title: Retrieval of brain tumors with region-specific bag-of-visual-words representations in contrast-enhanced MRI images.
abstract: A content-based image retrieval (CBIR) system is proposed for the retrieval of T1-weighted contrast-enhanced MRI (CE-MRI) images of brain tumors. In this CBIR system, spatial information in the bag-of-visual-words model and domain knowledge on the brain tumor images are considered for the representation of brain tumor images. A similarity metric is learned through a distance metric learning algorithm to reduce the gap between the visual features and the semantic concepts in an image. The learned similarity metric is then used to measure the similarity between two images and then retrieve the most similar images in the dataset when a query image is submitted to the CBIR system. The retrieval performance of the proposed method is evaluated on a brain CE-MRI dataset with three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor). The experimental results demonstrate that the mean average precision values of the proposed method range from 90.4% to 91.5% for different views (transverse, coronal, and sagittal) with an average value of 91.0%.
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
authors.forename: Meiyan
authors.initials: M
authors.lastname: Huang
authors.forename: Wei
authors.initials: W
authors.lastname: Yang
authors.forename: Mei
authors.initials: M
authors.lastname: Yu
authors.forename: Zhentai
authors.initials: Z
authors.lastname: Lu
authors.forename: Qianjin
authors.initials: Q
authors.lastname: Feng
authors.forename: Wufan
authors.initials: W
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology; D001932:Brain Neoplasms / Q000175:diagnosis* / Q000473:pathology; D003287:Contrast Media / Q000494:pharmacology*; D005910:Glioma / Q000175:diagnosis* / Q000473:pathology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007091:Image Processing, Computer-Assisted; D007802:Language; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008490:Medical Informatics / Q000379:methods; D008579:Meningioma / Q000175:diagnosis* / Q000473:pathology; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D010911:Pituitary Neoplasms / Q000175:diagnosis* / Q000473:pathology; D015203:Reproducibility of Results; D014785:Vision, Ocular
chemical_list: D003287:Contrast Media