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| Subcellular Spot Detection for Fluorescence Microscopic Images |
1 Zhejiang Provincial Key Laboratory of CardioCerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, China
2 Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China
3 State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, Beijing 100094, China |
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Abstract Quantitative analysis of high throughput fluorescence microscopic image is a powerful tool to study dynamic processes in living cells. Many subcellular objects of interest appear as diffraction-limited spots in the image. The limitations of imaging conditions often lead to fluorescence microscopic images inhomogeneous and lower signal-to-noise ratio (SNR), making manual analysis a very challenging task. Designing the automatic subcellular spot detection method is a prerequisite for high throughput fluorescence microscopic image processing. This review presented a detailed overview of recent advances in the key techniques of spot detection method, including noise reduction, signal enhancement and signal thresholding. The bottlenecks and common difficulties of the algorithm design were discussed on the basis of summarizing advantages and disadvantages of the exiting spot detection methods. At the same time, the prospect of the related research was discussed.
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