Mathematical morphology (MM) is a popular formalism largely used for image processing. Of particular relevance in MM-based operations is the structuring element (SE). In an image processing environmentSE defines which pixel values, in the input image, to include in the calculation of the output value. Most MM-based image processing environments employ limited size SEs which prevents their use in tasks requiring larger SEs. This paper proposes a computer-based method for optimizing the decomposition ofSEs, in binary image related tasks, that employs binary MM, which automatically transforms an original SE into a corresponding sequence of 3 × 3 SEs. The decomposition operation reduces the complexity of the morphological operations and has been implemented as a genetic algorithm (GA) based process, that searches for the best sequence of smaller structuring elements, using one dilation and four union operations, for the decomposition of each large-sized structuring element. By using a GA with a fixed-length chromosome as well as a fixed number of dilation and union operations, the method has a simple and fixed structure, which makes it a convenient choice for hardware implementations. Its performance, based on six images already used in the literature by other well-established method, has shown to be competitive.
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