Multiview Locally Linear Embedding for Effective Medical Image Retrieval

• Published in: PloS one Vol. 8; no. 12; p. e82409
• Main Authors: Shen, Hualei, Tao, Dacheng, Ma, Dianfu
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 13.12.2013; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Alignment; Artificial intelligence; Computer science; Decision making; Diagnostic Imaging; Embedding; Engineering; Humans; Image coding; Image management; Image retrieval; Information Storage and Retrieval; Laboratories; Medical imaging; Medical imaging equipment; Optimization; Principal Component Analysis; Principal components analysis; Reduction; Relevance feedback; Retrieval; ROC Curve; Sensitivity and Specificity; Set theory; Ultrasonic imaging; Beijing China; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0082409

Content-based medical image retrieval continues to gain attention for its potential to assist radiological image interpretation and decision making. Many approaches have been proposed to improve the performance of medical image retrieval system, among which visual features such as SIFT, LBP, and intensity histogram play a critical role. Typically, these features are concatenated into a long vector to represent medical images, and thus traditional dimension reduction techniques such as locally linear embedding (LLE), principal component analysis (PCA), or laplacian eigenmaps (LE) can be employed to reduce the "curse of dimensionality". Though these approaches show promising performance for medical image retrieval, the feature-concatenating method ignores the fact that different features have distinct physical meanings. In this paper, we propose a new method called multiview locally linear embedding (MLLE) for medical image retrieval. Following the patch alignment framework, MLLE preserves the geometric structure of the local patch in each feature space according to the LLE criterion. To explore complementary properties among a range of features, MLLE assigns different weights to local patches from different feature spaces. Finally, MLLE employs global coordinate alignment and alternating optimization techniques to learn a smooth low-dimensional embedding from different features. To justify the effectiveness of MLLE for medical image retrieval, we compare it with conventional spectral embedding methods. We conduct experiments on a subset of the IRMA medical image data set. Evaluation results show that MLLE outperforms state-of-the-art dimension reduction methods.