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Bioimage classification with subcategory discriminant transform of high dimensional visual descriptors
- Source :
- BMC Bioinformatics
- Publication Year :
- 2016
- Publisher :
- BMC, 2016.
-
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.
- Subjects :
- 0301 basic medicine
Databases, Factual
Computer science
Machine learning
computer.software_genre
Biochemistry
Discriminative feature transform
Convolution
Subcategory model
03 medical and health sciences
Discriminative model
Structural Biology
Image Processing, Computer-Assisted
Feature (machine learning)
Animals
Humans
Microscopy imaging
Representation (mathematics)
Molecular Biology
Subcategory
business.industry
Applied Mathematics
Dimensionality reduction
Discriminant Analysis
Pattern recognition
Classification
Linear discriminant analysis
Computer Science Applications
030104 developmental biology
ComputingMethodologies_PATTERNRECOGNITION
Discriminant
Artificial intelligence
business
computer
Algorithms
Research Article
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- BMC Bioinformatics
- Accession number :
- edsair.doi.dedup.....7ec77b5ea31e0eb230b81e3fff39da78