MATLAB Image Processing Toolbox

Expert skill for medical image analysis using MATLAB's Image Processing Toolbox (IPT R2025a+).

When to Use This Skill

•Image filtering, denoising, or enhancement in MATLAB
•Image segmentation (thresholding, watershed, active contours, deep learning)
•Morphological operations (erosion, dilation, opening, closing, reconstruction)
•Fourier/frequency domain filtering and transforms
•Image registration and geometric transformations
•Feature extraction (edges, regions, texture)
•Medical imaging: MRI, CT/X-ray, microscopy/histology
•Deep learning segmentation (semanticseg, U-Net, DeepLabv3+)
•Large image processing (blockproc) and GPU acceleration
•Data type handling and conversions

Read Before Coding

Task	Knowledge Card	Key Functions
Reduce noise	`filtering-denoising.md`	`imgaussfilt`, `medfilt2`, `wiener2`
Detect edges	—	`edge`, `imgradient`, `imgradientxy`
Clean binary masks	`morphology-binary.md`	`bwareaopen`, `imfill`, `imopen`, `imclose`
Select threshold	`segmentation-thresholding.md`	`graythresh`, `imbinarize`, `multithresh`
Separate touching objects	—	`watershed`, `bwdist`, `imhmin`
Measure regions	`feature-regions.md`	`regionprops`, `bwconncomp`, `bwlabel`
Register images	—	`imregister`, `imregtform`, `imwarp`
Process large images	—	`blockproc`, `gpuArray`
Segment with DL	`deep-learning-segmentation.md`	`semanticseg`, `unetLayers`
Convert data types	`data-types.md`	`im2double`, `im2uint8`, `mat2gray`
Process MRI	`medical-mri.md`	`dicomread`, `adapthisteq`, bias correction
Process CT/X-ray	—	HU windowing, bone segmentation
Count cells	`medical-microscopy.md`	Cell detection, stain normalization

Note: Rows with "—" indicate functions covered in SKILL.md quick patterns. Future knowledge cards may be added.

Critical Rules

Rule 1: Data Type Handling

The #1 source of errors. Different types have different ranges:

Type	Range	When to Use
`uint8`	[0, 255]	Display, storage, most IPT functions
`uint16`	[0, 65535]	Medical images (DICOM), high dynamic range
`double`	[0, 1] normalized	Arithmetic operations, filtering
`logical`	0 or 1	Binary masks, segmentation results

matlab

% WRONG: Mixed types cause silent truncation
result = uint8_image + double_mask;  % Truncates!

% CORRECT: Explicit conversion
img = im2double(uint8_image);  % [0,255] → [0,1]
result = img + mask;
output = im2uint8(result);     % [0,1] → [0,255]

Rule 2: Filter Normalization

matlab

% LOWPASS: Coefficients must sum to 1 (preserves brightness)
h_avg = fspecial('average', 5);       % sum = 1
h_gauss = fspecial('gaussian', 5, 1); % sum ≈ 1

% HIGHPASS: Coefficients must sum to 0 (extracts changes)
h_laplacian = fspecial('laplacian');  % sum = 0
h_log = fspecial('log', 9, 1.5);      % sum = 0

% Verify before custom filters:
assert(abs(sum(h(:)) - 1) < 1e-6, 'Lowpass filter must sum to 1');

Rule 3: Boundary Handling

matlab

% For medical images: use 'replicate' to avoid edge artifacts
filtered = imfilter(img, h, 'replicate');

% Options: 'replicate' | 'symmetric' | 'circular' | numeric value
% Default is zero-padding which creates dark borders!

Rule 4: Structuring Element Sizing

SE radius ≈ half the feature size you want to affect.

matlab

% Remove noise blobs smaller than ~10 pixels diameter
se = strel('disk', 5);  % radius = 10/2 = 5
cleaned = imopen(bw, se);

% Fill holes up to ~20 pixels diameter
se = strel('disk', 10);
filled = imclose(bw, se);

Rule 5: Threshold Output Range

graythresh returns normalized [0,1] regardless of input type:

matlab

img_uint8 = imread('image.png');
level = graythresh(img_uint8);  % Returns 0.45, NOT 115

% For uint8 threshold value:
threshold_uint8 = level * 255;  % 0.45 * 255 = 115

% Better: use imbinarize (handles automatically)
bw = imbinarize(img_uint8);  % Internally uses graythresh

