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Bacterial biofilm segmentation poses significant challenges due to lack ofapparent structure, poor imaging resolution, limited contrast betweenconterminous cells and high density of cells that overlap. Although there existbacterial segmentation algorithms in the existing art, they fail to delineatecells in dense biofilms, especially in 3D imaging scenarios in which the cellsare growing and subdividing in a complex manner. A graph-based data clusteringmethod, LCuts, is presented with the application on bacterial cellsegmentation. By constructing a weighted graph with node features in locationsand principal orientations, the proposed method can automatically classify anddetect differently oriented aggregations of linear structures (represent bybacteria in the application). The method assists in the assessment of severalfacets, such as bacterium tracking, cluster growth, and mapping of migrationpatterns of bacterial biofilms. Quantitative and qualitative measures for 2Ddata demonstrate the superiority of proposed method over the state of the art.Preliminary 3D results exhibit reliable classification of the cells with 97%accuracy.

LCuts: Linear Clustering of Bacteria using Recursive Graph Cuts