Design and FPGA Implementation of Real-Time Hardware Co-Simulation for Image Enhancement in Biomedical Applications

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1 Design and FPGA Implementation of Real-Time Hardware Co-Simulation for Image Enhancement in Biomedical Applications Mohammed Alareqi Laboratory of Electrical Engineering & Energy Systems, Faculty of Science, Ibn Tofail University Kenitra, Morocco Community College, Sana'a, Yemen R. Elgouri National School of Applied Sciences Ibn Tofail University Kenitra, Morocco M. Tarhda, K. Mateur, A. Zemmouri, A. Mezouari, L. Hlou. Laboratory of Electrical Engineering & Energy Systems, Faculty of Science, Ibn Tofail University Kenitra, Morocco Abstract This paper presents the design and the implementation of real-time hardware digital image processing techniques for biomedical applications in a spatial domain on FPGA. It explains various techniques such as inverting image operation, control, segmentation (threshold) and contrast stretching. A comparative study of all these techniques is carried out to find the best technique to enhance a biomedical image on FPGA. These techniques are applied to the hand image with veins using Open Access Biomedical Image Search Engine. The result shows the controlling addition technique provides better of a biomedical image. The purpose of this work is to achieve a realtime hardware implementation with higher execution in both size and speed. It focuses on the implementation of an efficient architecture by using the fewest possible system generator blocks for DSP tool, which integrates itself with the high-level graphical interface of MATLAB Simulink environment and relieves the user from the use of the textual HDL programming. Performances of efficient architecture are implemented on FPGA Virtex5 (XUPV5- LX11T). Keywords ; Image processing; XSG; FPGA; DSP; Biomedical image I. INTRODUCTION Biomedical image (BIE) is one of the most important and difficult techniques in the field of digital images processing research. The principal objective of BIE is to ameliorate the visual appearance of an image or to provide a better transform representation for future automated image processing [1]. BIE techniques can be performed both by spatial domain as well as by frequency domain [2] [3]. Spatial domain techniques are analyzed in this paper. In spatial domain techniques, we directly deal with the image pixels. The pixel values are manipulated to achieve desired. Spatial domain process is Enhancement by point processing methods which are based only on the intensity of single pixels. Frequency domain techniques achieve using mathematical transforms such as Fourier transforms [4]. Image processing in spatial domain involves the adjustment of, contrast or color of an image. Manipulation of these attributes results in of pictorial visual information, which in turn, reveals enough details to allow proper interpretation of the intended application. One of the most important stages in biomedical images detection and analysis is image, which improves the quality of images for human viewing. Removing blur and noise, increasing contrast, and revealing details are examples of operations. The technique differs from one field to another according to its objective. Biomedical image processing is one of the important DSP applications that require accurate mathematical platform for producing the best results in the understanding and to diagnose of various diseases. Add to that, biomedical image processing in real-time is really challenge because the image resolution and the frame rate are higher. The traditional way to enhance images in the frequency domain cannot satisfy the requirement of the realtime image due to the transform between different domains. The main advantage of spatial domain techniques is that they are conceptually simple to understand and their complexity is low which suits real-time implementations. FPGA has many significant features that serve as a platform for processing real-time algorithm. It gives substantially higher performance over programmable Digital Signal Processor (DSP) and the microprocessor. Along with the development of the programmable logic device, the FPGA application manages to provide a new solution for high-speed image processing. It makes full use of the parallel and flexibility of FPGA, improves the speed of image processing, not only reduces the cost but also makes the real-time image processing to obtain a satisfactory effect [5]. Furthermore, the implementation of image processing algorithms on FPGA minimizes the time-to-market cost, enables rapid prototyping of complex algorithms and simplifies debugging and verification. This Implementation on FPGA has the advantage of using large memory and embedded multipliers. Therefore, FPGAs are a Perfect choice for the implementation of real-time biomedical image processing algorithms /17/$ IEEE

