Fuzzy image segmentation with Fuzzy Labelled Neural Gas

Abstract. Processing biological data often requires handling of uncertain and sometimes inconsistent information. Particularly when coping with image segmentation tasks against biomedical background, a clear description of for example tissue borders is often hard to obtain. On the other hand, there are only a few promising segmentation algorithms being able to process fuzzy input data. This paper describes one novel alternative applying the recently introduced Fuzzy Labelled Neural Gas (FLNG) as subsequent classification step to a biologically relevant fuzzy labelling with underlying image feature extraction.

• Type: Conference
• Year: 2006
• Where: ESANN
• Authors: Cornelia Brüß, Felix Bollenbeck, Frank-Michael Schleif, Winfriede Weschke, Thomas Villmann, Udo Seiffert
• Keywords: ESANN 2006, ESANN 2007, Fuzzy Input Data, Image Segmentation Tasks, Relevant Fuzzy Labelling