:5), 0:45:135);
features = cat(3, single(I), single(imgaborfilt(im2gray(I), gBank)));
L = imsegkmeans(features, 5);

SAM 2: Automatic Zero-Shot Segmentation (R2024b+)

Use imsegsam to automatically segment an entire image or objects within a specified ROI using SAM 2. By default, the function uses SAM 2; you can also specify a base SAM model.

Requires IPT Model for Segment Anything Model support package. Check availability:

try
    sam = segmentAnythingModel;  % Verifies support package and required add-on are available
    hasSAM = true;
catch
    hasSAM = false;  % Help the user install the support package
end

Do not use which alone — it can return true even when required add-ons are missing. Attempt construction to confirm usability.

[cc, scores] = imsegsam(I, MinObjectArea=500, ScoreThreshold=0.7);
% cc is a connected component struct (like bwconncomp output)
% cc.NumObjects, cc.PixelIdxList — use labelmatrix(cc) for a label map

Alternatives when SAM is unavailable: If the image contains regions that can be separated by intensity, color, or selected image features, consider imsegisodata when the number of clusters is unknown or imsegkmeans when you can specify the number of clusters. These methods are not direct substitutes for object-aware segmentation.

imsegsam is automatic only (no prompts). For point/box-prompted segmentation, see segmentAnythingModel in segmentation-interactive.md.

Deep Learning Segmentation

For pixel-level classification with labeled training data, see deep-learning-imaging.md for the full pipeline. Quick inference pattern:

result = semanticseg(testImage, net);  % Returns categorical label map
Instance Segmentation

For per-object masks (each object gets a unique ID), use Mask R-CNN or SOLOv2 (CVT+DLT). SOLOv2 is more lightweight and often preferable when per-instance bounding boxes are not required:

% SOLOv2 — lightweight instance segmentation
net = solov2("resnet50-coco");
[masks, labels, scores] = segmentObjects(net, testImage);

% Mask R-CNN — instance segmentation with bounding boxes
net = maskrcnn("resnet50-coco");
[boxes, scores, labels, masks] = detectObjects(net, testImage);

To convert polygon-labeled ground truth into training data for Mask R-CNN or SOLOv2, use instanceSegmentationTrainingData (R2026b+).

Preprocessing for Segmentation

ProblemFixFunction
NoiseSmooth firstimgaussfilt, medfilt2
Low contrastEnhanceimadjust, adapthisteq
Uneven illuminationFlatten backgroundimtophat or imflatfield
Color → clusterConvert to L*a*b*rgb2lab then imsegkmeans on Lab channels
Adaptive Thresholding with Masked Regions

imbinarize(I, "adaptive") calls adaptthresh internally. When you need to modify the threshold map before binarizing, compute it explicitly with adaptthresh + imbinarize(A, T) instead.

When parts of the image are zeroed out (e.g., background outside a brain, removed ROI), adaptthresh uses those zeros in local neighborhood calculations, corrupting thresholds near the mask boundary. Setting masked pixels to NaN also fails (produces black blocks).

Fix: post-process the threshold map — let adaptthresh run, then interpolate over the affected region:

T = adaptthresh(A, "NeighborhoodSize", M);
B2 = imdilate(mask, ones(M, M));  % Expand mask by neighborhood size
T2 = regionfill(T, B2);           % Interpolate threshold values from surroundings
BW = imbinarize(A, T2);

This works because regionfill smoothly fills in threshold values from the unaffected boundary — the actual image zeros never influence the final threshold in the masked region.

Displaying Segmentation Results

% Direct overlay display (preferred — one step, interactive)
imageshow(gray, OverlayData=L);

% For generating an RGB array (e.g., saving to file or further processing)
rgb = labeloverlay(gray, L, Transparency=0.5);

Use imageshow with OverlayData to interactively inspect segmentation results. Use labeloverlay when you need an RGB overlay array for saving, compositing, or further processing.

Conventions

  • Always use imbinarize — never im2bw (not recommended) or manual graythresh + threshold

  • For watershed, always suppress or constrain minima first — never watershed(imgradient(I)) on an unmodified gradient

  • For color segmentation, convert to L*a*b* before clustering

  • Prefer imsegisodata over manual ISODATA when cluster count is unknown

  • Use bwareafilt or imfill(BW, "holes") to clean up after thresholding (see morphological-ops.md) — imfill(BW) without "holes" is interactive (opens a figure for seed point selection), not automatic

Common Mistakes

MistakeWhy it's wrongCorrect approach
watershed(imgradient(I))Severe over-segmentation (thousands of regions)Marker-controlled: imhmin or imimposemin first
im2bw(I, level)Not recommended since R2016aimbinarize(I) or imbinarize(I, level)
Clustering in RGB spaceRGB not perceptually uniformConvert to L*a*b*: imsegkmeans(single(rgb2lab(I)), K)
segnetLayers or fcnLayersLegacy / not recommendedUse deeplabv3plus, unet, or solov2 (see deep-learning-imaging.md)
imshow(I, OverlayData=L)imshow does not support OverlayDataimageshow(I, OverlayData=L) (R2024b+)

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

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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)
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