  Bacterial biofilm segmentation poses significant challenges due to lack of
apparent structure, poor imaging resolution, limited contrast between
conterminous cells and high density of cells that overlap. Although there exist
bacterial segmentation algorithms in the existing art, they fail to delineate
cells in dense biofilms, especially in 3D imaging scenarios in which the cells
are growing and subdividing in a complex manner. A graph-based data clustering
method, LCuts, is presented with the application on bacterial cell
segmentation. By constructing a weighted graph with node features in locations
and principal orientations, the proposed method can automatically classify and
detect differently oriented aggregations of linear structures (represent by
bacteria in the application). The method assists in the assessment of several
facets, such as bacterium tracking, cluster growth, and mapping of migration
patterns of bacterial biofilms. Quantitative and qualitative measures for 2D
data demonstrate the superiority of proposed method over the state of the art.
Preliminary 3D results exhibit reliable classification of the cells with 97%
accuracy.
