自适应调节医学CT序列图像窗宽窗位算法

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

调窗处理能够提高医学CT图像对灰度的识别准确率,默认窗位窗宽(127.5,255)局限性强,人工调窗不仅需要具备丰富的先验知识,而且效率低下无法广泛应用,为此提出一种自适应调窗算法.该算法首先根据序列图像最大和最小灰度值绘制直方图.其次设置相关参数,遍历直方图去除频数小于阈值T 0的部分,再次遍历直方图将相邻两组频数差值小于阈值T 1的部分合并.最后根据直方图计算窗宽窗位.通过对比实验表明,该算法在均方误差、信噪比和峰值信噪比方面都有所提高,能够有效地提高灰度识别准确率.

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 T 0.After traversing the histogram again,parts with a difference between adjacent groups of frequencies less than the threshold T 1 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.