Efficient MRI Image Real-Time Processing Using FPGA-Based IIR Filters

Magnetic Resonance Imaging (MRI) is a noninvasive imaging technique capable of producing highly detailed images of the human body. Used mainly for disease diagnosis, general physical examination, and evaluation of the therapeutic response. MRI uses sophisticated techniques to induce and image the changes in the spinning axis of the protons of the water that make up body tissues. MRI data require post-processing which involves conversion of raw data to obtain images, promoting image analysis and interpretation, and supporting additional functions for applications in both clinical and research fields [1].

The conversion of an image from its original analog form to a digital form and then subjected to several other processes such as analysis, manipulation, or enhancement, is referred to as image processing. This field comprises a variety of techniques and approaches aimed at identifying useful data captured within the digital images, enhancing the images themselves, or making them ready for additional stages of processing and application [2]. Image processing is used in various disciplines such as in analyzing digital photography, medical image processing, computer vision, digital encryption, and decryption [3]. MRI image processing challenges comprise computational efficiency issues and operational complexity. Traditional algorithms may have high requirements, limiting their use in real-time or resource-constrained settings. When it comes to digital data processing and collection, FPGAs are quite helpful, especially for real-time applications. They are widely used for processing data in parallel at fast speeds, including processing images [4, 5]. Utilizing a high-level programming language for FPGA-based image processing applications often demands the creation of thousands of lines of code, which can be inefficient and time-consuming. To address these challenges, this paper realized various algorithms for MRI image processing, including compression, edge detection, and fusion. These algorithms are using IIR filters and implemented on an FPGA kit utilizing the highly effective Xilinx System Generator tool, this tool featuring a user-friendly graphical interface integrated with Matlab and Simulink-based blocks. This decision streamlines the development workflow, distinguishing it from other hardware description tools. The selected approach reduces complexity in the development process, making it stand out from the typical hardware description software options.

The paper explores the following research questions:

1. What are the most effective methods for performing real-time processing of MRI images on FPGA hardware?
2. Parallel processing on FPGA can improve speed and efficiency of real-time MRI analysis using Xilinx System Generator?

The manuscript examines potential hypotheses to investigate, including:

1. Using IIR filters on FPGA kit for real-time MRI image processing can dramatically enhance the speed and efficiency of the system compared to traditional processing algorithms.
2. Employing IIR filters for real-time MRI image compression achieved higher compression rates and better quality of the compressed images compared to traditional methods.
3. FPGA-based IIR filters for real-time MRI processing reduce power and resources, enabling more efficient and cost-effective imaging systems.

The Key Contributions of this manuscript are:

(1) Using IIR filter in MRI image processing algorithms is a critical component for improving the performance and efficiency of these imaging algorithms.

(2) Implementing MRI image processing algorithms on FPGA using XSG is a highly effective and appealing approach for real-time MRI image processing.

(3) The paper compares existing and proposed MRI processing techniques and finds the proposed algorithms outperform existing methods in compression, edge detection, and fusion.

This is the format for the remainder of the paper. The suggested image processing algorithms are shown in Section 3. Section 4 provides the implementation results utilizing a Xilinx FPGA kit. Section 5 includes a comparison of the suggested algorithms with alternative methods. Finally, Section 6 concludes this paper.

In this section, three MRI image processing techniques are presented. The performance of each algorithm is evaluated using a predefined set of performance measures, which demonstrate an excellent closeness to typical algorithms.

The MRI image processing algorithm's suggested architecture, which employs Simulink and Xilinx blocks, is segmented into three stages, as depicted in Figure 1.

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Figure 1. Block diagram of proposed MRI image processing algorithms

All necessary hardware methods are implemented between MRI image pre-processing and MRI image post-processing. Simulink is utilized to implement the MRI Image Pre-Processing and MRI Image Post-Processing blocks. The basic MRI image processing algorithms namely Compression, Edge detection, and Fusion are implemented using Xilinx blocks and then implemented on Artix 7 (XC7A100t-1CSG324) FPGA kit. The test dataset consists of five grayscale MRI pictures obtained from the study [15], each with a size of 256×256 pixels. These images are displayed in Figure 2.

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Figure 2. Test MRI images

3.1 Algorithm for MRI image compression

The issue of minimizing the quantity of data needed to portray an MRI picture is addressed by MRI image compression. The proposed compression algorithm is shown in Figure 3, which utilized an IIR filter for its realization, the IIR filter coefficients are selected as in the study [16] for getting 7th low-pass IIR filter, the transfer function for the 7th low pass IIR is defined as in Eq. (1). The compression and reconstruction of brain MRI images in Figure 2(a) using the proposed compression algorithm is shown in Figure 4.

