An Adaptive Algorithm for Multimodal Focus Functions in Automated Fluorescence Microscopy
| Authors | |
|---|---|
| Year of publication | 2008 |
| Type | Article in Proceedings |
| Conference | Medical Imaging Conference |
| MU Faculty or unit | |
| Citation | |
| Field | Use of computers, robotics and its application |
| Keywords | automated microscopy; focus function |
| Description | This work presents a new autofocusing algorithm for fluorescence microscopy that aims at finding all significant planes of focus in cases that the focus function applied on real data is not unimodal, which is often the case. First, nineteen focus functions are tested and their ability to show local maxima clearly is evaluated. The results show that only six focus functions work successfully. Then adaptively variable step size is introduced because wide range of possible focus positions has to be passed not to miss a local maximum. The algorithm therefore assesses the steepness of the focus function on-line so that it can decide whether bigger or smaller step size should be used for acquiring next image. It is shown that for Normalized Variance, the knowledge about steepness can be obtained after normalizing with respect to the theoretical maximum of this function. The resulting algorithm is reliable and efficient compared to a simple procedure with constant steps. |
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