2 The need to process the image in real time, lead to the implementation level hardware, which offers parallelism, and thus significantly reduces the processing time, which was why decided to use XSG in FPGA. XSG is a tool with a graphical interface under the Matlab Simulink, based blocks, which make it very easy to handle with respect to other software for hardware description. In addition to offering all, the tools for an easy graphical simulation level [6]. The rest of the paper is organized as follows: Section two presents related work. The methodology of proposed work displays in section three. Sections four describes the Implementation. Results and discussions are present in Section five. Finally, the concluding remarks are given in Section six. II. RELATED WORK Most BIE algorithms are implemented in software [7]. Implementation BIE on hardware is a big challenge for most of the researchers because it requires large and complex hardware. The FPGA technology has received much attention by the digital electronic engineer for implementing image-processing applications. In [8] a new method of Parallel 2-D MRI Image filtering algorithms using Xilinx System Generator was proposed and implemented on the field programmable gate array. Advanced CT and MR Image processing with FPGA was presented in [9]. Efficient Real-Time Hardware Co-Simulation for image applications was presented in [1]. In [11] FPGA implementation of spatial image filters using XSG was proposed. Implementation of image processing algorithms using XSG was presented in [12]. FPGA implementation of an efficient partial volume interpolation for medical image registration was proposed in [13]. Real time implementation of detection of bacteria in microscopic images using system generator was presented in [14]. In [15] an overview of MRI brain classification using FPGA implementation was described. This article presents the architecture of image-processing algorithm applicable to of a biomedical image by using system generator, which is an extension of Simulink and consists of a library called "Xilinx blocks ". "Xilinx blocks " are mapped architectures, entities, signs, gates and attributes, which script file to produce synthesis in FPGAs, HDL simulation, and development tools. III. METHODOLOGY TO PROPOSED WORK The objective of this paper is to implement biomedical image algorithms by using the fewest possible System Generator Blocks on FPGA. Using XSG for still image processing as shown in the proposed block diagram Fig.3. The processing method needs to be implemented in hardware in order to meet the real-time applications. FPGA implementation can be performed using prototyping environment using Matlab/Simulink and XSG tool goes through 5 phases: Image source Image pre-processing blocks Algorithm model and design using XSG Image post-processing blocks Image viewer Image source and image viewer are Simulink blocks by using these blocks image can give as input (Image from file block reads the image from file) and output image can be viewed on the image viewer block (Video viewer block is used to display the output image back on the monitor). The pre-processing and post-processing Unit that transmits the image into the suitable standard of image processing for next unit are also given by Simulink blocks. Those units were described in [16]. The image-processing algorithm is designed by Xilinx blocks. The block diagram of BIE algorithm is shown in Fig.1. A. Xilinx System Generator model of the BIE algorithm In this paper, XSG provides a virtual platform for easy and effective design of the required system models in Xilinx FPGA environments. Optimized automatic VHDL code is generated for the biomedical image-processing algorithm, which is simulated using ISE XSG offers the advantages: Less time in the design and development Automatic generation of the target synthesizable code Easy to modify the design architecture and requirements Reusability and flexibility Less background knowledge of the implementation platform is sufficient There are various steps as shown in the flowchart in Fig.2 to understand the development phases of any algorithm with XSG following the digital image processing approach. The design steps are as follows: Step 1: All the blocks that we need for the algorithm development are collected from the Xilinx block set in the Simulink toolbox library. The gateway in (input) and the gateway out (output) blocks are used for defining the FPGA boundary. All the blocks are arranged and connected according to the specific algorithm. Step 2: After arranging the entire Xilinx block it is must place the XSG block in the model otherwise the model is unable to run showing error. XSG token is necessary as it provides a virtual platform for the simulation of the Xilinx blocks as if they are acting like a real FPGA.

3 Fig. 1. Design flow of hardware implementation of biomedical image Step 3: After the successful simulation, the results are to be analyzed and XSG token is used for setting the parameter to select proper FPGA kit. The package is to be defined for the kit available with the experimentation lab. If the experimental environment is compatible with the Xilinx ISE edition and Matlab version, then only the VHDL code for the algorithm is created automatically by calling back to ISE. XSG Model for BIE Algorithm is shown in Fig.3. Different image processing algorithms like Image Negative, Image Enhancement, Contrast Stretching, and Image Segmentation are designed in System Generator with the help of Xilinx and Simulink blocksets. B. Study Case In this study, the technique of was applied on medical image, hand image with veins. It was taken from Open Access Biomedical Image Search Engine. Image quality measures (IQMs) are figures of merit used for the evaluation of imaging systems are also evaluated. All image-processing algorithms are designed in XSG with the help of Xilinx and Simulink blocks. Fig.2. Xilinx system generator design steps for algorithm development IV. IMPLEMENTATION BIE techniques are designed in MATLAB and Simulink (system generator) and it is implemented on FPGA. All steps start by generating the Simulink model for the system using Simulink blocks in MATLAB until it gets downloaded to FPGA shown in the Fig.4. Software and hardware testing platforms used in this paper are the PC with Intel Core i5 2.67GHz and a 4GB memory, windows OS, MATLAB (R211a), ISE14.1. The hardware implementation results are produced using Xilinx Virtex-5 XC5VLX11T FPGA.

