Dynamic feature fusion in the self organising tree map - applied to the segmentation of biofilm images

Abstract

The self organising tree map (SOTM) neural network is investigated as a means of segmenting microorganisms from confocal microscope image data. Features describing pixel & regional intensities, phase congruency and spatial proximity are explored in terms of their impact on the segmentation of bacteria and other micro-organisms. The significance of individual features is investigated, and it is proposed that, within the context of micro-biological image segmentation, better object delineation can be achieved if certain features dominate the initial stages of learning. In this way, other features are allowed to become more/less significant as learning progresses: as the network gains more knowledge about the data being segmented. The efficiency and flexibility of the SOTM in adapting to, and preserving the topology of input space, makes it an appropriate candidate for implementing this idea. Preliminary experiments are presented and it is found that favouring intensity characteristics in the early phases of learning, whilst relaxing proximity constraints in later phases of learning, offers a general mechanism through which we can improve the segmentation of microbial constituents