Self-adaptation Adjusting Window Width and Window Level Algorithm for Medical CT Sequence Images

doi:10.12068/j.issn.1005-3026.2023.10.004

Abstract

Abstract: 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.

Key words: medical CT sequence images; self-adaptation; window transformation; histogram; threshold

CLC Number:

CHEN Jin-lin， YUAN Pei-xin， HOU Hao-nan， ZHAO Zhao. Self-adaptation Adjusting Window Width and Window Level Algorithm for Medical CT Sequence Images[J]. Journal of Northeastern University(Natural Science), 2023, 44(10): 1392-1400.

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