Binary image
The measurement of the area of connected regions is a common operation in Image Analysis. Image Analysis applies to operations performed on images (2D data) to make quantitative measurements in order to describe an image. That is, we want to extract certain features of an image. A feature is an attribute (primitive) that is used to make decisions about objects in an image. Some primitives are natural and are defined by the visual appearance of the image. There are other features called artificial features and are a result of operations performed on an image. The procedures applied for image analysis are application-oriented. That is, what it is good for one application may not be suitable for another. An important concept not to forget is that procedures on the data will not increase the information content of the original data.
This experiment describes a procedure to generate an image where the area of each connected region is associated to its visual appearance.
The binary image and the associated labeled image are shown below.
Binary image
Labeled image
Pseudocoloring is useful to better visualize and distinguish the connected regions (cells), especially when the labeled number of neighboring regions are near each other. Grey scale visualization is poor for discerning between closely connected regions.
Random pseudocolor: labeled image
The histogram of the labeled image gives the following information. The length of the table gives the number of connected regions and its contents gives the area associated with each region.
# Size: Width = 1, Height = 105, Depth = 1, Time = 1, Elements = 1 0 79409 1 204 2 542 3 795 4 784 5 946 6 242 ... ... ... 101 74 102 89 103 95 104 333There are 104 connected regions plus the background. Region 1 has 204 pixels, region 104 has 333 pixels, etc.
Mapping the labeled image with the histogram as a colormap yields
Area display of connected regions
In this image, the value of each pixel is the area of the region it belongs to.