Observation of the Clinical Correlation between Carotid Artery Intima-Media Thickness and Cardiovascular Risk Factors in Hypertensive Patients under Microscope Images

With the development of social science and technology and people’s demand for exploring the micro world, digital microscope technology came into being. Microscope image processing technology has been widely used in industrial micromanipulation, biomedicine, and cultural relic identification and preservation. In order to in-depth study the use of microscope image technology to observe the thickness of the carotid artery intima-media in hypertensive patients to find out whether it is feasible to explore many risk factors for cardiovascular development, this article uses data analysis, concept analysis, and graphic construction methods to collect samples, analyzed the image, streamlined the algorithm, and created a microscope image that can study its risk factors. The severity of hypertension, diabetes, and other factors affects the thickness of carotid arteries and cardiovascular diseases. The results show that the longer the duration of hypertension, the higher the central and peripheral blood pressure, the higher the height, and the higher the central blood pressure, which is relatively low. The detection rate of elevated central blood pressure is higher (76.8%). The second group (53.6%) and the third group (49.2%) ( P < 0.008 ); older age, the central blood pressure is relatively high, and the detection rate of central blood pressure increases (75.5%). It is higher than normal blood pressure (24.5%), so it can be concluded that hypertension not only affects central blood pressure but also peripheral blood; height and age are independent factors that affect central blood pressure. It is basically realized that starting from the microscope image, many risk factors of cardiovascular disease have been successfully analyzed.

Application of Image Analysis Method Combined with Microhardness Measurement to Determine Phase Fractions

We have determined different phase fractions from microscopy images using semi-automated image analysis fitting technique, and in addition we have classified each phase according to its hardness. The distribution of grayscale pixels of different phases is first characterised separately for each phase, which are sampled from the microscope image. After this the distributions of the separate phases are fitted to give the corresponding distribution of the whole image. The microhardness measurement provides reliability on the classification of the different phases to ferrite, bainite or martensite. In addition to describing the applied techniques in detail, we present the results obtained from the analysis for one steel subjected to isothermal holding experiments at different temperatures.