This paper presents an efficient and effective approach based on local shape structure classification for detecting media-adventitia border in intravascular ultrasound (IVUS) images.First

the category of local shape structures is found by using

k

-means clustering method.Second

patches from IVUS images indexed by the category are extracted by two kinds of features including integral channel and self-similarities features

and therefore a random decision forest model is constructed.Finally

the key points of testing IVUS images are detecte

d using the trained classification model.Then with the help of curve fitting methods

detection of media-adventitia border is acquired.Experimental results demonstrate that the proposed algorithm effectively relieves the difficulties of interference factors such as plaques

artifacts and side vessel

and more accurately recognizes the key points of target border compared with existing algorithms

detects the whole target border successfully.The Jaccard Measure (JM) of media-adventitia border detected by the algorithm is 88.9%

Percentage of Area Difference (PAD) and Hausdorff Distance (HD) measures are reduced by 19.1% and 9.7% respectively.