Segmentation of the lumen and media-adventitial borders in intravascular ultrasound images using a geometric deformable model

Abstract

This study presents a geometric deformable model-based segmentation approach to segmentation of the intima and media-adventitial (MA) borders in sequential intravascular ultrasound (IVUS) images. The initial estimation of the vessel borders was done manually only for the first frame of each sequence. After the border initialisation, pre-processing including edge preservation, noise reduction, and dead zone preservation was successively performed on each IVUS frame. To improve segmentation performance, the image masks were determined preliminarily by local binary pattern-based mask initialisation. Then, the inner and outer borders were approximated using a modified distance regularised level set evolution model. The results showed superior performance of the suggested approach for estimating intima and MA layers from the IVUS images. The corresponding correlation coefficients of area, vessel perimeter, maximum vessel diameter, and maximum lumen diameter were r=0.782, r=0.716, r=0.956, and r=0.874 for the 20MHz images, respectively, and r=0.990, r=0.995, r=0.989, and r=0.996 for the 45MHz images, respectively. In addition, linear regression analysis indicated that the manual segmentation had significantly high similarity at r>0.967 and r>0.993 for 20 and 45MHz images, respectively.