Browsing by Subject "Imaging systems in biology."

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Open Access
Perceptual watersheds for cell segmentation in fluorescence microscopy images
(2012) Arslan, Salim
High content screening aims to analyze complex biological systems and collect quantitative data via automated microscopy imaging to improve the quality of molecular cellular biology research in means of speed and accuracy. More rapid and accurate high-throughput screening becomes possible with advances in automated microscopy image analysis, for which cell segmentation commonly constitutes the core step. Since the performance of cell segmentation directly a ects the output of the system, it is of great importance to develop e ective segmentation algorithms. Although there exist several promising methods for segmenting monolayer isolated and less con uent cells, it still remains an open problem to segment more con uent cells that grow in aggregates on layers. In order to address this problem, we propose a new marker-controlled watershed algorithm that incorporates human perception into segmentation. This incorporation is in the form of how a human locates a cell by identifying its correct boundaries and piecing these boundaries together to form the cell. For this purpose, our proposed watershed algorithm de nes four di erent types of primitives to represent di erent types of boundaries (left, right, top, and bottom) and constructs an attributed relational graph on these primitives to represent their spatial relations. Then, it reduces the marker identi cation problem to the problem of nding prede ned structural patterns in the constructed graph. Moreover, it makes use of the boundary primitives to guide the ooding process in the watershed algorithm. Working with uorescence microscopy images, our experiments demonstrate that the proposed algorithm results in locating better markers and obtaining better cell boundaries for both less and more con uent cells, compared to previous cell segmentation algorithms.
Open Access
Smart markers for watershed-based cell segmentation
(2012) Koyuncu, Can Fahrettin
Automated cell imaging systems facilitate fast and reliable analysis of biological events at the cellular level. In these systems, the first step is usually cell segmentation that greatly affects the success of the subsequent system steps. On the other hand, similar to other image segmentation problems, cell segmentation is an ill-posed problem that typically necessitates the use of domain specific knowledge to obtain successful segmentations even by human subjects. The approaches that can incorporate this knowledge into their segmentation algorithms have a potential to greatly improve the segmentation results. In this study, we propose a new approach for the effective segmentation of live cells from phase-contrast microscopy. This approach introduces a new set of “smart markers” for a marker-controlled watershed algorithm, for which the identification of its markers is critical. The proposed approach relies on using domain specific knowledge, in the form of visual characteristics of the cells, to define the markers. We evaluate our approach on a total of 1954 cells. The experimental results demonstrate that the proposed approach is quite effective in identifying better markers compared to its counterparts. This will in turn be effective in improving the segmentation performance of a marker-controlled watershed algorithm.