Partitions a raster image into arbitrarily sized groups of cells from the input image based on intensity differences in the input raster image cells.

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Typical Uses

  • Medical image processing
  • Computer Vision
  • Object detection
  • Recognition Tasks

How does it work?

The RasterSegmenter accepts features with raster geometry. All other geometry types will be rejected. Each input raster feature will be processed independently.

The RasterSegmenter executes the user selected segmentation algorithm on the input raster geometry and outputs one raster for each segment of each input raster.

Each output segment is a raster feature containing cell values for the segmented area and Nodata or zero values for the remainder depending on the options selected.

It is recommended to use some kind of feature enhancing pre-processing step before using the RasterSegmenter, such as the RasterSharpener or the RasterConvolver with a sharpening kernel.

Bands, Cell types, and Palettes

Rasters may have one band or multiple bands. The RasterSegmenter can handle up to 4 bands of the same datatype. Input rasters with an unexpected number of bands or with inconsistent types, will be rejected.

All raster cell types are accepted but are internally converted to Real64. If non-floating-point cell types are provided as input they will be converted to a floating-point cell type and a warning will be issued.

Alpha bands are preserved by removing them from the input raster prior to processing and appending them to the output raster after processing is complete. The output alpha band will be converted to 8 bit to match the bit depth of the RGB24 output bands.

Rasters may have no palette, one palette, or multiple palettes. For more information on raster structure, see Rasters (IFMERaster). The RasterSegmenter will resolve palettes on input rasters prior to processing. Input rasters with string palette values on cannot be resolved and will be rejected.

Selecting Raster Bands and Palettes

To select specific bands and/or palettes, use the RasterSelector prior to the RasterSegmenter.


Attributes from the input features will be preserved in the output feature.

Segmentation Method


Watershed segmentation partitions the image by considering the image as a function of its coordinates, where the intensity of the raster cells is the value.

A Gradient Magnitude filter is applied to the raster to enhance the borders of the image prior to running a Watershed Filter to produce an intensity based segmentation of the image.

The watershed algorithm does not produce a single image segmentation, but a hierarchy of segmentations. Different segmentations can be selected from this hierarchy by adjusting the maximum water level parameter.

Usage Notes

Choosing a Raster Transformer

FME has an extensive selection of transformers for working with raster data. They can be generally categorized as working with whole rasters, bands, cells or palettes, and those designed for workflow control or combining raster with vector data.

For information on raster geometry and properties, see Rasters (IFMERaster).


Input Ports

Output Ports


Editing Transformer Parameters

Using a set of menu options, transformer parameters can be assigned by referencing other elements in the workspace. More advanced functions, such as an advanced editor and an arithmetic editor, are also available in some transformers. To access a menu of these options, click beside the applicable parameter. For more information, see Transformer Parameter Menu Options.

Defining Values

There are several ways to define a value for use in a Transformer. The simplest is to simply type in a value or string, which can include functions of various types such as attribute references, math and string functions, and workspace parameters. There are a number of tools and shortcuts that can assist in constructing values, generally available from the drop-down context menu adjacent to the value field.

Dialog Options - Tables

Transformers with table-style parameters have additional tools for populating and manipulating values.


Processing Behavior


Feature Holding


Dependencies None

FME Community

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Examples may contain information licensed under the Open Government Licence – Vancouver and/or the Open Government Licence – Canada.