Welcome to the InfoSci Platform
Reference Hub9
Multimodal Information Integration and Fusion for Histology Image Classification

Multimodal Information Integration and Fusion for Histology Image Classification

Tao Meng, Mei-Ling Shyu, Lin Lin
Copyright: © 2011 |Volume: 2 |Issue: 2 |Pages: 17
ISSN: 1947-8534|EISSN: 1947-8542|EISBN13: 9781613508527|DOI: 10.4018/jmdem.2011040104
Cite Article Cite Article

MLA

Meng, Tao, et al. "Multimodal Information Integration and Fusion for Histology Image Classification." IJMDEM vol.2, no.2 2011: pp.54-70. http://doi.org/10.4018/jmdem.2011040104

APA

Meng, T., Shyu, M., & Lin, L. (2011). Multimodal Information Integration and Fusion for Histology Image Classification. International Journal of Multimedia Data Engineering and Management (IJMDEM), 2(2), 54-70. http://doi.org/10.4018/jmdem.2011040104

Chicago

Meng, Tao, Mei-Ling Shyu, and Lin Lin. "Multimodal Information Integration and Fusion for Histology Image Classification," International Journal of Multimedia Data Engineering and Management (IJMDEM) 2, no.2: 54-70. http://doi.org/10.4018/jmdem.2011040104

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Biomedical imaging technology has become an important tool for medical research and clinical practice. A large amount of imaging data is generated and collected every day. Managing and analyzing these data sets require the corresponding development of the computer based algorithms for automatic processing. Histology image classification is one of the important tasks in the bio-image informatics field and has broad applications in phenotype description and disease diagnosis. This study proposes a novel framework of histology image classification. The original images are first divided into several blocks and a set of visual features is extracted for each block. An array of C-RSPM (Collateral Representative Subspace Projection Modeling) models is then built that each model is based on one block from the same location in original images. Finally, the C-Value Enhanced Majority Voting (CEWMV) algorithm is developed to derive the final classification label for each testing image. To evaluate this framework, the authors compare its performance with several well-known classifiers using the benchmark data available from IICBU data repository. The results demonstrate that this framework achieves promising performance and performs significantly better than other classifiers in the comparison.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.