Muiqa: Image Quality Assessment Database And Algorithm For Medical Ultrasound Images

Abstract

In the process of medical image acquisition, medical images may be blurred or ghosted due to machine noise, electromagnetic interference, man-made disturbance, etc. This can result in poor image quality and severely affect the diagnosis accuracy and confidence of doctors. IntraVascular UltraSound (IVUS) is an important supplementary method for the diagnosis of coronary angiography. IVUS images can be distorted for many reasons and some severe distortions can affect diagnosis confidence. However, existing manual medical image quality control method is extremely time-consuming and requires a lot of manpower. To solve this problem, we first construct an Medical UltraSound Image Quality Assessment (MUIQA) database, which consists of 10766 IVUS images with quality labels given by professional doctors. Then we propose a deep-learning network to automatically distinguish the low, medium and high level images from each other. We achieve good classification accuracy of 96.34% on the testing set.