摘要
对急性脑梗塞中医辨证分型与西医某些观察、化验指标如血压、血糖、意识障碍、血液流变学、末梢血象等进行了相关性研究 ,重点分析了辨证分型与血液流变学 ,血液流变学与证候轻重 ,血液流变学与血瘀、痰湿证的相关性。结果表明 :中医辨证分型与所观察、化验的指标相关不显著。
Correlation study was made on TCM diagnostic classification and some modern medical criteria such as blood pressure, blood sugar, state of consciousness, blood rheological changes (BRC), and peripheral hemogram of acute cerebral infarction; and the stess of the study was put on the correlation between TCM diagnostic classification and BRC, BRC and the severity of TCM syndromes, BRC and the syndromes of blood stagnation and phlegm dampness. The results showed that there was no marked correlation between TCM diagnostic classification and the modern medical criteria used in the study. KEY WORDS: Cerebral Infarction; TCM Diagnostic Classification; Correlationology of China, Hefei 230027) Abstract DPIV (Digital Particle Imaging Velocimetry) has been attached importance to and applied wisely interiorly and overseas. But at present, its most difficult problem is the precision. Because the tracer particle images of DPIV are sampled by CCD and image digitizer, the noise (mainly the noise of particle image size, the size of interrogation window, local velocity gradients, the number of particles within the sampling window and quantization effects) that is imported inevitably in the process of experiment falls the precision of DPIV experiment. This paper presents the image (simulative and practical image) noise removing in DPIV based on wavelet transform whose characteristic is the multiresolution. This method is compared with image noise removing by Wiener and median filter. The result shows that image noise removing in DPIV based on wavelet transform improves the precision of DPIV experiment, and it is the most precise to rebuild the velocity field based on cross correlation after image noise removing in DPIV using wavelet transform.
出处
《北京中医药大学学报》
CAS
CSCD
北大核心
1998年第3期53-55,共3页
Journal of Beijing University of Traditional Chinese Medicine
基金
"八五"国家科技攻关课题
关键词
脑梗塞
辨证分型
相关性
Wavelet transform, Noise removing, DPIV
