Matlab Process Images

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

Core Conventions

Image Data Types

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

Always use imageshow instead of imshow (R2024b+). imageshow handles sub-bit-depth containers (e.g., 12-bit in uint16) via DisplayRangeMode, supports OverlayData for label/mask overlays, and works with viewer2d. Do not fall back to imshow — it lacks these capabilities.

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?

In scope: pick the reference

Toolbox Detection

When unsure what's available, check installed toolboxes:

matlabCopy code
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.

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