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Figure 3. Model simulation for MRI image compression algorithm

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Figure 4. Compression and reconstruction of test image brain MRI

Throughout this technique are given for four compression performance metrics are represented by numbers. These measures are SNR, PSNR, and Cor [17, 18] are taken between the original image and reconstructed image and, CR between the original image and compressed image [19]. Figure 5 and Table 2 illustrate the performance of the compression algorithm on the test MRI images.

$\begin{gathered}H(z)={\left[\frac{0.00605+0.11 z^{-2}+0.5 z^{-3}+0.5 z^{-4}+0.11 z^{-5}+0.00605 z^{-7}}{1+0.22 z^{-2}+0.121 z^{-4}}\right]}\end{gathered}$           (1)

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Figure 5. Performance of compression algorithm on the test MRI images

Table 2. Compression algorithm performance on test MRI

3.2 Algorithm for MRI image edge detection

Edges are points of sharp change in an image. where there is a rapid transition in pixel intensity, signifying a distinct change in luminance. Edges encapsulate a significant portion of an image's shape-related information and play a crucial role in highlighting object characteristics within the image [20]. Edge detection stands as one of the most frequently employed techniques in image analysis, with a plethora of algorithms documented in the literature dedicated to enhancing and identifying these edges. Edge detection involves a straightforward process of filtering and masking using an appropriate filter mask. For MRI image edge detection, the input MRI image is convolved with the 7th IIR High-pass filter as outlined in the study [16], the transfer function for the 7th High-pass IIR is defined as in Eq. (2). utilizing Xilinx block sets for processing. This method is illustrated in Figure 6. Then the image will become sharper by enhancing the areas of the image that have edges. Figure 7 presents the results of MRI image edge detection.

$\begin{gathered}H(z)=  {\left[\frac{0.00605+0.11 z^{-2}-0.5 z^{-3}+0.5 z^{-4}-0.11 z^{-5}-0.00605 z^{-7}}{1+0.22 z^{-2}-0.121 z^{-4}}\right]}\end{gathered}$              (2)

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Figure 6. Model simulation for MRI image edge detection algorithm

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Figure 7. MRI image edge detection algorithm results

3.3 Algorithm for MRI image fusion

A technique known as image fusion turns two or more images from the same scene into a single image. Compared to all of the input images, the fused image that is produced will be more informative [21]. It is widely used in many application domains, such as medical imaging, photography, and surveillance [22]. There are two categories for image fusion techniques: spatial and frequency domains. The spatial-based approach is a basic image fusion technique made up of Max-Min, maximum, minimum, averaging, and principal component analysis (PCA) [22].

This paper achieved MRI Image Fusion utilizing the Simple Average technique, which is computationally efficient, requiring minimal processing time and resources, this approach is useful where real-time image fusion is required or when dealing with large datasets, it combines images by averaging the pixels, as in Eq. (3) [22, 23].

$w(i, j)=\frac{X(i, j)+Y(i, j)}{2}$           (3)

W(i,j) is a fused image and X(i,j), Y(i,j) are input image. Figure 8 shows the fusion algorithm, and the simulation results are shown in Figure 9.

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Figure 8. Model simulation for MRI image fusion algorithm

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Figure 9. Results for MRI image fusion algorithm

The metrics used for measuring the performance MRI image fusion algorithm, are mean, information entropy (IE), and standard deviation (SD) [24, 25]. The Fusion algorithm's performance on the test MRI images is displayed in Table 3. In Figure 10, the Fusion algorithm's performance was examined and plotted.

Table 3. Performance of fusion algorithm on the test MRI images

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Figure 10. Performance of fusion algorithm on the test MRI images

To demonstrate the effectiveness of the suggested algorithms, comparisons from the performance point of view are made between the proposed algorithms and other algorithms in the studies [6, 25] is shown in Table 5.

Table 5. Performance comparison of proposed algorithms and referenced algorithms

Based on Table 5, it is notable that the proposed compression algorithm provides superior signal quality in terms of PSNR, SNR, and Corr. values. Additionally, it exhibits efficient subjective quality. Furthermore, the fusion algorithm demonstrates improved signal quality in Mean, IE, and SD values. The below list shows the benefits of enchantment values of image quality metrics:

• Higher compression ratios reduced storage requirements without significant loss of important diagnostic information.
• Efficient compression ratios enable faster transmission of MRI images over networks or through communication channels.
• Improved SNR contributes to clearer visualization of anatomical structures, lesions, and other diagnostic features, assisting radiologists and clinicians in making accurate assessments.

Additionally, the results from Matlab simulations and hardware implementations were highly similar, with minor differences deemed negligible for the study. Key metrics like SNR, PSNR, and compression ratio showed less than 1% variation between software and hardware, attributed to rounding errors and implementation differences.