A new X-ray images enhancement method using a class of fractional differential equation

Aldoury, Rasha Saad; Al-Saidi, Nadia M. G.; Ibrahim, Rabha W.; Kahtan, Hasan

doi:10.1016/j.mex.2023.102264

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…Unlike conventional IQA methods 82 – 84 , which hinge on comparing images against high-quality references, BRISQUE’s significance shines in real-world scenarios where reference images are notably absent, rendering methods like BRISQUE indispensable. In the domain of medical imaging, BRISQUE's potential in image quality assessment has been investigated across diverse medical imaging modalities, including MRI 85 , 86 , lung CT 87 scans, and chest X-ray images 88 . This application proves invaluable as it empowers healthcare professionals to ensure that images employed for diagnosis attain requisite quality thresholds, thereby enhancing the reliability of medical assessments.…”

Section: Resultsmentioning

confidence: 99%

Effective processing pipeline PACE 2.0 for enhancing chest x-ray contrast and diagnostic interpretability

Siracusano,

La Corte,

Nucera

et al. 2023

Sci Rep

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Preprocessing is an essential task for the correct analysis of digital medical images. In particular, X-ray imaging might contain artifacts, low contrast, diffractions or intensity inhomogeneities. Recently, we have developed a procedure named PACE that is able to improve chest X-ray (CXR) images including the enforcement of clinical evaluation of pneumonia originated by COVID-19. At the clinical benchmark state of this tool, there have been found some peculiar conditions causing a reduction of details over large bright regions (as in ground-glass opacities and in pleural effusions in bedridden patients) and resulting in oversaturated areas. Here, we have significantly improved the overall performance of the original approach including the results in those specific cases by developing PACE2.0. It combines 2D image decomposition, non-local means denoising, gamma correction, and recursive algorithms to improve image quality. The tool has been evaluated using three metrics: contrast improvement index, information entropy, and effective measure of enhancement, resulting in an average increase of 35% in CII, 7.5% in ENT, 95.6% in EME and 13% in BRISQUE against original radiographies. Additionally, the enhanced images were fed to a pre-trained DenseNet-121 model for transfer learning, resulting in an increase in classification accuracy from 80 to 94% and recall from 89 to 97%, respectively. These improvements led to a potential enhancement of the interpretability of lesion detection in CXRs. PACE2.0 has the potential to become a valuable tool for clinical decision support and could help healthcare professionals detect pneumonia more accurately.

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Section: Resultsmentioning

confidence: 99%

Effective processing pipeline PACE 2.0 for enhancing chest x-ray contrast and diagnostic interpretability

Siracusano,

La Corte,

Nucera

et al. 2023

Sci Rep

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“…Furthermore, foundational knowledge of digital image processing has underpinned many traditional segmentation methods [44]. However, it is crucial to acknowledge that these traditional techniques often face difficulties when handling noisy or non-uniformly illuminated images [45,46]. This recognition has driven the exploration for more advanced and adaptable approaches.…”

Section: Traditional Segmentation Methodsmentioning

confidence: 99%

Advanced Medical Image Segmentation Enhancement: A Particle-Swarm-Optimization-Based Histogram Equalization Approach

Saifullah,

Dreżewski

2024

Applied Sciences

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Accurate medical image segmentation is paramount for precise diagnosis and treatment in modern healthcare. This research presents a comprehensive study of the efficacy of particle swarm optimization (PSO) combined with histogram equalization (HE) preprocessing for medical image segmentation, focusing on lung CT scan and chest X-ray datasets. Best-cost values reveal the PSO algorithm’s performance, with HE preprocessing demonstrating significant stabilization and enhanced convergence, particularly for complex lung CT scan images. Evaluation metrics, including accuracy, precision, recall, F1-score/Dice, specificity, and Jaccard, show substantial improvements with HE preprocessing, emphasizing its impact on segmentation accuracy. Comparative analyses against alternative methods, such as Otsu, Watershed, and K-means, confirm the competitiveness of the PSO-HE approach, especially for chest X-ray images. The study also underscores the positive influence of preprocessing on image clarity and precision. These findings highlight the promise of the PSO-HE approach for advancing the accuracy and reliability of medical image segmentation and pave the way for further research and method integration to enhance this critical healthcare application.

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An Improved Object Detection Algorithm Based on the Hessian Matrix and Conformable Derivative

Lavín-Delgado,

Solís-Pérez,

Gómez-Aguilar

et al. 2024

Circuits Syst Signal Process

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A new X-ray images enhancement method using a class of fractional differential equation

Cited by 4 publications

References 13 publications

Effective processing pipeline PACE 2.0 for enhancing chest x-ray contrast and diagnostic interpretability

Effective processing pipeline PACE 2.0 for enhancing chest x-ray contrast and diagnostic interpretability

Advanced Medical Image Segmentation Enhancement: A Particle-Swarm-Optimization-Based Histogram Equalization Approach

An Improved Object Detection Algorithm Based on the Hessian Matrix and Conformable Derivative

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