Urine crystal detection in a urinalysis process using the Harris and Stephens corner detection algorithm
In urinalysis, medical technologists have to spend most of their time looking through a microscope trying to identify and count the urine crystals in a sample manually. Their evaluation of the sample is then written in a medical report and sent to the doctor or handed to the patient. This process ta...
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| Main Authors: | , , , |
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| Format: | text |
| Language: | English |
| Published: |
Animo Repository
2012
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| Subjects: | |
| Online Access: | https://animorepository.dlsu.edu.ph/etd_bachelors/11053 |
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| Institution: | De La Salle University |
| Language: | English |
| Summary: | In urinalysis, medical technologists have to spend most of their time looking through a microscope trying to identify and count the urine crystals in a sample manually. Their evaluation of the sample is then written in a medical report and sent to the doctor or handed to the patient. This process takes time and it is common for patients to come back several hours or the next day to get the result from the hospital. To address this issue, the previous research conducted by Lee, A. et. al proposed the automation of urine crystal detection in a urine sample. However, their study only included the detection of single crystals. The authors of this study develops a more robust detection system that will detect not only several single crystals in one urine sample image but overlapping crystal as well.
The system developed will incorporate the Harris and Stephens algorithm and the Slope Corner Detection algorithm made by the group to further improve the corner detection capability and reach an accuracy of 95%. The main programming language used is C++ while the graphical user interface makes use of Java. |
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