Several approaches for registering a subset of imaged points to their true origins were analyzed and compared for seed based TRUS-fluoroscopy registration. The methods include the Downhill Simplex method (DS), the Powell's method (POW), the Iterative Closest Point (ICP) method, the Robust Point Matching method (RPM) and variants of RPM. Several modifications were made to the standard RPM method to improve its performance. One hundred simulations were performed for each combination of noise level, seed detection rate and spurious points and the registration accuracy was evaluated and compared. The noise level ranges from 0 to 5mm, the seed detection ratio ranges from 0.2 to 0.6, and the number of spurious points ranges from 0 to 20. An actual clinical post-implant dataset from permanent prostate brachytherapy was used for the simulation study. The experiments provided evidence that our modified RPM method is superior to other methods, especially when there are many outliers. The RPM based method produced the best results at all noise levels and seed detection rates. The DS based method performed reasonably well, especially at low noise levels without spurious points. There was no significant performance difference between the standard RPM and our modified RPM methods without spurious points. The modified RPM methods outperformed the standard RPM method with large number of spurious points. The registration error was within 2mm, even with 20 outlier points and a noise level of 3mm.