Retrieval of Brain Tumors by Adaptive Spatial Pooling and Fisher Vector Representation

• Published in: PloS one Vol. 11; no. 6; p. e0157112
• Main Authors: Cheng, Jun, Yang, Wei, Huang, Meiyan, Huang, Wei, Jiang, Jun, Zhou, Yujia, Yang, Ru, Zhao, Jie, Feng, Yanqiu, Feng, Qianjin, Chen, Wufan
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 06.06.2016; Public Library of Science (PLoS)
• Subjects: Algorithms; Artificial Intelligence; Biology and Life Sciences; Biomedical engineering; Brain; Brain cancer; Brain Neoplasms - diagnosis; Brain Neoplasms - pathology; Brain research; Brain tumors; Care and treatment; Classification; Clustering; Data bases; Databases, Factual; Diagnosis; Discriminant analysis; Engineering; Feature extraction; Glioma - diagnosis; Glioma - pathology; Humans; Image contrast; Image enhancement; Image Interpretation, Computer-Assisted - methods; Image management; Image retrieval; Information Storage and Retrieval - methods; Machine learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical databases; Medicine and Health Sciences; Meningeal Neoplasms - diagnosis; Meningeal Neoplasms - pathology; Meningioma - diagnosis; Meningioma - pathology; Models, Theoretical; Neuroimaging; Pattern recognition; Pattern Recognition, Automated - methods; People and Places; Physical Sciences; Pituitary; Pituitary gland; Pituitary Neoplasms - diagnosis; Pituitary Neoplasms - pathology; Representations; Research and Analysis Methods; Retrieval; Subtraction Technique; Tumors; Wavelet transforms; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0157112

Content-based image retrieval (CBIR) techniques have currently gained increasing popularity in the medical field because they can use numerous and valuable archived images to support clinical decisions. In this paper, we concentrate on developing a CBIR system for retrieving brain tumors in T1-weighted contrast-enhanced MRI images. Specifically, when the user roughly outlines the tumor region of a query image, brain tumor images in the database of the same pathological type are expected to be returned. We propose a novel feature extraction framework to improve the retrieval performance. The proposed framework consists of three steps. First, we augment the tumor region and use the augmented tumor region as the region of interest to incorporate informative contextual information. Second, the augmented tumor region is split into subregions by an adaptive spatial division method based on intensity orders; within each subregion, we extract raw image patches as local features. Third, we apply the Fisher kernel framework to aggregate the local features of each subregion into a respective single vector representation and concatenate these per-subregion vector representations to obtain an image-level signature. After feature extraction, a closed-form metric learning algorithm is applied to measure the similarity between the query image and database images. Extensive experiments are conducted on a large dataset of 3604 images with three types of brain tumors, namely, meningiomas, gliomas, and pituitary tumors. The mean average precision can reach 94.68%. Experimental results demonstrate the power of the proposed algorithm against some related state-of-the-art methods on the same dataset.