Towards Automatic Segmentation of Serial High-Resolution Images

Developing barley grains are to be visualised by a 4-D model, in which spatiotemporal experimental data can be integrated. The most crucial task lies in the automation of the extensive segmentation procedure. Because of constraints like incomplete a-priori expert knowledge and the complexity of this specific segmentation task, learning techniques like Artificial Neural Networks (ANN ) yield promising solutions. In this work we present our first good segmentation results. Two different supervised trained ANN classifiers were applied, on one hand, the wellestablished borderline-learning Multiple-Layer Perceptron (MLP) and on the other hand, the prototype-based Supervised Relevance Neural Gas (SRNG). While so far segmentation was mainly achieved using manual tools, now almost automatic segmentation becomes more feasible.

• Type: Conference
• Year: 2006
• Where: BILDMED
• Authors: Cornelia Brüß, Marc Strickert, Udo Seiffert
• Keywords: Automatic Segmentation, BILDMED 2006, BILDMED 2007, Extensive Segmentation Procedure, Specific Segmentation Task