Fast local Laplacian filtering based enhanced medical image fusion using parameter-adaptive PCNN and local features-based fuzzy weighted matrices

Ullah, Hikmat; Zhao, Yaqin; Abdalla, Fakheraldin Y. O.; Wu, Longwen

doi:10.1007/s10489-021-02834-0

Cited by 15 publications

(5 citation statements)

References 68 publications

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“…This article primarily focuses on the contrast enhancement techniques applied to coronary angiography images, with an emphasis on improving their quality by addressing issues such as low contrast. This study explores various techniques such as the Retinex Algorithm [30,31], Contrast Stretching (CS) [32], Gamma Correction (GC) [33], Histogram equalization (HE) [34], Local Bright Contrast (LBC) [35], Local Transformation Histogram Equalization (LTHE), Optimized maximum contrast (OMC) [36], Piecewise Linear Transformation (PLT), Sigmoid, Adaptive Histogram Equalization (AHE) [37], Bi-Histogram Equalization (BHE), Brightness Bi-Histogram Equalization (BBHE) [38], Contrast Limited Adaptive Histogram Equalization (CLAHE) [37], Dualistic Sub Image Histogram Equalization (DSIHE) [39], Logarithmic Transform (LT), Multi Histogram Equalization (MHE), Multi-Scale Retinex with Color Restoration (MSRCR) [40], Global Transformation Histogram Equalization (GTHE) [41], and Fast Local Laplacian Filter (FLLF) [10], and assesses their efficacy in enhancing the images before further analysis or diagnosis. This section of the article discusses the experiments conducted to evaluate the performance of noise removal and low contrast identification.…”

Section: Resultsmentioning

confidence: 99%

“…The Fast Local Laplacian Filter (FLLF) technique is an advanced approach for enhancing the contrast of images while preserving their fine details and textures. Unlike traditional global contrast enhancement methods that can lead to oversaturation or loss of subtle details, the FLLF technique operates locally, adapting to the unique characteristics of different image regions [10], as shown in Figure 1. of subtle details, the FLLF technique operates locally, adapting to the unique characteristics of different image regions [10], as shown in Figure 1.…”

Section: Fast Local Laplacian Filtermentioning

confidence: 99%

“…Unlike traditional global contrast enhancement methods that can lead to oversaturation or loss of subtle details, the FLLF technique operates locally, adapting to the unique characteristics of different image regions [10], as shown in Figure 1. of subtle details, the FLLF technique operates locally, adapting to the unique characteristics of different image regions [10], as shown in Figure 1. At the core of the FLLF technique lies the Laplacian pyramid.…”

Section: Fast Local Laplacian Filtermentioning

confidence: 99%

“…The central objective of this study is to explore the effectiveness of Fast Local Laplacian Filter techniques in enhancing blood vessel contrast within coronary angiography images [10,11]. In the FLLF technique, a Laplacian pyramid is created to capture local image variations and details at different scales.…”

Section: Introductionmentioning

confidence: 99%

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Fast Local Laplacian Filter Based on Modified Laplacian through Bilateral Filter for Coronary Angiography Medical Imaging Enhancement

Khan,

Alharbi

2023

Algorithms

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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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Section: Resultsmentioning

confidence: 99%

Section: Fast Local Laplacian Filtermentioning

confidence: 99%

Section: Fast Local Laplacian Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fast Local Laplacian Filter Based on Modified Laplacian through Bilateral Filter for Coronary Angiography Medical Imaging Enhancement

Khan,

Alharbi

2023

Algorithms

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“…The Fast Local Laplacian Filter (FLLF) was employed by Ullah et al (2022) to enhance the quality of the source image in the image fusion method. For the fusion of medical images, (Zhang et al, 2023b) provide a novel joint sparse model with coupled dictionary learning.…”

Section: Introductionmentioning

confidence: 99%

A Robust Medical Image Fusion Based on Synthetic Focusing Degree Criterion and Special Kernel Set for Clinical Diagnosis

Lepcha,

Goyal,

Dogra

et al. 2024

Journal of Computer Science

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Medical imaging has been widely used to diagnose diseases over the last two decades. Medical professionals still struggle to diagnose diseases using a single modality since there is a shortage of data in this domain. Therefore, images of specific organs with diseases from a variety of medical imaging systems can be combined using a technique called image fusion. Medical image fusion has prompted immense requisite applications in clinical applications in recent years. However, the fusion of medical images still facing a variety of challenges due to the input image quality. Protonema such as noise and low-contrast input medical images significantly reduces the quality of medical images. Still, recent image fusion methods are not significantly able to address the image quality problems. In order to address these problems, this study introduces a novel image fusion method that provides effective fusion performance even if the input images are noisy or low-contrast by combining the benefits of synthetic focusing degree criterion with a special kernel set. First, a Gaussian Curvature Filter (GCF) is used to sharpen the images in order to perform a Salient Feature Extraction (SFE). Then, we create a synthetic Focusing Degree Condition (FDC) that combines the Spatial Frequency (SF) and the Local Variance (LV) of the images to get the coarse fusion maps. The course fusion maps are then processed using median and morphological filters. The weighted fusion technology is used to generate the fused image. Finally, image enhancement is achieved by adding a special kernel to the fused image to obtain the final fusion result. Experimental results on the publicly available datasets exhibited that the proposed research article obtains the best results in terms of noisy and low-contrast medical images. Overall, it achieves significant performance both qualitatively and quantitively when compared to other competing state-of-the-art methods.

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Alzheimer’s disease classification using distilled multi-residual network

et al. 2022

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Fast local Laplacian filtering based enhanced medical image fusion using parameter-adaptive PCNN and local features-based fuzzy weighted matrices

Cited by 15 publications

References 68 publications

Fast Local Laplacian Filter Based on Modified Laplacian through Bilateral Filter for Coronary Angiography Medical Imaging Enhancement

Fast Local Laplacian Filter Based on Modified Laplacian through Bilateral Filter for Coronary Angiography Medical Imaging Enhancement

A Robust Medical Image Fusion Based on Synthetic Focusing Degree Criterion and Special Kernel Set for Clinical Diagnosis

Alzheimer’s disease classification using distilled multi-residual network

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