1. By: Mrs. S. Allin Christe, Mr.M.Vignesh, Dr.A.Kandaswamy
AN EFFICIENT FPGA IMPLEMENTATION OF MRI IMAGE FILTERING AND TUMOUR CHARACTERIZATION USING XILINX SYSTEM GENERATOR (2011)
By: Mrs. S. Allin Christe, Mr.M.Vignesh, Dr.A.Kandaswamy
Paper Review by:
Richard Chao

2. Outline Introduction Xilinx System Generator Edge Detection
Methods and Algorithm
Proposed Design
Hardware Implementation
Results
Critiques
Questions
Outline
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3. Introduction Realtime FPGA applications:
Modern Image Processing
Medical Imaging
Image Compression
Wireless Communications
Usually written in high language code C (Slows down implementation)
Xilinx System Blocks to convert Block Models directly to VHDL
Introduction
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4. Xilinx System Generator (XSG)
Simulink is the development environment
Usage of Xilinx blocks makes it a model based design
XSG abstracts arithmetic which means it is able to perform fixed point math with precision-arbitrary.
Easy to move from design to *.bit file to implement on FPGA
XSG produces VHDL code, UCF, Simulation and testbench vectors
Xilinx System Generator (XSG)
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5. An edge is a point or sharp change is in an image in a region where pixel locations have abrupt luminance changes
Main steps of edge detection
Smoothing: Reduce Noise in an image
Enhancement: Enhance quality of the edges
Detection: since many points in the image will have a non-zero gradient, but shouldn't be considered an edge
Localization: Find the edges and their direction
Brightness values are discrete so approximation using a derivative function can be used (edge detection)
Edge Detection
(1)Smoothing: suppress as much noise as possible, without destroying the true edges.
(2) Enhancement: apply a filter to enhance the quality of the edges in the image (sharpening).
(3) Detection: determine which edge pixels should be discarded as noise and which should be retained (usually, thresholding provides the criterion used for detection).
(4) Localization: determine the exact location of an edge (sub-pixel resolution might be required for some applications, that is, estimate the location of an edge to better than the spacing between pixels). Edge thinning and linking are usually required in this step.
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6. Using Derivaitives to find Edges
First Order derivatives find maximas and minimas (Change in intensity)
Second order derivatives detect the zero- crossing
Using Derivaitives to find Edges
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7. XSG Design Flow
A gradient is basically a vector field that points in the direction of the greatest rate of change.
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8. Roberts Cross Operator
Approximate the gradient of an image through discrete differentiation
Sum of the squares of the differences between diagonally adjacent pixels implemented by two 2x2 vector:
The magnitude of gradient and direction are given as:
This method is very sensitive to noise however
Roberts Cross Operator
The Roberts cross operator is a very simple computational filter that that are designed to respond to a maximum of 45 degrees edges. The gradient is to find the strength of the edge, and the direction is to determine the orientation of the edge. However, since the masks are so small, it makes it very susceptible to noise
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9. The Prewitt operator is based on the idea of central difference (approximation of derivative)
It is based on convolving the image with a small, separable integer valued filter in horizontal and vertical direction as shown in equation
The Magnitude and Direction are calculated the same as the Roberts Cross.
Prewitt’s operator still very vulnerable to noise even though it has a larger mask
Prewitt Operator
Next is the prewitt operator. This filter is a 3x3 mash done in both vertical and horizontal direction again, but it is based off the idea of central differences, an approximation of where the edge starts and stops. This filter very susceptible to noise as well.
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10. Like the Prewitt Operator, this method also uses the idea of central difference to find the edge but improves on the noise cancellation.
However, the drawback with the Sobel operator is that it does not handle curved edges very well.
Sobel Operator
The sobel operator tries to fix this problem of noise. However, they both produce a very similar result to one another, and they are both produce very good edges. The downside of the sobel operator is that it does not produce well defined edges that are rounded.
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11. The Scharr Operator attempts to fix the angular detection by computing the weighted mean squared angular error in the Fourier domain:
Scharr Operator
The scharr operator attemps to fix the angular error! The filer does a decent job when dealing with rounded edges
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12. Highlights regions of rapid intensity change (Second order, finds maximums and minimums of gradients, or slope of the change)
Usually image has been smoothed first to reduce the sensitivity of noise
The Laplacian operator is then performed on the image, which is given as:
Laplacian of Gaussian
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13. Gaussian Blur, Edge Sharpening and Thresholding
Gaussian Blur is used to smooth the image and reduce the noise in the image. The equation is given as:
Edge Sharpening is performed to strengthen the output image which usually connects edges to produce a sharper image. The filter is given as:
Thresholding is done to convert grayscale images into binary for faster computation of the filters
Gaussian Blur, Edge Sharpening and Thresholding
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14. Proposed Design in XSG Image will go through three phase
Image Pre-processing blocks
Edge detection using XSG
Image post-processing blocks
For the design of the filters to meet hardware requirements, the images must be pre-processed prior to the edge detection components.
