Medical applications of mathematical morphology

Nonlinear filters have become very popular tools in signal and image processing because of their attractive properties. Their characteristics differentiate them strongly from the existing linear filters in may areas of image processing such as noise reduction, edge detection, segmentation, etc. A very important class of nonlinear digital signal/image processing results from the concepts of mathematical morphology and is known as morphological filtering (Serra, 1982) mathematical morphology is a new approach to signal/image analysis, using nonlinear pictorial transformations and functionals derived from set theory and integral geometry. As a result, morphological filters are powerful tools for geometrical shape analysis and description and their applications in image processing and analysis are numerous. Areas of applications include shape smoothing, texture analysis, automated industrial inspection, enhancement and noise suppression. The present paper investigates the use of various existing and new morphological operations for filtering and segmenting real medical data with the purpose of assisting diagnosis.<>