Bioimage classification with subcategory discriminant transform of high dimensional visual descriptors

BMC Bioinformatics

Research Article

Yue Wang¹  Mei Chen²  Heng Huang³  Weidong Cai⁴  Yang Song⁴  Dagan Feng⁵ 

[1] Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, USA;Computer Engineering Department, University of Albany State University of New York, Albany, USA;Robotics Institute, Carnegie Mellon University, Pittsburgh, USA;Department of Computer Science and Engineering, University of Texas, Arlington, USA;School of Information Technologies, The University of Sydney, Sydney, Australia;School of Information Technologies, The University of Sydney, Sydney, Australia;Med-X Research Institute, Shanghai Jiaotong University, Shanghai, China;

关键词: Microscopy imaging;    Classification;    Subcategory model;    Discriminative feature transform;   

DOI  :  10.1186/s12859-016-1318-9

 received in 2016-03-07, accepted in 2016-11-01,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundBioimage 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.ResultsWe 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.ConclusionsWe 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.

【 授权许可】

CC BY   
© The Author(s) 2016

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