Early Diagnosis and Treatment of Coronary Heart Disease with Image Features of Optical Coherence Tomography under Adaptive Segmentation Algorithm

This research was aimed at exploring the application value of optical coherence tomography (OCT) images under adaptive segmentation algorithm in the early diagnosis of coronary heart disease (CHD). Eighty-two patients with CHD were included, who were to undergo coronary angiography (CAG) to confirm their condition. According to the diagnostic criteria of CHD in the American Coronary Artery Surgery Study (CASS), the patients were divided into the stable plaque group (41 cases) and unstable plaque group (41 cases). Besides, 20 healthy volunteers were selected as the control group, and all of them underwent OCT scans. On the basis of a fourth-order partial differential equation (PDE) and active contour (AC) model, a novel adaptive image segmentation algorithm PDE-AC was constructed and used for OCT image processing. No significant difference was found in general clinical data and serological indicators in the control group compared to the other two groups ( $P>0.05$ ). The lipid plaque length, degree of stenosis, and lipid pool angle, macrophages and intimal erosion, and plaque fissure in the unstable plaque group were highly greater than those in the stable plaque group. The fibrous cap thickness (FCT) was significantly thinner than that in the stable plaque group ( $P<0.05$ ). The diagnostic sensitivity, specificity, and accuracy of OCT under PDE-AC algorithm for CHD (91.53%, 84.08%, and 95.38%) were markedly higher than those of single OCT (83.46%, 75.11%, and 88.02%) ( $P<0.05$ ). In summary, OCT images under PDE-AC algorithm did better than simple OCT images in the diagnosis of CHD. Lipid plaque length, degree of stenosis, and lipid pool angle, macrophage and intimal erosion, plaque fissure, and FCT were important indicators for judging plaque stability, having the better clinical application value.