The Correlation Between Texture Features and Fibrous Cap Thickness of Lipid-Rich Atheroma Based on Optical Coherence Tomography Imaging

Fibrous cap thickness (FCT) is seen as critical to plaque vulnerability. Therefore, the development of automatic algorithms for the quantification of FCT is for estimating cardiovascular risk of patients. Intravascular optical coherence tomography (IVOCT) is currently the only in vivo imaging modality with which FCT, the critical component of plaque vulnerability, can be assessed accurately.This study was aimed to discussionthecorrelation between the texture features of OCT images and the FCT in lipid-rich atheroma. Firstly, a full automatic segmentation algorithm based on unsupervised fuzzy c means (FCM) clustering with geometric constrains was developed to segment the ROIs of IVOCT images. Then, 32 features, which are associated with the structural and biochemical changes of tissue, were carried out to describe the properties of ROIs. The FCT in grayscale IVOCT images were manually measured by two independent observers. In order to analysis the correlationbetween IVOCT image features and manual FCT measurements, linear regression approach was performed. Inter-observer agreement of the twice manual FCT measurements was excellent with an intraclass correlation coefficient (ICC) of 0.99.The correlation coefficient between each individual feature set and mean FCT of OCT images were 0.68 for FOS, 0.80 for GLCM, 0.74 for NGTDM, 0.72 for FD, 0.62 for IM and 0.58 for SP. The fusion image features of automatic segmented ROIs and FCT measurements improved the results significantly with a high correlation coefficient (r=0.91, p<0.001). To conclude, the OCT images features demonstrated the perfect performances and could be used for automatic qualitative analysis and the identification of high-risk plaques instead manual FCT measurements.