医学超声影像设备采集信息噪声滤除仿真
|
摘要
对医学超声影像设备采集的信息进行噪声滤除,能够有效提高医学设备信息处理精度。对超声影像设备采集信息噪声的滤除,需要生成医学影像中噪声信息的编码,对超声影像设备噪声信息与背景区域进行分离,完成对采集信息中噪声的滤除。传统方法对噪声信号进行建模,对设备中噪声可调参数进行计算,但忽略了对噪声信息与背景的分离处理,导致噪声滤除效果不理想。提出基于LBP算子与小波系数法结合的医学超声影像设备信息噪声滤除方法。根据基本LBP算子生成医学影像中噪声信息的编码,对噪声信息进行滤除,减少对医学超声影像设备中噪声滤除所用的时间;利用小波系数完成医学超声影像设备中噪声信息与背景区域的分离,准确完成医学超声影像设备中噪声的滤除。实验结果表明,选用方法可以准确地对医学超声影像设备中的噪声信息进行滤除,滤除所用时间少,效率快。
Filtering the information collected by medical ultrasonic imaging equipment can effectively improve the accuracy of medical equipment information processing. To filter the information and noise of ultrasound imaging equipment, we need to generate noise information coding in medical image, separate the noise information and background area of ultrasound image equipment, and complete the filtering of noise in acquisition information. In traditional way, noise signal is modeled, and the noise adjustable parameters in the device are calculated, but the separation of noise information and background is ignored, resulting in the effect of noise filtering is not ideal. An information noise filtering method is proposed based on the combination of LBP operator and wavelet coefficient method for medical ultrasonic imaging equipment. According to the basic information of LBP operator, the noise in medical image was encoded to generate, to remove the noise information, and the use of noise filtering medical ultrasonic imaging equipment in time was reduced. The wavelet coefficients was used to carried out the complete separation of noise and background information of medical ultrasonic imaging equipment, and noise of medical ultrasonic imaging equipment was filtered out accurately and completely. The experimental results show that the method can be used to filter the noise information of medical ultrasonic imaging equipment accurately with less filtering time and higher.efficiency.
Filtering the information collected by medical ultrasonic imaging equipment can effectively improve the accuracy of medical equipment information processing. To filter the information and noise of ultrasound imaging equipment, we need to generate noise information coding in medical image, separate the noise information and background area of ultrasound image equipment, and complete the filtering of noise in acquisition information. In traditional way, noise signal is modeled, and the noise adjustable parameters in the device are calculated, but the separation of noise information and background is ignored, resulting in the effect of noise filtering is not ideal. An information noise filtering method is proposed based on the combination of LBP operator and wavelet coefficient method for medical ultrasonic imaging equipment. According to the basic information of LBP operator, the noise in medical image was encoded to generate, to remove the noise information, and the use of noise filtering medical ultrasonic imaging equipment in time was reduced. The wavelet coefficients was used to carried out the complete separation of noise and background information of medical ultrasonic imaging equipment, and noise of medical ultrasonic imaging equipment was filtered out accurately and completely. The experimental results show that the method can be used to filter the noise information of medical ultrasonic imaging equipment accurately with less filtering time and higher.efficiency.
引文
[1] 朱敏,苏博. 三维人工视觉图像特征滤除仿真研究[J]. 计算机仿真, 2016,33(7): 261-264.
[2] 李军伟,等. 基于颈动脉粥样硬化斑块的多序列MR图像去噪与配准[J]. 中国图象图形学报, 2015,20(7):871-881.
[3] 钟礼山,等. 利用SAR影像时间序列的耕地滤除研究[J]. 地理科学进展, 2015,34(7): 830-839.
[4] 张兵. 高光谱图像处理与信息滤除前沿[J]. 遥感学报, 2016,20(5):1062-1090.
[5] 肖进胜,等. 基于多聚焦图像深度信息滤除的背景虚化显示[J]. 自动化学报, 2015,41(2):304-311.
[6] 沈利强,姜仁荣,王培法. 一种高分辨率遥感图像单木树冠信息滤除方法[J]. 遥感信息, 2017,32(3):142-148.
[7] 马利,等. 一种应用轮廓锐度的单视点图像深度信息滤除算法[J]. 小型微型计算机系统, 2016,37(2):316-320.
[8] 王理政,林祥国,宁晓刚. 面向对象的高分辨率遥感影像阴影滤除方法[J]. 测绘科学, 2017,42(3):108-113.
[9] 符喜优,等. 基于模糊连接度的高分辨率SAR图像道路自动滤除[J]. 计算机应用, 2015,35(2):523-527.
[10] 付晓薇,等. 基于局部熵的量子衍生医学超声图像去斑[J]. 电子与信息学报, 2015,37(3):560-566.
[2] 李军伟,等. 基于颈动脉粥样硬化斑块的多序列MR图像去噪与配准[J]. 中国图象图形学报, 2015,20(7):871-881.
[3] 钟礼山,等. 利用SAR影像时间序列的耕地滤除研究[J]. 地理科学进展, 2015,34(7): 830-839.
[4] 张兵. 高光谱图像处理与信息滤除前沿[J]. 遥感学报, 2016,20(5):1062-1090.
[5] 肖进胜,等. 基于多聚焦图像深度信息滤除的背景虚化显示[J]. 自动化学报, 2015,41(2):304-311.
[6] 沈利强,姜仁荣,王培法. 一种高分辨率遥感图像单木树冠信息滤除方法[J]. 遥感信息, 2017,32(3):142-148.
[7] 马利,等. 一种应用轮廓锐度的单视点图像深度信息滤除算法[J]. 小型微型计算机系统, 2016,37(2):316-320.
[8] 王理政,林祥国,宁晓刚. 面向对象的高分辨率遥感影像阴影滤除方法[J]. 测绘科学, 2017,42(3):108-113.
[9] 符喜优,等. 基于模糊连接度的高分辨率SAR图像道路自动滤除[J]. 计算机应用, 2015,35(2):523-527.
[10] 付晓薇,等. 基于局部熵的量子衍生医学超声图像去斑[J]. 电子与信息学报, 2015,37(3):560-566.