title: Bioimage classification with subcategory discriminant transform of high dimensional visual descriptors.
abstract: BACKGROUND
  Bioimage classification is a fundamental problem for many important biological studies that require accurate cell phenotype recognition, subcellular localization, and histopathological classification. In this paper, we present a new bioimage classification method that can be generally applicable to a wide variety of classification problems. We propose to use a high-dimensional multi-modal descriptor that combines multiple texture features. We also design a novel subcategory discriminant transform (SDT) algorithm to further enhance the discriminative power of descriptors by learning convolution kernels to reduce the within-class variation and increase the between-class difference.
  
  
  RESULTS
  We evaluate our method on eight different bioimage classification tasks using the publicly available IICBU 2008 database. Each task comprises a separate dataset, and the collection represents typical subcellular, cellular, and tissue level classification problems. Our method demonstrates improved classification accuracy (0.9 to 9%) on six tasks when compared to state-of-the-art approaches. We also find that SDT outperforms the well-known dimension reduction techniques, with for example 0.2 to 13% improvement over linear discriminant analysis.
  
  
  CONCLUSIONS
  We present a general bioimage classification method, which comprises a highly descriptive visual feature representation and a learning-based discriminative feature transformation algorithm. Our evaluation on the IICBU 2008 database demonstrates improved performance over the state-of-the-art for six different classification tasks.
authors.affiliation: School of Information Technologies, The University of Sydney, Sydney, Australia. yang.song@sydney.edu.au.
authors.forename: Yang
authors.initials: Y
authors.lastname: Song
authors.affiliation: School of Information Technologies, The University of Sydney, Sydney, Australia.
authors.forename: Weidong
authors.initials: W
authors.lastname: Cai
authors.affiliation: Department of Computer Science and Engineering, University of Texas, Arlington, USA.
authors.forename: Heng
authors.initials: H
authors.lastname: Huang
authors.affiliation: School of Information Technologies, The University of Sydney, Sydney, Australia.|Med-X Research Institute, Shanghai Jiaotong University, Shanghai, China.
authors.forename: Dagan
authors.initials: D
authors.lastname: Feng
authors.affiliation: Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, USA.
authors.forename: Yue
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Computer Engineering Department, University of Albany State University of New York, Albany, USA.|Robotics Institute, Carnegie Mellon University, Pittsburgh, USA.
authors.forename: Mei
authors.initials: M
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D000818:Animals; D016208:Databases, Factual; D016002:Discriminant Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*
keywords: Classification; Discriminative feature transform; Microscopy imaging; Subcategory model