Medical Imaging and Image Processing

Zhang, Yudong; Dong, Zhengchao

doi:10.3390/technologies11020054

Cited by 9 publications

(4 citation statements)

References 31 publications

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“…The use of ML has become integral to our daily lives. Several domains, such as transport and manufacturing have already proven their value, but now they are gaining popularity thanks to their excellent results in healthcare fields, like lung disease classification [6,7], body organ recognition by medical images [8], lung nodule detection [9], reconstruction of medical images [10], and segmentation of brain tumors [11,12]. To examine patients, the use of intelligent systems that are based on ML is increasing [13].…”

Section: Introductionmentioning

confidence: 99%

The Role of Machine Learning in Managing and Organizing Healthcare Records

Alghamdi,

Al-Khasawneh,

Alarood

et al. 2024

Eng. Technol. Appl. Sci. Res.

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With the exponential growth of medical data, Machine Learning (ML) algorithms are becoming increasingly important to the management and organization of healthcare information. This study aims to explore the role that ML can play in optimizing the management and organization of healthcare records, by identifying the challenges, advantages, and limitations associated with this technology. Consequently, the current study will contribute to the understanding of how ML might be applied to the healthcare industry in a variety of circumstances. Using the findings of this study, healthcare professionals, researchers, and policymakers will be able to make informed decisions regarding the adoption and implementation of ML techniques for regulating healthcare records. The findings of this paper revealed that ML can play an important role in efficiently directing and classifying healthcare records using different perspectives.

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Section: Introductionmentioning

confidence: 99%

The Role of Machine Learning in Managing and Organizing Healthcare Records

Alghamdi,

Al-Khasawneh,

Alarood

et al. 2024

Eng. Technol. Appl. Sci. Res.

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“…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%

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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“…MIA uses computer algorithms and machine learning techniques to analyze and interpret medical images [ 10 ]. MIP roughly contains eight common tasks, as shown in Figure 1 .…”

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confidence: 99%