Bacterial cell segmentation and imaging processing
Water is essential to life, 72% of the planet earth is covered in water. However, less than 1% is available for drinking, industry and nature. 1 ml of drinking water on average contains 80,000 of bacteria, and some are harmful to human body such as Cryptosporidium, E.coli, Giardia, Hepatitis A, Legi...
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| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2019
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| Online Access: | http://hdl.handle.net/10356/78017 |
| _version_ | 1811685082400817152 |
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| author | Gao, Peiji |
| author2 | Liu Aiqun |
| author_facet | Liu Aiqun Gao, Peiji |
| author_sort | Gao, Peiji |
| collection | NTU |
| description | Water is essential to life, 72% of the planet earth is covered in water. However, less than 1% is available for drinking, industry and nature. 1 ml of drinking water on average contains 80,000 of bacteria, and some are harmful to human body such as Cryptosporidium, E.coli, Giardia, Hepatitis A, Legionella pneumophila, and Salmonella. Consumption of raw water may cause disease or even death due to waterborne bacterial infection. Monitoring of the water quality is necessary and crucial. My project focus on the detection of Cryptosporidium and Giardia as these microorganisms will highly cause the outbreak of diseases. Cytometer will be used to detect and photograph the various particles found in preconcentrated sample of drinking water, the liquid mixture with sample and fluid is injected into the flow cytometer instrument, ideally the cell will flow through the laser beam one by one and the light scattered is characteristic to the cells and their components. With this flow cytometry technology, thousands of cells can be examined in short time. However, the basic characteristic have to be measured and collated manually. Manual labelling requires the lab user to have the relevant expertise to handle the detection process and measure the samples individually. Repetition of this process and long operation time may lead to human error, this can affect the accuracy of the data and efficiency of the measurement process. With this algorithm implantation, the characteristic of the bacterial image will be measure automatically and collated into excel file for further usage. Follow by the user interface design, the designed software reduce the disadvantage of manual measurement and enhance the data accuracy as well. |
| first_indexed | 2024-10-01T04:38:52Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/78017 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:38:52Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/780172023-07-07T17:45:59Z Bacterial cell segmentation and imaging processing Gao, Peiji Liu Aiqun Muhammad Faeyz Karim School of Electrical and Electronic Engineering DRNTU::Engineering::Computer science and engineering::Software::Programming languages Water is essential to life, 72% of the planet earth is covered in water. However, less than 1% is available for drinking, industry and nature. 1 ml of drinking water on average contains 80,000 of bacteria, and some are harmful to human body such as Cryptosporidium, E.coli, Giardia, Hepatitis A, Legionella pneumophila, and Salmonella. Consumption of raw water may cause disease or even death due to waterborne bacterial infection. Monitoring of the water quality is necessary and crucial. My project focus on the detection of Cryptosporidium and Giardia as these microorganisms will highly cause the outbreak of diseases. Cytometer will be used to detect and photograph the various particles found in preconcentrated sample of drinking water, the liquid mixture with sample and fluid is injected into the flow cytometer instrument, ideally the cell will flow through the laser beam one by one and the light scattered is characteristic to the cells and their components. With this flow cytometry technology, thousands of cells can be examined in short time. However, the basic characteristic have to be measured and collated manually. Manual labelling requires the lab user to have the relevant expertise to handle the detection process and measure the samples individually. Repetition of this process and long operation time may lead to human error, this can affect the accuracy of the data and efficiency of the measurement process. With this algorithm implantation, the characteristic of the bacterial image will be measure automatically and collated into excel file for further usage. Follow by the user interface design, the designed software reduce the disadvantage of manual measurement and enhance the data accuracy as well. Bachelor of Engineering (Electrical and Electronic Engineering) 2019-06-11T04:00:00Z 2019-06-11T04:00:00Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/78017 en Nanyang Technological University 45 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering::Software::Programming languages Gao, Peiji Bacterial cell segmentation and imaging processing |
| title | Bacterial cell segmentation and imaging processing |
| title_full | Bacterial cell segmentation and imaging processing |
| title_fullStr | Bacterial cell segmentation and imaging processing |
| title_full_unstemmed | Bacterial cell segmentation and imaging processing |
| title_short | Bacterial cell segmentation and imaging processing |
| title_sort | bacterial cell segmentation and imaging processing |
| topic | DRNTU::Engineering::Computer science and engineering::Software::Programming languages |
| url | http://hdl.handle.net/10356/78017 |
| work_keys_str_mv | AT gaopeiji bacterialcellsegmentationandimagingprocessing |