Application of Advanced Morphological Filters into Image Segmentation

This paper is devoted to a segmentation method using advanced morphological filtering by reconstruction followed by clustering by k-means algorithm. Advanced morphological filtering bases on morphological reconstruction and two filters are applied: opening by reconstruction and closing by reconstruction. This kind of operation has very important advantage from the point of view of segmentation it preserves the borders of regions. Traditional filters (opening, closing, linear filters) remove noise, but on the other hand they cause some blur effects, which can be the serious obstacle for correct segmentaion. Morphological filtering by reconstruction has very good filtration properties without changing the shapes. After segmentation simple k-means clustering is performed. Two versions of k-means clustering algorithm is described: classic and fuzzy one. First, 'crisp' version will be applied to cases with a knowledge regarding number of clusters given a priori. Fuzzy version should be used when it is difficult to define number of clusters. The algorithm will automatically adapt number of clusters into the structure of the image. A combination of filtering by morphological reconstruction and clustering makes possible to consider two kind of information: spatial (filtering) and spectral (clustering).