2012BAI14B02), the National Natural Science Foundation of China ( ) under grant (No. 2010CB732505), the National Key Technology Research and Development Program of the Ministry of Science and Technology of China ( ) under grant (No. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.įunding: This work was supported by the National Basic Research Program of China (973 Program) ( ) under grant (No. Received: JanuAccepted: JPublished: July 16, 2014Ĭopyright: © 2014 Huang et al. PLoS ONE 9(7):Įditor: Kewei Chen, Banner Alzheimer's Institute, United States of America (2014) Content-Based Image Retrieval Using Spatial Layout Information in Brain Tumor T1-Weighted Contrast-Enhanced MR Images. These preliminary results demonstrate that the proposed method is effective and feasible for the retrieval of brain tumors in T1-weighted CE-MR Images.Ĭitation: Huang M, Yang W, Wu Y, Jiang J, Gao Y, Chen Y, et al. The mAP of the CBIR system is as high as 91.8% using the proposed method, and the precision can reach 93.1% when the top 10 images are returned by the system. The distance metric learned by REML significantly outperforms three other existing distance metric learning methods in terms of mAP. Using the BoVW model with partition learning, the mean average precision (mAP) of retrieval increases beyond 4.6% with the learned distance metrics compared with the spatial pyramid BoVW method. The effectiveness of the proposed method is evaluated on a brain T1-weighted CE-MR dataset with three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor). Furthermore, a distance metric learning algorithm called the Rank Error-based Metric Learning (REML) is proposed to reduce the semantic gap between low-level visual features and high-level semantic concepts. The bag-of-visual-words (BoVW) model with partition learning is incorporated into the system to extract informative features for representing the image contents. When a tumor region is fed to the CBIR system as a query, the system attempts to retrieve tumors of the same pathological category. This study aims to develop content-based image retrieval (CBIR) system for the retrieval of T1-weighted contrast-enhanced MR (CE-MR) images of brain tumors.
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