Name
Abstract | ||
|---|---|---|
Cell counting is an important problem in many microscopy related applications of cellular functional analysis or disease diagnosis. While automatic 2D image processing and pattern recognition approaches for cell counting have been studied, their 3D counterparts are less visited, especially when the cells are crowded. Here we propose a discriminative model to count cells automatically and apply it to for 3D confocal images of the Drosophila melanogaster (fruit fly)'s larval nervous system. We design and implement the process of cell counting based on the detection of nuclear centers; we formulate a binary classification task that incorporates both 3D morphological and texture features. Compared with unsupervised cell counting based on template matching, our model produces more reliable results. This supervised model also requires a smaller number of parameters than the unsupervised approach, and at the mean time improves the robustness. Our cell counting method is also computationally efficient, which is important for high-throughput quantification of 3D cellular images. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1145/2147805.2147858 | BCB |
Keywords | Field | DocType |
discriminative model,unsupervised cell,important problem,binary classification task,drosophila melanogaster,supervised model,confocal image,cellular functional analysis,cellular image,cell counting,unsupervised approach,functional analysis,pattern recognition,classification,template matching,nervous system,high throughput,binary classification | Template matching,Computer vision,Binary classification,Pattern recognition,Biology,Image processing,Robustness (computer science),Artificial intelligence,Confocal,Cell counting,Discriminative model | Conference |
Citations | PageRank | References |
4 | 0.56 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jie Zhou | 1 | 16 | 2.41 |
| Hanchuan Peng | 2 | 3930 | 182.27 |