Automatic Identification Of Atherosclerotic Tissue In Intravascular Optical Coherence Tomography Images

Coronary artery disease is the leading cause of death in the world.Especially,the rupture of vulnerable atherosclerotic plaques is believed to account for coronary thrombosis,myocardial ischemia,and sudden cardiac death.Currently,intravascular optical coherence tomography(IVOCT)has drawn the attention of the scientific community because of its high spatial resolution(10-20um).The adventitia,lipid,calcified and fibrous tissues in IVOCT images are significantly different in intensity,homogeneity and border sharpness.Therefore,the quantitative analysis of all these properties can be implemented through image processing or mechine learning.1.An automatic lumen detection approach is performed based on OSTU threshold and active contour model.The method can identify the catheter artifact,the guide wire and the lumen contour.Compared with manual lumen detection,the approach can deal with IVOCT images with high robustness and accuracy.2.An approach is proposed to detact plaque-contour.The plaque-contour is defined as the border between fibrous and other tissues in this thesis,which demonstrates the composition of artery wall and geometric shape of atherosclerotic plaques.The approach is a complete framework for all atherosclerotic tissues without any manual interaction.The good agreement between the automatic and experts' manual results confirms the effectiveness.3.An approach is proposed to identify atherosclerotic tissue based on image processing.The calcium tissue contour is initialized by K-means algorithm and then evolved using an active contour model.The adventita tissue is detected by spectral method due to its layer structural textures.The lipid tissue detection is performed by a curve fitting of black-body radiation formula.The experimental results show that this approach is objective,accurate,and automatic without any manual intervention(measurement differences:calcified tissues 0.03±0.03mm,adventitia tissues 0.04±0.07mm,lipid tissues 0.04 ± 0.05mm).4.An approach is proposed for atherosclerotic tissue identification by machine learning technology.Considering the properties of the different tissue types,texture feature vector is designed for the identification task based on discrete wavelet transformation and gray-level co-occurrence matrix.Then the previous feature vectors are used as the input of Random Forests algorithm.The validation study suggested that the main atherosclerotic tissue components can be automatically identified(overlapping area ratio:calcified tissues 82%,lipid tissues 69%,fibrous tissues 85%).