Content-based petrieval of 3D medical images

While content-based image retrieval (CBIR) has been researched for more than two decades, retrieving 3D datasets has been progressing considerably slowly, especially in the application to medical domain. This is in part due to the limitation of processing speed when trying to retrieve high-resolution datasets in real-time. Another barrier is that most existing methods have been developed based on 2D images instead of 3D, leaving a gap to be filled. At present, significant amount of exploitations are focusing on the extraction of 3D shapes. However, it appears other information tends to be equally important in clinical decision making. In this paper, Local Binary Pattern (LBP), the texture based approach stemming from 2D forms, has been studied extensively through the application to 3D images from a collection of MR brain images in a content-based image retrieval system (CBIR). The initial results show LBP not only can achieve precision rate of up to 78% but also can perform retrieval in real time with sub-second processing speeds. Comparison with the other three popular texture-based methods, namely 3D Grey Level Co-occurrence Matrices, 3D Wavelet Transforms and 3D Gabor Transforms, is also carried out. The results demonstrate that LBP outperforms over them both in terms of retrieval precision and processing speed