4 A B Fig.3.a) Proposed Model for Grey Level BIE (Algorithm model and design using XSG). b) Proposed Model for Color BIE (Algorithm model and design using XSG) schematic for the biomedical image algorithms (thresholding algorithm) architecture, power analysis and resource utilization can be observed. The RTL schematic and its internal details are shown in Fig.5. Devices utilization statistics, Power and maximum frequency are shown in Table. I. This system blocks are designed for the Virtex-5 ML55 board. Fig.4.Design and implementation flow The Top-level RTLs schematics for all biomedical image algorithms are developed and implemented on FPGA. In this work, we showing just one case of RTL for BIE design. After successful implementation into FPGA the RTL Fig.5. RTL schematic and its internal details image threshold on FPGAs

5 V. RESULTS AND DISCUSSIONS To compare different image techniques, a comparison is made between the image before and after. The different techniques are applied to original hand images with veins distinct Fig.6 (A). The analysis of image is based on the human interpretation and image quality measure for the technique. Fig.6 shows the hand image with veins (original image) and the results of each technique applied to the original image with the histogram of all. Fig6 (A) shows the captured hand image with veins distinct. Fig.6 (B), Fig.6 (C) and Fig.6 (D) show the result from image negative technique for the original images by using a different method of negative. A negative image can be obtained in three ways: Image Negative using XOR operation, Image Negative using NOT operation and Image Negative using AddSub Block. We observed in "Fig.6 (B), (C) and (D)"the Negative image enhances white or gray details embedded in dark regions of the original whereas the dark regions, i.e. vein in the original image are not enhanced. Hence, image negative is not suitable technique for vein detection. Fig.6 (G) shows the result of threshold. It is observed that veins are not clearly in that range properly; hence, this technique of vein patterns is also not sufficient. Fig.6 (H) shows the result of contrast stretching. It is observed that veins are not clearly sliced in that range properly. Hence, contrast stretching is not suitable technique for vein detection. Brightness controlling is categorized into two parts: Brightness addition and Brightness subtraction. Fig.6 (E) and Fig.6 (F) show the result of controlling, and addition respectively. It is observed that veins are clearly, hence, controlling technique provides better result compared to all other technique. Since a measure (PSNR) percentage of Signal to Noise, to find the efficiency of the user processing to increase the clarity of the resulting image, recalling the highest value of the (PSNR) arrival of the image to a high degree of clarity and symmetry in the color intensity resulting [17] Table I shows the image quality measure, which is calculated for all the techniques designed in the paper, based on full-reference method. The quality of the enhanced image is evaluated by comparing it with a reference image that is assumed to have perfect quality. The result clearly shows that the controlling addition provides the best result. When we compare a value of the (PSNR) of our BIE techniques with the value of the (PSNR) in [7] we find our proposed model has the better value of the (PSNR) than [7]. Type of Image (A)-Original (input image) (B)-Negative image using XOR operation (C)-Negative image using NOT operation (D)-Negative image using AddSub (E)-Image controlling using addition (F)-Image controlling using subtraction (G)-Image threshold (H)-Image contrast stretching Hand image with veins histogram of image Fig.6 Hand image with veins (original image) and the result of each BIE technique applied to the original image with the histogram of all Top-level module

