Identification of atherosclerotic plaques in intravascular optical coherence tomography images based on textures and artificial neural network

Intravascular optical coherence tomography (IVOCT) is becoming an important choice for in vivo diagnosis of coronary artery diseases. The atherosclerotic disease can be detected from IVOCT images, but the segmentation of IVOCT images and identification of plaques are mainly performed manually. This process is laborious and time consuming and its accuracy relies on the expertise of the observer. To address these limitations, a semi-automated identification algorithm based on texture features is presented in this paper. Regions of interest (ROIs) in IVOCT images are firstly selected, then texture features are calculated to represent this ROI. Finally, an artificial neural network is trained to identify the types of atherosclerotic plague: calcium, lipid tissue and fibrous tissue. The effectiveness of the developed algorithm is evaluated by using hundreds of IVOCT images acquired from 46 patients. It is proven that texture can be regarded as typical features of plagues, whose number has a great influence on the accuracy. In addition, identification can be improved by adding a step of screening the neural network before testing samples.