A Comprehensive Survey Analysis for Present Solutions of Medical Image Fusion and Future Directions

Faragallah, Osama S.; El‐Hoseny, Heba M.; El‐Shafai, Walid; El‐Rahman, Wael Abd; El‐Sayed, Hala S.; El-Rabaie, El-Sayed M.; El‐Samie, Fathi E. Abd; Geweid, Gamal G. N.

doi:10.1109/access.2020.3048315

Cited by 83 publications

(39 citation statements)

References 80 publications

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“…The typical value for the PSNR in the lossy image and video compression is between 30 and 50 dB, provided the bit depth is 8 bits, where higher is better. The processing quality of 12-bit images is considered high when the PSNR value is 60 dB or higher [12][13]. For 16-bit data typical values for the PSNR are between 60 and 80 dB [14] Acceptable values for wireless transmission quality loss are considered to be about 20 dB to 25 dB [15].…”

Section: Methodsmentioning

confidence: 99%

Development of an automated Moon observation system using the ALTS-07 Robotic Telescope: 2. Progress report on standard contrast enhancement of Moon crescent image with OpenCV

Muztaba

Malasan

Djamal

2022

J. Phys.: Conf. Ser.

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Vision is a continuous two-dimensional (2-D) image that can be perceived by human visual system. Mathematically, image is a 2-D function that expresses the intensity of light. This research introduces the fundamental ideas of computer vision. We used OpenCV (Open-Source Computer Vision) to solve automatic image processing techniques, especially enhancement of contrast crescent visibility. We described an image using feature vectors to characterize and numerically to quantify the contents of an image. Then, we compared several techniques for increasing image quality, such as Basic contrast, Binary thresholding, Otsu thresholding, Histogram equalization, and Adaptive Histogram equalization. Finally, we calculated the statistical features of the image before and after enhancement process, and also decided to choose the best enhancement technique that will be implemented on a self-constructed cascade classifier in the future.

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Section: Methodsmentioning

confidence: 99%

Development of an automated Moon observation system using the ALTS-07 Robotic Telescope: 2. Progress report on standard contrast enhancement of Moon crescent image with OpenCV

Muztaba

Malasan

Djamal

2022

J. Phys.: Conf. Ser.

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show abstract

“…The auto-tuning algorithm gives as output the mantissa and exponent sizes that should be used to avoid noticeable precision loss. As threshold, we use 0.99 in order to have an approximate image as much similar to the original one [71]. We employ the precision tuning method with the overhead of running the application multiple times to detect the target precision.…”

Section: B Precision Auto-tuningmentioning

confidence: 99%

“…FP<8,10> and FP<8,7> correspond respectively to NVIDIA Tensor and brain floating point. High-resolution images closely similar to the original usually have an SSIM equal to 0.99 [71]. Given this threshold, reduced precision can produce high-quality images.…”

Section: A Gridding Acceleratormentioning

confidence: 99%

Reduced-Precision Acceleration of Radio-Astronomical Imaging on Reconfigurable Hardware

et al. 2022

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Radio telescopes produce large volumes of data that need to be processed to obtain high-resolution sky images. This is a complex task that requires computing systems that provide both high performance and high energy efficiency. Hardware accelerators such as GPUs (Graphics Processing Units) and FPGAs (Field Programmable Gate Arrays) can provide these two features and are thus an appealing option for this application. Most HPC (High-Performance Computing) systems operate in double precision (64-bit) or in single precision (32-bit), and radio-astronomical imaging is no exception. With reduced precision computing, smaller data types (e.g., 16-bit) are used to improve energy efficiency and throughput performance in noise-tolerant applications. We demonstrate that reduced precision can also be used to produce high-quality sky images. To this end, we analyze the gridding component (Image-Domain Gridding) of the widely-used WSClean imaging application. Gridding is typically one of the most time-consuming steps in the imaging process and, therefore, an excellent candidate for acceleration. We identify the minimum required exponent and mantissa bits for a custom floating-point data type. Then, we propose the first custom floating-point accelerator on a Xilinx Alveo U50 FPGA using High-Level Synthesis. Our reduced-precision implementation improves the throughput and energy efficiency of respectively 1.84x and 2.03x compared to the single-precision floating-point baseline on the same FPGA. Our solution is also 2.12x faster and 3.46x more energy-efficient than an Intel i9 9900k CPU (Central Processing Unit) and manages to keep up in throughput with an AMD RX 550 GPU.

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“…For medical image fusion problems, numerous methods have been proposed which can be roughly divided into three levels: pixel-level, feature-level, and decision-level [3].…”

Section: Introductionmentioning

confidence: 99%

A Novel GA-Based Optimized Approach for Regional Multimodal Medical Image Fusion With Superpixel Segmentation

et al. 2021

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For multimodal medical image fusion problems, most of the existing fusion approaches are based on pixel-level. However, the pixel-based fusion method tends to lose local and spatial information as the relationships between pixels are not considered appropriately, which has much influence on the quality of the fusion results. To address this issue, a region-based multimodal medical image fusion framework is proposed based on superpixel segmentation and a post-processing optimization method in this paper. In this framework, the averaging image of the source medical images are firstly obtained by a weighted averaging method. To effectively obtain homogeneous regions and preserve the complete information of image details, the fast linear spectral clustering(LSC) superpixel algorithm is carried out to segment the averaging image and get superpixel labels. For each region of the medical images, log-gabor filter(LGF) and sum modified laplacian(SML) are adopted to extract texture feature and contrast feature for the measurement of region importance. The most important regions are selected and the decision map is generated by comparison. Moreover, to get a more accurate decision map, a new post-processing optimized method based on genetic algorithm(GA) is given. A weighted strategy is applied to the extracted features and the weighting factor can be adaptively adjusted by GA. The effectiveness of the proposed fusion method is validated by conducting experiments on eight pairs of medical images from diverse modalities. In addition, seven other mainstream medical image fusion methods are adopted for comparing the performance of fusion. Experimental results in terms of qualitative and quantitative evaluation demonstrate that the proposed method can achieve stateof-the-art performance for multimodal medical image fusion problems.INDEX TERMS Multimodal medical image fusion, superpixel segmentation, genetic algorithm, log-gabor filter, sum modified laplacian.

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A Comprehensive Survey Analysis for Present Solutions of Medical Image Fusion and Future Directions

Cited by 83 publications

References 80 publications

Development of an automated Moon observation system using the ALTS-07 Robotic Telescope: 2. Progress report on standard contrast enhancement of Moon crescent image with OpenCV

Development of an automated Moon observation system using the ALTS-07 Robotic Telescope: 2. Progress report on standard contrast enhancement of Moon crescent image with OpenCV

Reduced-Precision Acceleration of Radio-Astronomical Imaging on Reconfigurable Hardware

A Novel GA-Based Optimized Approach for Regional Multimodal Medical Image Fusion With Superpixel Segmentation

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