| Summary: | 碩士 === 國立暨南國際大學 === 電機工程學系 === 104 === As technology continues to advance, the need of biological testing such as detection of blood and cancer cells increase as people pay more attention to their health concern. Conventional manual biological testing, cells are counted one by one with eye observation of operators, however, the physical and mental conditions of the operators are likely to affect the observation test to be erroneous results.
An automated cell counting by computer is a solution to replace manual counting which can reduce the labor cost and have less error-prone results. In this thesis, an automated cell counting system, that combines a Raspberry Pi, Pi camera, μ Handy action microscopy and image processing algorithms, including the processes of image Gaussian blur processing algorithm, OTSU binary image and morphological operations, is implemented to perform the cell counting in a mobile device.
With the implemented automatic cell counting system, users can adjust the algorithm in accordance with the properties of cell image to lower the counting error rate. Experimental results show that our system can achieve accuracy higher than 95%.
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