Rule 6: Memory for Large Images

matlab

% Use blockproc for images that don't fit in memory
% BorderSize MUST match filter radius to avoid tile artifacts

fun = @(block) imgaussfilt(block.data, 2);
result = blockproc(huge_image, [512 512], fun, ...
    'BorderSize', [6 6], ...      % 3*sigma for Gaussian
    'TrimBorder', true, ...
    'UseParallel', true);

Quick Patterns

Standard Preprocessing Pipeline

matlab

function out = preprocess(img)
    img = im2double(img);                           % Normalize
    img = imgaussfilt(img, 1);                      % Denoise
    img = adapthisteq(img, 'ClipLimit', 0.02);      % Enhance contrast
    out = mat2gray(img);                            % Ensure [0,1]
end

Binary Mask Cleanup

matlab

function bw = clean_mask(bw, min_area, se_radius)
    se = strel('disk', se_radius);
    bw = imopen(bw, se);              % Remove small protrusions
    bw = imclose(bw, se);             % Close small gaps
    bw = imfill(bw, 'holes');         % Fill holes
    bw = bwareaopen(bw, min_area);    % Remove small objects
end

Segment and Measure

matlab

function stats = segment_measure(img)
    bw = imbinarize(img, 'adaptive');
    bw = bwareaopen(bw, 50);
    cc = bwconncomp(bw);
    stats = regionprops('table', cc, img, ...
        'Area', 'Centroid', 'MeanIntensity', 'Eccentricity');
end

Function Quick Reference

Filtering

Function	Purpose	Example
`imfilter`	Apply custom filter	`imfilter(I, h, 'replicate')`
`imgaussfilt`	Gaussian smoothing	`imgaussfilt(I, sigma)`
`medfilt2`	Median filter (salt-pepper)	`medfilt2(I, [3 3])`
`wiener2`	Adaptive Wiener	`wiener2(I, [5 5])`
`imsharpen`	Unsharp masking	`imsharpen(I, 'Amount', 1)`

Segmentation

Function	Purpose	Example
`graythresh`	Otsu threshold	`level = graythresh(I)`
`imbinarize`	Binarize image	`bw = imbinarize(I, 'adaptive')`
`multithresh`	Multi-level threshold	`thresh = multithresh(I, 2)`
`watershed`	Watershed segmentation	`L = watershed(D)`
`activecontour`	Active contours	`bw = activecontour(I, mask)`

Morphology

Function	Purpose	Example
`strel`	Create structuring element	`se = strel('disk', 5)`
`imerode` / `imdilate`	Erosion / Dilation	`imerode(bw, se)`
`imopen` / `imclose`	Opening / Closing	`imopen(bw, se)`
`bwareaopen`	Remove small objects	`bwareaopen(bw, 100)`
`imfill`	Fill holes	`imfill(bw, 'holes')`

Features

Function	Purpose	Example
`edge`	Edge detection	`edge(I, 'Canny')`
`regionprops`	Region measurements	`regionprops('table', bw, I, 'Area')`
`bwconncomp`	Connected components	`cc = bwconncomp(bw)`
`graycomatrix`	Texture GLCM	`glcm = graycomatrix(I)`

I/O & Types

Function	Purpose	Example
`imread` / `imwrite`	Read/write images	`I = imread('img.png')`
`dicomread`	Read DICOM	`I = dicomread('scan.dcm')`
`im2double`	To double [0,1]	`I = im2double(I)`
`im2uint8`	To uint8 [0,255]	`I = im2uint8(I)`

Knowledge Cards

Detailed documentation organized by topic:

Core Processing:

•knowledge/cards/filtering-denoising.md - Noise reduction techniques
•knowledge/cards/segmentation-thresholding.md - Otsu, adaptive, multi-level
•knowledge/cards/morphology-binary.md - Binary morphological operations

Feature Extraction:

•knowledge/cards/feature-regions.md - regionprops, connected components

Medical Imaging:

•knowledge/cards/medical-mri.md - MRI preprocessing and segmentation
•knowledge/cards/medical-microscopy.md - Cell counting, histology

Advanced:

•knowledge/cards/deep-learning-segmentation.md - semanticseg, U-Net
•knowledge/cards/data-types.md - Type conversions and pitfalls

Cross-Toolbox Integration

For wavelet-based image processing (multiresolution denoising, fusion), see: matlab-wavelet-toolbox skill.

matlab

% Example: Wavelet + IPT fusion for MRI denoising
denoised = wdenoise2(mri, 'DenoisingMethod', 'Bayes');  % Wavelet
cleaned = imopen(denoised, strel('disk', 2));           % IPT morphology
enhanced = adapthisteq(cleaned);                        % IPT contrast

Source: MathWorks Image Processing Toolbox Documentation (R2025a)