Real-time multi-circle detection has been a challenging problem in the field of biomedical image processing, due to the variable sizes and non-ideal shapes of cells in microscopic images. In this study, two new multi-circle detection algorithms are developed to facilitate an automatic bladder cancer diagnosis system: one is a modified circular Hough Transform algorithm integrated with edge gradient information; and the other one is a stochastic search approach based on real valued artificial immune systems. Computer simulation results show both algorithms outperform traditional methods such as the Hough Transform and the geometric feature based method, in terms of both precision and speed.
| Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-1777 |
| Date | 01 May 2012 |
| Creators | Lu, Dingran |
| Publisher | DigitalCommons@CalPoly |
| Source Sets | California Polytechnic State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Master's Theses |
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