2D Images of M x N size must be made into 1D for the hardware
Proposed Design in XSG
The paper proposed a three phase design that to perform the edge detection. Firstly, the image will need to be pre processed so that the hardware is able to handle the data, then the image will be processed, and lastly, the image will be put back into a human readable form
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15. Image Pre-processing blocks
Input images can be either RGB or grayscale.
Images need to be converted to 1D before edge detection
The frame conversion block provides the unbuffer block frames that can be made in scalar samples to output to the XSG at very high sample rates
Image Pre-processing blocks
The image preprocessing block simply takes any RGB or grayscale image and converts it from 2D M by N matrix and into a 1D MN by 1 array. The frame conversion block translate the data in such a way that the buffer block is able to continuously read the data in.
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16. Edge Detection with XSG
Processes input images for edge detection
Xilinx fixed point conversion is done by using Gateway In blocks
Edge detection is performed using MAC FIR within the DAFIR blocks to perform sum-of-products operation to compute the convolution sum of the input 9x9 kernel
Edge Detection with XSG
Next is the edge detection phase. The Gateway in blocks allow the FPGA to perform fixed point precision arbitrary numbers due to the way that Xilinx has created the block. The DAFIR blocks are the ones performing convolution sum (approximation of derivative) to the input.
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17. Image post-processing blocks
This phase needs to convert the 1D image back to a 2D human readable image
Image post-processing blocks
The last phase of the implementation is to convert the headware 1D image back into 2D.
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18. The systems also tries to outline several types of tumors that it extracts from the images it processes (Region of Interest)
Uses several statistics such as mean, variance, and standard deviation are used to differentiate the different type of tumors it finds
Regions of Interest
The research team also proposed another phase in addition to the edge detection which is to find regions of interest in three different types of tumors.
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19. Hardware Implementation
All the blocks listed above needs to be converted into VHDL where it can be synthesized onto an FPGA
Using the System Generator Block, a *.bit file is generated and sent through the JTAG to be programmed on the FPGA
Spartan 3E starter kit is used (XC3S500E-FG320) for hardware level simulations.
Hardware Implementation
The hardware implementation was made onto a Spartan 3E starter board. This is the system gernator block property dialog to setup Simulink to be able to write to the FPGA. The great thing about using the XSG is that it is able to be configured to all Xilxinx FPGAs, which makes this a very powerful platform.
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20. Complete XSG block diagram with Edge Detection, Blur , Thresholding, and Sharpening
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22. Results
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23. Results
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24. Results
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25. The results used less resources than Zhang’s implementation because he implemented a separate X and Y filter where as this paper performed all the filters in a single stage.
XSG generated 4547 lines of VHDL -> DSP applications are very complex and not logic based, as they include a lot of floating/fixed point operations.
XSG helps minimize design implementation time
Results
Luckly for us, our school has access to the IEEE conference papers are I was able to find out that Zhang’s implementation of the vechicle edge detetion was also written in Simulink, but his proposed design used a lot of redundant blocks to perform the edge detection. This paper cuts down on the amount of blocks by about half.
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26. Example of the ROI extracted extraction from the image
ROI Extracted Images
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27. ROI Accuracy
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28. ROI Accuracy
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29. Critique Paper was well written and easy to follow
Lots of diagrams showing the effects of each filter
Good documentation on the XSG implementation
Multiple references per topic they described in the paper
Does not tell us what filter they ended up using when finding ROI in the MRI images
Does not include the time it takes for the images to be processed
Critique
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30. Conclusion XSG offers model based design for processing
All filters are designed by blocks, MATLB code even supported as well
Generates necessary files for implementation in an Xilinx FPGAs
Able to perform optimization in speed, area, robustness, parallelism
Proposed design can be used on ALL Xilinx FPGA, if System Generator Block is configured properly
Offers fast and efficient real-time image processing
Conclusion
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31. References http://homepages.inf.ed.ac.uk/rbf/HIPR2/roberts.htm
References
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32. Question
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