X-ray image global enhancement algorithm in medical image classification

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Abstract

The current global enhancement algorithm for medical X-ray image has problems of poor de-noising and enhancement effect and low reduction of the enhanced medical X-ray image. To address the problems, a global enhancement algorithm for X-ray image in medical image classification is proposed in this paper. The medical X-ray image is gray scaled, which provides the basis for the further processing of the image. The noise in medical X-ray image is removed by using multi-wavelet transform to improve the enhancement effect of the method. With the curve-let domain the medical X-ray image is enhanced, the reduction degree of medical X-ray image is improved and the global enhancement of the medical X-ray image is completed. Experimental results show that the de-noising effect of the proposed method is effective, the enhanced medical X ray image is better, and the reduction degree of medical X-ray image is high.

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APA

Zhu, W., Jiang, H., Wang, E., Hou, Y., Xian, L., & Debnath, J. (2019). X-ray image global enhancement algorithm in medical image classification. Discrete and Continuous Dynamical Systems - Series S, 12(4–5), 1297–1309. https://doi.org/10.3934/dcdss.2019089

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