DSP-FPGA Based Image Processing System Final Presentation Jessica Baxter  Sam Clanton Simon Fung-Kee-Fung Almaaz Karachi  Doug Keen Computer Integrated Surgery II May 3, 2001

Plan of Action Project Description Implementation Overview Significance Results Future Directions

Project Overview Objective: To develop a robust image processing system using adaptive edge detection, taking advantage of a DSP and FPGA hardware implementation to increase speed. Deliverables –Minimum: Adaptive Edge Detection Software –Expected: Software Implemented in Hardware, Handling of Static Images –Maximum: Real-time Handling of Input

Purpose Gain a better understanding of Genetic Algorithms for use in DSPs and FPGAs. To develop a robust image processing system using adaptive edge detection, taking advantage of DSP and FPGA hardware Edge Detection Optimization Software Adaptive Edge Detection Software Implemented in Hardware, Handling of Static Image Real-time Processing of Live Input

Plan of Action Project Description Implementation Overview Significance Results Future Directions

GA Method for Adaptive Image Segmentation System: Software Side 1)Input image 2)Compute image statistics. 3)Segment the image using initial parameters. 4)Compute the segmentation quality measures 5)WHILE not DO a)Select individuals using the reproduction operator b)Generate new population using the crossover and mutation operators c)Segment the image using new parameters d)Compute the segmentation quality measures END 6)Update the knowledge base using the new knowledge structure Figure: Bhanu, Lee

Hardware Assignment DSP’s serve as the main processor and FPGA’s provide support as co-processors. The genetic algorithm (GA) is included in the DSP. FPGA’s compute the image statistics and the segmentation of quality measure.

Functional Break-Up: FPGA: Image Acquisition Basic Image Processing (ex. Brightness) Image Analysis – choosing and calculating statistical parameters Segmentation – background extraction Evaluation of Metrics of Population Fitness DSP: Initiation of Genetic Algorithm Optimization Join – calls vector graphic file to align segmented pieces CRT: Output (including values of statistical evaluation parameters)

Plan of Action Project Description Implementation Overview Significance Results Future Directions

Significance Leads to increases in –Reliability –Adaptability –Performance Medical technology: –Demands: High reliability and performance – Leads to Development of failsafe, precise sensor systems for computer-integrated surgical applications –Retinal Applications

Plan of Action Project Description Implementation Overview Significance Results Future Directions

Demonstration Genetic Algorithm

Background Extraction Extract background from input image to isolate areas that contain useful information Use algorithm presented in: Rodriguez, Arturo A., Mitchell, O. Robert. “Robust statistical method for background extraction in image segmentation” Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision. Vol. 1569, 1991 Output to evaluation module

Original Image

Results Image After Preprocessing

Background Extraction Output

Processed Output

Extracted Image

Another Example

Plan of Action Project Description Implementation Overview Significance Results Future Directions

Work to date Developed a first draft of an edge detection optimization algorithm Developed C and Matlab coding modules to be used for direct mapping into TI C67 DSP and Xilinx Virtex FPGA

Future Directions Integrate with image capture device - Important for reaching the maximum goal of real-time visual processing CRT: Output (including values of statistical evaluation parameters) Integrate code into Xilinx and TI parts Further develop ideas for potential collaboration with JHU Wilmer Eye Institute

Acknowledgments Dr. Charles Johnson-Bey Co- Researchers – Morgan State Student - Nykia Jackson

Questions