Image Segmentation

Segment images

Image segmentation is the process of partitioning an image into parts or regions. This division into parts is often based on the characteristics of the pixels in the image. For example, one way to find regions in an image is to look for abrupt discontinuities in pixel values, which typically indicate edges. These edges can define regions. Other methods divide the image into regions based on color values or texture.

Apps

Color Thresholder	Threshold color image
Image Segmenter	Segment an image by refining regions
Volume Segmenter	Segment 3-D grayscale or RGB volumetric images (Since R2020b)

Functions

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Segmentation Techniques

`graythresh`	Global image threshold using Otsu's method
`multithresh`	Multilevel image thresholds using Otsu’s method
`otsuthresh`	Global histogram threshold using Otsu's method
`adaptthresh`	Adaptive image threshold using local first-order statistics
`grayconnected`	Select contiguous image region with similar gray values using flood-fill technique
`watershed`	Watershed transform
`activecontour`	Segment image into foreground and background using active contours (snakes) region growing technique
`lazysnapping`	Segment image into foreground and background using graph-based segmentation
`grabcut`	Segment image into foreground and background using iterative graph-based segmentation
`imseggeodesic`	Segment image into two or three regions using geodesic distance-based color segmentation
`imsegfmm`	Binary image segmentation using fast marching method
`gradientweight`	Calculate weights for image pixels based on image gradient
`graydiffweight`	Calculate weights for image pixels based on grayscale intensity difference
`imsegkmeans`	K-means clustering based image segmentation
`imsegkmeans3`	K-means clustering based volume segmentation
`superpixels`	2-D superpixel oversegmentation of images
`superpixels3`	3-D superpixel oversegmentation of 3-D image

Display Segmentation Results

`imoverlay`	Burn binary mask into 2-D image
`labeloverlay`	Overlay label matrix regions on 2-D image
`label2idx`	Convert label matrix to cell array of linear indices
`boundarymask`	Find region boundaries of segmentation

Evaluate Segmentation Accuracy

`jaccard`	Jaccard similarity coefficient for image segmentation
`dice`	Sørensen-Dice similarity coefficient for image segmentation
`bfscore`	Contour matching score for image segmentation

Topics

Segmentation Using Image Segmenter

Getting Started with Image Segmenter

Segment an image using different techniques, refine and save the binary mask, and export the segmentation code by using the Image Segmenter app.
Install Sample Data Using Add-On Explorer
Image Processing Toolbox Image Data package contains sample 3-D volumetric data.

Segmentation Using Color Thresholder

Segment Image and Create Mask Using Color Thresholder
Segment an image based on color values and create a binary mask image using Color Thresholder.
Acquire Live Images in Color Thresholder
Perform color thresholding on an image acquired from a live USB webcam using the Color Thresholder app.

Segmentation Using Gabor Filters, Superpixels, and Other Techniques

Plot Land Classification with Color Features and Superpixels
Classify land types based on color features using K-means clustering and superpixels.
Segment Lungs from 3-D Chest Scan
Perform a 3-D segmentation using active contours (snakes) and view the results using the Volume Viewer app.

Segmentation Using Volume Segmenter

Create Binary Mask Using Volume Segmenter
This example shows how to segment a volume in the Volume Segmenter app.
Create Semantic Segmentation Using Volume Segmenter
This example shows how to create a semantic segmentation of a volume using the Volume Segmenter app.
Work with Blocked Images Using Volume Segmenter
Segment a volumetric image that could be too large to fit into memory by converting the volume to a blocked image.

Featured Examples

Texture Segmentation Using Gabor Filters

Use texture segmentation to identify regions based on their texture. The goal is to segment the dog from the bathroom floor. The segmentation is visually obvious because of the difference in texture between the regular, periodic pattern of the bathroom floor, and the regular, smooth texture of the dog's fur.

Open Live Script