Autofocusing algorithm selection in computer microscopy

Autofocusing is a fundamental technology for automated biological and biomedical analyses and is indispensable for routine use of microscopes on a large scale. This paper presents a comprehensive comparison study of 18 focus algorithms in which a total of 139, 000 microscope images are analyzed. Six samples were used with three observation methods (bright field, phase contrast, an d differential interference contrast (DIC)) under two magnifications (100/spl times/ and 400/spl times/). A ranking methodology is proposed, based on which the 18 focus algorithms are ranked. Image pre-processing is also conducted to extensively reveal the performance and robustness of the focus algorithms. The presented guidelines allow for the selection of the optimal focus algorithm for different microscopy applications.

Authors

• Yu Sun 0001
• Stefan Duthaler
• Bradley J. Nelson

Keywords

• Microscopy
• Focusing
• Biology computing
• Biomedical computing
• Large-scale systems
• Image analysis
• Algorithm design and analysis
• Interference
• Robustness
• Guidelines
• Magnification
• Optimization Algorithm
• Phase Contrast
• Performance Of Algorithm
• Bright Field
• Application Of Algorithm
• Differential Interference Contrast
• Image Preprocessing
• Use Of Microscopy
• Bright Contrast
• Application Of Microscopy
• Field Interference
• Bright Phase
• Convolution
• Autocorrelation
• Low-pass
• Differences In Intensity
• Random Noise
• Second Derivative
• Bright-field Images
• Pre-processing Operations
• Differential Interference Contrast Images
• Depth Of Field
• Global Peak
• Sobel Operator
• Peak Location
• Neighboring Pixels
• Individual Criteria
• Small Width
• autofocusing
• ranking
• selection