Image-Based Shrimp Length Determination using OpenCV

Gamara, Rex Paolo C.; Baldovino, Renann G.; Loresco, Pocholo James M.

doi:10.1109/hnicem54116.2021.9731886

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…To enhance chest X‐ray images, Nefoussi et al 19 developed a variety of image pre‐processing techniques, such as histogram equalization, CLAHE, and Unsharp mask (UM). Gamara et al 20 suggested a hybrid algorithm that combines the CLAHE and Wiener filter (WF) techniques for better X‐ray image enhancement, based on the performance metric results. The suggested technique improves the process of image enhancement and it is useful in disease detection.…”

Section: Introductionmentioning

confidence: 99%

Lung computed tomography image enhancement using U‐Net segmentation

Sheer,

Kareem,

Daway

2024

Int J Imaging Syst Tech

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The goal of image enhancement methods is to improve image's quality. The efficacy of U‐net is evident through its extensive utilization across various significant image modalities, involving computed tomography (CT) scans, magnetic resonance imaging, X‐rays, and microscopy. In this study, we provided a novel and efficient strategy to improve lung CT images based on segmentation using U‐Net architecture. Subsequently, contrast enhancement was performed using adaptive histogram equalization and dark channel prior methods. Finally, the lightness of the lung CT image was enhanced using nonlinear mapping. The contrast enhancement performance of the suggested method is quantified by various measures like the average gradient, mean of the local standard deviation, contrast enhancement measure, and structural similarity index. The performance of the suggested method is compared against other methods, and the results indicate that the suggested method achieves better quality measures of 23.4907, 55.20341, 0.961674, and 0.4143 for the four performance metrics.

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Section: Introductionmentioning

confidence: 99%