Hybrid Denoising Algorithm for Medical CT Sequence Images

doi:10.12068/j.issn.1005-3026.2024.04.002

Abstract

Abstract:

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.

Key words: medical CT sequence images, structural similarity, hybrid denoising algorithm, histogram, window adjustment

CLC Number:

TP 391.41

Jin-lin CHEN, Pei-xin YUAN. Hybrid Denoising Algorithm for Medical CT Sequence Images[J]. Journal of Northeastern University(Natural Science), 2024, 45(4): 464-473.

Figures/Tables 19

References 22

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评价标准	本文算法	维纳滤波	K-SVD	NLM	BM3D	中值滤波	均值滤波	高斯滤波	双边滤波	各向异性滤波
MSE	19.661 7	104.491 8	106.853 1	104.768 4	103.511 6	104.627 9	104.655 4	104.876 5	103.347 5	104.231 8
PSNR/dB	35.685 9	27.974 3	27.871 4	27.961 2	28.017 8	27.968 1	27.966 0	27.956 2	28.025 4	27.985 6
sim	0.777 4	0.574 7	0.660 0	0.594 5	0.554 0	0.581 2	0.597 0	0.605 9	0.547 4	0.575 3

评价标准	本文算法	维纳滤波	K-SVD	NLM	BM3D	中值滤波	均值滤波	高斯滤波	双边滤波	各向异性滤波
MSE	19.661 7	104.491 8	106.853 1	104.768 4	103.511 6	104.627 9	104.655 4	104.876 5	103.347 5	104.231 8
PSNR/dB	35.685 9	27.974 3	27.871 4	27.961 2	28.017 8	27.968 1	27.966 0	27.956 2	28.025 4	27.985 6
sim	0.777 4	0.574 7	0.660 0	0.594 5	0.554 0	0.581 2	0.597 0	0.605 9	0.547 4	0.575 3

评价标准	本文算法	维纳滤波	K-SVD	NLM	BM3D	中值滤波	均值滤波	高斯滤波	双边滤波	各向异性滤波
MSE	19.661 7	27.723 3	25.165 5	27.733 0	28.008 0	27.664 8	27.801 6	27.650 3	28.260 2	27.898 7
PSNR/dB	35.685 9	33.983 6	34.520 5	33.994 4	33.928 9	33.997 2	33.991 3	34.027 9	33.887 1	33.960 0
sim	0.777 4	0.749 6	0.812 8	0.769 5	0.743 0	0.756 1	0.771 8	0.780 4	0.724 4	0.753 6

评价标准	本文算法	维纳滤波	K-SVD	NLM	BM3D	中值滤波	均值滤波	高斯滤波	双边滤波	各向异性滤波
MSE	19.661 7	27.723 3	25.165 5	27.733 0	28.008 0	27.664 8	27.801 6	27.650 3	28.260 2	27.898 7
PSNR/dB	35.685 9	33.983 6	34.520 5	33.994 4	33.928 9	33.997 2	33.991 3	34.027 9	33.887 1	33.960 0
sim	0.777 4	0.749 6	0.812 8	0.769 5	0.743 0	0.756 1	0.771 8	0.780 4	0.724 4	0.753 6

部位	MSE
部位	本文算法	维纳滤波	K-SVD	NLM	BM3D	中值滤波	均值滤波	高斯滤波	双边滤波	各向异性滤波
骨架	31.436 0	57.095 9	55.454 0	56.807 2	56.748 1	57.113 0	54.555 6	54.158 4	56.710 8	56.838 4
膝盖	21.783 0	30.068 1	29.906 4	30.030 5	30.015 7	30.056 7	29.891 7	29.891 3	30.010 3	30.054 7
头部女	13.113 2	28.145 4	27.435 6	27.973 5	28.029 4	28.090 8	27.602 4	27.549 7	28.018 2	28.074 8
头部男	21.928 2	38.183 4	37.271 5	38.025 6	38.049 1	38.142 8	37.647 3	37.591 1	38.036 0	38.106 9
髋部女	55.292 5	74.290 9	73.923 5	74.235 7	74.213 6	74.270 9	74.089 4	74.077 8	74.207 0	74.271 1
髋部男	44.590 3	80.712 8	80.360 5	80.668 9	80.694 5	80.697 4	80.551 9	80.508 5	80.693 5	80.699 7
骨盆男	57.087 6	94.132 4	93.540 0	94.002 8	94.105 0	94.075 4	93.689 3	93.658 9	94.102 9	94.123 1
踝关节	24.592 6	29.602 8	29.259 8	29.549 5	29.564 5	29.587 7	29.414 7	29.413 5	29.563 1	29.586 5
肩部男	53.722 2	94.426 3	94.047 4	94.270 5	93.863 3	94.439 7	94.171 5	94.203 0	93.841 9	94.234 5