Matlab Process Images
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- 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://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/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 type | Task | Constraints | Algorithm |
|---|---|---|---|
| 2-D gray | Binary, high contrast | Automatic | imbinarize (global) |
| 2-D gray | Binary, uneven lighting | Automatic | imbinarize("adaptive") |
| 2-D gray/color | Multi-level threshold | Automatic | multithresh + imquantize |
| 2-D gray/color | Multi-region, known K | Automatic | imsegkmeans |
| 2-D gray/color | Multi-region, unknown K | Automatic | imsegisodata (R2024b+) |
| 2-D/3-D | Splitting touching convex objects (cells, particles) | Have binary mask | Marker-controlled watershed |
| 2-D | Complex scene, zero-shot | No training data | imsegsam (CVT+DLT) (R2024b+) |
| 2-D/3-D | Pixel-level classes | Have labeled data | semanticseg pipeline (CVT+DLT) |
| 2-D | Instance (per-object masks) | Have labeled data | maskrcnn or solov2 (CVT+DLT) |
| 3-D volume | Multi-region | Automatic | imsegkmeans3 |
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 thresholdAdaptive (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 labelTuning 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 1Clustering — 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
endDo not use
whichalone — 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 mapAlternatives 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 mapInstance 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
| Problem | Fix | Function |
|---|---|---|
| Noise | Smooth first | imgaussfilt, medfilt2 |
| Low contrast | Enhance | imadjust, adapthisteq |
| Uneven illumination | Flatten background | imtophat or imflatfield |
| Color → cluster | Convert 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— neverim2bw(not recommended) or manualgraythresh+ thresholdFor watershed, always suppress or constrain minima first — never
watershed(imgradient(I))on an unmodified gradientFor color segmentation, convert to L*a*b* before clustering
Prefer
imsegisodataover manual ISODATA when cluster count is unknownUse
bwareafiltorimfill(BW, "holes")to clean up after thresholding (seemorphological-ops.md) —imfill(BW)without"holes"is interactive (opens a figure for seed point selection), not automatic
Common Mistakes
| Mistake | Why it's wrong | Correct approach |
|---|---|---|
watershed(imgradient(I)) | Severe over-segmentation (thousands of regions) | Marker-controlled: imhmin or imimposemin first |
im2bw(I, level) | Not recommended since R2016a | imbinarize(I) or imbinarize(I, level) |
| Clustering in RGB space | RGB not perceptually uniform | Convert to L*a*b*: imsegkmeans(single(rgb2lab(I)), K) |
segnetLayers or fcnLayers | Legacy / not recommended | Use deeplabv3plus, unet, or solov2 (see deep-learning-imaging.md) |
imshow(I, OverlayData=L) | imshow does not support OverlayData | imageshow(I, OverlayData=L) (R2024b+) |
Copyright 2026 The MathWorks, Inc.
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- MathWorks (Official)
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