6 Method Mean Square Error (MSE) TABLE I. IMAGE QUALITY MEASURE FOR THE BIE TECHNIQUE Peak Signal Normalized Average Structural Noise Ratio Cross Difference content (PSNR) Correlation (AD) (SC) (NCC) Maximum Difference (MD) Normalized Absolute Error (NAE) Negative XOR e NegativeNOT 2.283e Negative e AddSub Brightness Controlling Addition Brightness Controlling Subtraction Threshold Contrast Stretching e e e VI. Conclusion The various contrast BIE techniques are effectively applied on medical image, hand image with veins. From these techniques; -controlling addition gives the best result and hopefully could give exact information about the vein pattern in the image. This paper realizes hardware architecture based imageprocessing algorithm applicable to the of biomedical image applications on FPGAs mainly by using a graphical user interface that mixes MATLAB, Simulink and Xilinx System Generator. As a result, the vein can be detected using this technique appears to be clearer and would provide ease further analysis in vein application. REFERENCES [1] V. Gurunathan, S. Bharathi and R. Sudhakar, "Image techniques for palm vein images," In Advanced Computing and Communication Systems, 215 International Conference on IEEE, pp. 1-5, 215. [2] P. Pawar and R. Patil, "FPGA implementation of canny edge detection algorithm," International Journal of Engineering and Computer Science, vol. 3, pp , 214. [3] J. Yang, Y. Ma, W. Yao and W. Lu, "A spatial domain and frequency domain integrated approach to fusion multifocus images," International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 37, 28. [4] M. Alareqi, K. Mateur, R. Elgouri and L. Hlou, "FPGA Based Image Processing Algorithms (Digital image techniques) using xilinx system generator," European Journal of Scientific Research, vol. 134, no. 3, pp , 215. [5] C. Ramos-Arregu'n, J. Morales, J. M. R. Arreguína, J. C. P. Ortega, S. T. Arriagaa, M. Fernandez and J. Magdaleno, "FPGA open architecture design for a VGA driver," Procedia Technology, vol. 3, pp , 212. [6] C. Moctezuma, S. Sanchez, R. Alvarez and A. Sánchez, "Architecture for filtering images using xilinx system generator," International Conference on Computer Engineering and Applications. World Scientific and Engineering Academy and Society (WSEAS), pp , 28. [7] R. Prasanna, P. Neelamegam, S. Sriram and N. Raju, "Enhancement of vein patterns in hand image for biometric and biomedical application using various image techniques," Procedia Engineering, vol. 38, pp , 212. [8] S. Hasan, A. Yakovlev and S. Boussakta, "Performance efficient FPGA implementation of parallel 2-D MRI image filtering algorithms using xilinx system generator," In Communication Systems Networks and Digital Signal Processing (CSNDSP), 21 7th International Symposium on IEEE., pp , 21. [9] V. Kasik, M. Cerny, M. Penhaker and V. Snášel, " Advanced CT and MR image processing with FPGA," International Conference on Intelligent Data Engineering and Automated Learning. Springer Berlin Heidelberg, pp , 212. [1] U. Nelakuditi, M. Babu and T. Bhagirath, "Efficient real-time hardware co-simulation for image applications, " Electronics and Communication Systems (ICECS), 215 2nd International Conference on. IEEE, pp , 215. [11] V. Elamaran, A. Praveen, M. Reddy and L. Aditya, "FPGA implementation of spatial image filters using XSG, " Procedia Engineering,, vol. 38, pp , 212. [12] K. Deepika, M. Jabeen and K. Sridivya, "implementation of image processing algorithms using xilinx system generator," Journal of Innovation in Electronics and Communication Engineering, vol. 5, no. 1, pp , 215. [13] C. Moses, D. Selvathi and S. Rani, "FPGA implementation of an efficient partial volume interpolation for medical image registration, " Communication Control and Computing Technologies (ICCCCT), IEEE International Conference, pp , 21. [14] A. Ladgham, A. Sakly and A. Mtibaa, " Real Time implementation of detection of bacteria in microscopic images using system generator, " Journal of Biosensors & Bioelectronics, vol. 3, no. 5, pp.1-7, 212. [15] M. Othman, N. Abdullah and N. Rusli, "An Overview of MRI brain classification using FPGA implementation," Industrial Electronics & Applications (ISIEA), IEEE, pp , 21. [16] M. Alareqi, E. Rachid et H. Laamari, "High level FPGA modeling for image processing algorithms using xilinx system generator, " International Journal of Computer Science and Telecommunications, vol. 5, n.16, pp. 1-8, 214. [17] S. Mohapatra, B. Swain et S. K. Mahapatra, "Optimized approach of sobel edge detection technique using Xilinx system generator, " InElectronics and Communication Systems (ICECS), 2nd International Conference,IEEE., pp , 215.