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Mendeley readers
| Title |
Multi-scale Gaussian representation and outline-learning based cell image segmentation
|
|---|---|
| Published in |
BMC Bioinformatics, August 2013
|
| DOI | 10.1186/1471-2105-14-s10-s6 |
| Pubmed ID | |
| Authors |
Muhammad Farhan, Pekka Ruusuvuori, Mario Emmenlauer, Pauli Rämö, Christoph Dehio, Olli Yli-Harja |
| Abstract |
High-throughput genome-wide screening to study gene-specific functions, e.g. for drug discovery, demands fast automated image analysis methods to assist in unraveling the full potential of such studies. Image segmentation is typically at the forefront of such analysis as the performance of the subsequent steps, for example, cell classification, cell tracking etc., often relies on the results of segmentation. |
Mendeley readers
The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 2 | 7% |
| Finland | 1 | 3% |
| Denmark | 1 | 3% |
| Spain | 1 | 3% |
| United States | 1 | 3% |
| Unknown | 24 | 80% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 11 | 37% |
| Researcher | 7 | 23% |
| Student > Master | 6 | 20% |
| Student > Bachelor | 1 | 3% |
| Other | 1 | 3% |
| Other | 2 | 7% |
| Unknown | 2 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 8 | 27% |
| Computer Science | 7 | 23% |
| Engineering | 5 | 17% |
| Biochemistry, Genetics and Molecular Biology | 3 | 10% |
| Medicine and Dentistry | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 5 | 17% |