Bacterial segmentation poses significant challenges due to
lack of structure, poor imaging resolution, limited contrast
between touching 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 an unstructured 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 location and trending
direction, the proposed method can automatically classify

and detect differently oriented aggregations of linear structures
(represent 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 by 4%. The performance in preliminary 3D results exhibit authentic classification of the cells

with 97% accuracy.