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Multiobjective Optimization for Reconfigurable Implementation of Medical Image Registration

Multiobjective Optimization for Reconfigurable Implementation of Medical Image Registration. Paper Review by: Adam Erb. Purpose:. To present a multi- objective word length strategy for custom image processing applications.

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Multiobjective Optimization for Reconfigurable Implementation of Medical Image Registration

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  1. Multiobjective Optimization for Reconfigurable Implementation of Medical Image Registration Paper Review by: Adam Erb

  2. Purpose: To present a multi-objective word length strategy for custom image processing applications. Compare heuristic methods for finding the Pareto-optimized configuration for a given system requirement. Demonstrate the feasibility of this approach in the context of FPGA based image registration.

  3. What is Image Registration? Medical Image Registration is the process of aligning two images that represent the same anatomy at different times, from different viewing angles. Attempts to align floating image with reference image using a similarity measure. This paper uses the Intensity based Mutual Information method which measures similarity based on image entropies.

  4. a) Image 1 (RI) b) Image 2(FI) c) Aligned images using image registration system.

  5. More Image Registration • Entropy calculations are made with the equations below. • Where p is the probability distribution function based on image histograms. • Thousands of iterations depending of image complexity.

  6. ApproachesSoftware Hardware • Uses fixed point representation of multiple word lengths. • Provides improved performance at reduced area cost. • Finite precision. • Difficult identifying ideal word length for each variable. • Uses floating point representation. • Flexible • Easy • Slow!

  7. FPGA-based Architecture • FGPA is used to calculate the MI of images. • Host platform uses these MI calculations to update candidate transformation. • Image information is stored in external DRAM memory. • In this design four internal variables are parameterized.

  8. Multi-Objective Problem Finding the Pareto-optimal word length for each variable so that required precision is achieved while still using minimum area. Shown to be NP-hard problem. Develop a platform so that the most accuracy can be achieved for a given area, OR the minimum area for a given accuracy.

  9. Multi-Objective Solution! • Framework uses loosely coupled search algorithm to explore design space. • Uses a bit-true emulator to evaluate these solutions. Results are fed back into search algorithm to refine search. • Makes use of 32-processor cluster.

  10. Search Algorithms • Exhaustive Search: • Searches entire design space. • Obviously problematic. If framework is to explore 4 variables ranging from 0-32bits then 32^4 solutions need to be explored. • This will take the cluster 3.5 weeks to complete assuming 1 minute per evaluation! • Guaranteed to find Pareto optimal solution if exists. • Partial Search: • Reduces design space by exploring every other option. • Explores 65,000 solutions (10x Random or EA)

  11. More Search Algorithms • Random Search: • Randomly generates a fixed number of feasible solutions. • Explores 6000 solutions in run. • Evolutionary Search (SPEA2): • Configuration is mapped onto “chromosome” • “genes” represent word length parameter. • Non-dominated solutions are propagated. Requires evaluation results to determine dominated and non-dominated. • Explores 6000 solutions in run.

  12. Methods of Comparison The partial, random, and evolutionary algorithms were all run once. Random and Evolutionary were run 5 times and results were averaged to account for effects of random number generation. Results were taken using bit true emulator.

  13. Qualitative Comparison: • Comparison of Pareto fronts for partial and EA-based search algorithms. • Keep in mind partial runs in approximately 10x the iterations. • EA solutions are more Pareto optimal.

  14. Quantitative Comparison Metrics: • Ratio of Non-Dominated Individuals • We don’t have pareto-optimal solution so for comparison we compare each method against the other. • Quality of solutions. • Compares non-dominated solutions, 100% is best.

  15. Cover Rate • Measurement of solution set distribution. • Partition possible range of solutions and then counts number of partitions covered. • 1 is best.

  16. Postsynthesis Validation Used design results based on EA-search. Chose three of these non-dominated results to show area/performance trade off. Physically synthesized configurations to look at performance, quality, timing, etc.

  17. Validation Results:

  18. Paper Evaluation Generally, well written and interesting. A good example of reconfigurable systems use in medical imaging but contribution of the paper is questionable. Not sure whether the search algorithm exploration was necessary. EA-search was obviously superior from the start and has been demonstrated superior in other papers.

  19. References: • Omkar Dandekar, William Plishker, Shuvra Bhattacharyya, Raj Shekhar, "Multiobjective Optimization of FPGA-Based Medical Image Registration," Field-Programmable Custom Computing Machines, Annual IEEE Symposium on, pp. 183-192, 2008 16th International Symposium on Field-Programmable Custom Computing Machines, 2008 • EckartZitzler, Marco Laumanns, and Lothar Thiele. Spea2: Improving the strength pareto evolutionary algorithm. Technical Report 103, Gloriastrasse 35, CH-8092 Zurich, Switzerland, 2001.

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