Refining competition in the self-organising tree map for unsupervised biofilm image segmentation.
The Self Organising Tree Map (SOTM) neural network is investigated as a means of segmenting micro-organisms from confocal microscope image data. Features describing pixel and 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 are more dominant in the initial stages of learning. In this way, other features are allowed to become more/less significant as learning progresses: as more knowledge is acquired about the data being segmented. We argue that 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. We propose a refinement to the competitive search strategy that allows for a more appropriate fusion of signal and proximal features, thereby promoting a segmentation that is more sensitive to the regional associations of different microbial matter. A refined stop criterion is also suggested such that the dynamically generated number of classes becomes more data dependant. 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.
Kyan
Matthew
M
School of Electrical and Information Systems Engineering, University of Sydney, NSW 2006, Australia. mkyan@ee.ryerson.ca
Guan
Ling
L
Liss
Steven
S
D000465:Algorithms; D018441:Biofilms; D016000:Cluster Analysis; D007091:Image Processing, Computer-Assisted; D018613:Microscopy, Confocal; D008959:Models, Neurological; D015233:Models, Statistical; D016571:Neural Networks, Computer