title: Content-based image retrieval using spatial layout information in brain tumor T1-weighted contrast-enhanced MR images.
abstract: 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. When a tumor region is fed to the CBIR system as a query, the system attempts to retrieve tumors of the same pathological category. The bag-of-visual-words (BoVW) model with partition learning is incorporated into the system to extract informative features for representing the image contents. 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 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). 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 distance metric learned by REML significantly outperforms three other existing distance metric learning methods in terms of mAP. 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. These preliminary results demonstrate that the proposed method is effective and feasible for the retrieval of brain tumors in T1-weighted CE-MR Images.
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.
authors.forename: Meiyan
authors.initials: M
authors.lastname: Huang
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Yang
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.
authors.forename: Yao
authors.initials: Y
authors.lastname: Wu
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Jiang
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Gao
authors.affiliation: Laboratory of Image Science and Technology, Southeast University, the Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Chen
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.
authors.forename: Qianjin
authors.initials: Q
authors.lastname: Feng
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.
authors.forename: Wufan
authors.initials: W
authors.lastname: Chen
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.
authors.forename: Zhentai
authors.initials: Z
authors.lastname: Lu
mesh_terms: D000465:Algorithms; D001932:Brain Neoplasms / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007256:Information Systems; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008962:Models, Theoretical; D057386:Search Engine / Q000379:methods*