基于通过双侧滤波器修正的拉普拉斯的快速局部滤波器,用于冠状动脉造影医学成像增强

IF 1.8 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Algorithms Pub Date : 2023-11-21 DOI:10.3390/a16120531
S. Khan, Muzammil Khan, Yasser Alharbi
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引用次数: 0

摘要

对比度增强技术的目的是减少图像噪点,增加相关结构的对比度。在医学影像中,正常组织和异常组织之间的区别可能相当微妙,当噪声水平相对较高时,精确的解读可能会变得具有挑战性。快速局部拉普拉斯滤波器(FLLF)的提出是为了提供更精确的解读,为观察者呈现更清晰的图像;这是通过降低噪声水平来实现的。在这项研究中,FLLF 通过其独特的对比度增强功能强化了图像,同时保留了重要的图像细节。它通过适应图像的特性,有选择性地增强对比度低的区域,从而提高了整体视觉质量。此外,FLLF 在边缘保留方面表现出色,确保了精细细节的保留和边缘的锐利。我们采用了多个性能指标来评估拟议技术的有效性。这些指标包括峰值信噪比(PSNR)、均方误差(MSE)、均方根误差(RMSE)、归一化系数(NC)和相关系数。结果表明,拟议技术的 PSNR 为 40.12,MSE 为 8.6982,RMSE 为 2.9492,NC 为 1.0893,相关系数为 0.9999。分析结果表明,在应用对比度增强技术时,特别是与现有技术相比,所提出的方法具有卓越的性能。这种方法能生成高质量的图像,同时将信息损失降到最低,最终帮助医学专家做出更准确的诊断。
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Fast Local Laplacian Filter Based on Modified Laplacian through Bilateral Filter for Coronary Angiography Medical Imaging Enhancement
Contrast enhancement techniques serve the purpose of diminishing image noise and increasing the contrast of relevant structures. In the context of medical images, where the differentiation between normal and abnormal tissues can be quite subtle, precise interpretation might become challenging when noise levels are relatively elevated. The Fast Local Laplacian Filter (FLLF) is proposed to deliver a more precise interpretation and present a clearer image to the observer; this is achieved through the reduction of noise levels. In this study, the FLLF strengthened images through its unique contrast enhancement capabilities while preserving important image details. It achieved this by adapting to the image’s characteristics and selectively enhancing areas with low contrast, thereby improving the overall visual quality. Additionally, the FLLF excels in edge preservation, ensuring that fine details are retained and that edges remain sharp. Several performance metrics were employed to assess the effectiveness of the proposed technique. These metrics included Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Normalization Coefficient (NC), and Correlation Coefficient. The results indicated that the proposed technique achieved a PSNR of 40.12, an MSE of 8.6982, an RMSE of 2.9492, an NC of 1.0893, and a Correlation Coefficient of 0.9999. The analysis highlights the superior performance of the proposed method when contrast enhancement is applied, especially when compared to existing techniques. This approach results in high-quality images with minimal information loss, ultimately aiding medical experts in making more accurate diagnoses.
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来源期刊
Algorithms
Algorithms Mathematics-Numerical Analysis
CiteScore
4.10
自引率
4.30%
发文量
394
审稿时长
11 weeks
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