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Automated Segmentation and Classification of High Throughput Yeast Assay Spots
| Content Provider | CiteSeerX |
|---|---|
| Author | Jafari-Khouzani, Kourosh Fotouhi, Farshad Parrish, Jodi R. Finley, Russell L. |
| Abstract | Several technologies for characterizing genes and proteins from humans and other organisms use yeast growth or color development as read outs. The yeast two-hybrid assay, for example, detects protein-protein interactions by measuring the growth of yeast on a specific solid medium, or the ability of the yeast to change color when grown on a medium containing a chromogenic substrate. Current systems for analyzing the results of these types of assays rely on subjective and inefficient scoring of growth or color by human experts. Here an image analysis system is described for scoring yeast growth and color development in high throughput biological assays. The goal is to locate the spots and score them in color images of two types of plates named “X-Gal ” and “growth assay ” plates, with uniformly placed spots (cell areas) on each plate (both plates in one image). The scoring system relies on color for the X-Gal spots, and texture properties for the growth assay spots. A maximum likelihood projection-based segmentation is developed to automatically locate spots of yeast on each plate. Then color histogram and wavelet texture features are extracted for scoring using an optimal linear transformation. Finally an artificial neural network is used to score |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | High Throughput Yeast Assay Spot Color Development Yeast Growth Inefficient Scoring X-gal Spot High Throughput Biological Assay Several Technology Current System Wavelet Texture Feature Image Analysis System Color Image Optimal Linear Transformation Chromogenic Substrate Artificial Neural Network Texture Property Maximum Likelihood Projection-based Segmentation Color Histogram System Relies Human Expert Read Out Protein-protein Interaction Specific Solid Medium Yeast Two-hybrid Assay |
| Content Type | Text |