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Copyright of South Architecture / Nanfang Jianzhu is the property of South Architecture Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The medical CT sequence images dopes noise for various reasons. Denoising can effectively improve image quality. The common algorithms are used for single image, while the CT sequence images have high similarity between adjacent images. Therefore, this paper proposes a hybrid denoising algorithm based on the structural similarity. Firstly, a histogram is drawn according to the maximum and minimum gray value. Secondly, relevant threshold parameters are set to calculate the window width and window level, and then conduct window adjustment. Thirdly, the structural similarity of the target image and its adjacent images are calculated. Finally, BM3D and Gaussian filtering algorithms are mixed for three images according to structural similarity. Experimental results show that the algorithm can improve the mean square error, peak signal‑to‑noise ratio and structural similarity, which effectively improves the image quality. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Northeastern University (Natural Science) is the property of Dongbei Daxue Xuebao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Window transformation can improve the accuracy of gray level recognition in medical CT images. The default window(127.5, 255)has strong limitations. Manual window transformation requires rich priori knowledge and is inefficient, making it unsuitable for widespread use. Therefore, a self-adaptation algorithm for window transformation in medical CT sequence images is proposed. Firstly, a histogram is drawn according to the maximum and minimum gray level values of the sequence images. Secondly, relevant parameters are set to traverse the histogram to remove parts with frequencies below the threshold T0. After traversing the histogram again, parts with a difference between adjacent groups of frequencies less than the threshold T1 are merged. Finally, the window width and window level are calculated based on the histogram. Experimental results showed that the algorithm improved the mean square error, signal-to-noise ratio and peak signal-to-noise ratio, which could effectively improve the accuracy of gray level recognition. [ABSTRACT FROM AUTHOR]