Image quality enhancement in Neutron Computerized Tomography based on projection exposure time adjustment

Soliman, Salwa R.; Zayed, Hala H.; Selim, Mazen M.; Kasban, H.; Mongy, T.

doi:10.1016/j.apradiso.2019.108862

Cited by 7 publications

(1 citation statement)

References 13 publications

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“…The resultant images are adjusted with high and low intensities of the input image. The grayscale image contrast is enhanced in this way, used for further diagnostic procedures [15].…”

Section: Image Adjustmentmentioning

confidence: 99%

Lung Cancer Prediction Using Robust Machine Learning and Image Enhancement Methods on Extracted Gray-Level Co-Occurrence Matrix Features

et al. 2022

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In the present era, cancer is the leading cause of demise in both men and women worldwide, with low survival rates due to inefficient diagnostic techniques. Recently, researchers have been devising methods to improve prediction performance. In medical image processing, image enhancement can further improve prediction performance. This study aimed to improve lung cancer image quality by utilizing and employing various image enhancement methods, such as image adjustment, gamma correction, contrast stretching, thresholding, and histogram equalization methods. We extracted the gray-level co-occurrence matrix (GLCM) features on enhancement images, and applied and optimized vigorous machine learning classification algorithms, such as the decision tree (DT), naïve Bayes, support vector machine (SVM) with Gaussian, radial base function (RBF), and polynomial. Without the image enhancement method, the highest performance was obtained using SVM, polynomial, and RBF, with accuracy of (99.89%). The image enhancement methods, such as image adjustment, contrast stretching at threshold (0.02, 0.98), and gamma correction at gamma value of 0.9, improved the prediction performance of our analysis on 945 images provided by the Lung Cancer Alliance MRI dataset, which yielded 100% accuracy and 1.00 of AUC using SVM, RBF, and polynomial kernels. The results revealed that the proposed methodology can be very helpful to improve the lung cancer prediction for further diagnosis and prognosis by expert radiologists to decrease the mortality rate.

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“…The resultant images are adjusted with high and low intensities of the input image. The grayscale image contrast is enhanced in this way, used for further diagnostic procedures [15].…”

Section: Image Adjustmentmentioning

confidence: 99%