Image regions, also called objects,
connected components, or
blobs, have properties such as area, center
of mass, orientation, and bounding box. To calculate these properties
(and many more), you can use the Image Region Analyzer app or the
You can also measure pixel values of individual pixels, along a path in an image, or aggregated over an entire image.
|Image Region Analyzer||Browse and filter connected components in an image|
|Measure properties of image regions|
|Measure properties of 3-D volumetric image regions|
|Area of objects in binary image|
|Generate convex hull image from binary image|
|Euler number of binary image|
|Measure Feret properties|
|Find perimeter of objects in binary image|
|Find connected components in binary image|
|Remove small objects from binary image|
|Extract objects from binary image by size|
|Extract objects from binary image using properties|
|Select objects in binary image|
|Select objects in binary volume|
|Label connected components in 2-D binary image|
|Label connected components in binary image|
|Create label matrix from |
|Convert label matrix into RGB image|
|Create label matrix from set of ROIs|
|Convert region of interest (ROI) polygon to region mask|
An object in a binary image is a set of connected pixels with the same value. You can count, label, and isolate objects, and you can measure object properties such as area.
This example shows how to calculate the properties of regions in binary images by using the Image Region Analyzer app.
To determine the values of one or more pixels in an image, you can select points on an image interactively, or you can specify the pixel coordinates in an array.
You can display information about the location and value of individual pixels or small neighborhoods of pixels.
You can measure the length of a line segment drawn between two pixels. You can refine the position of the line segment and make multiple measurements.
The intensity profile of an image is the set of intensity values taken from regularly spaced points along a line or path in the image.
You can compute standard statistics of all pixels in a 2-D image. The statistics in the toolbox differ from their 1-D counterparts, which operate on each column of an image separately.
This example shows how to create a histogram that shows the distribution of intensities in a grayscale image.
The distance transform of a binary image shows the distance from each pixel to a nonzero pixel. There are different ways to measure the distance between two pixels.
A contour is a path in an image along which intensity values are constant. Contour plots can show the outline of objects in an image or represent a 3-D shape in a 2-D plane.