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Low Dimensional Representation of Fisher Vectors for Microscopy Image Classification
- Source :
- IEEE Transactions on Medical Imaging. 36:1636-1649
- Publication Year :
- 2017
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2017.
-
Abstract
- Microscopy image classification is important in various biomedical applications, such as cancer subtype identification, and protein localization for high content screening. To achieve automated and effective microscopy image classification, the representative and discriminative capability of image feature descriptors is essential. To this end, in this paper, we propose a new feature representation algorithm to facilitate automated microscopy image classification. In particular, we incorporate Fisher vector (FV) encoding with multiple types of local features that are handcrafted or learned, and we design a separation-guided dimension reduction method to reduce the descriptor dimension while increasing its discriminative capability. Our method is evaluated on four publicly available microscopy image data sets of different imaging types and applications, including the UCSB breast cancer data set, MICCAI 2015 CBTC challenge data set, and IICBU malignant lymphoma, and RNAi data sets. Our experimental results demonstrate the advantage of the proposed low-dimensional FV representation, showing consistent performance improvement over the existing state of the art and the commonly used dimension reduction techniques.
- Subjects :
- 0301 basic medicine
Feature extraction
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Breast Neoplasms
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
Discriminative model
Dimension (vector space)
Electrical and Electronic Engineering
Representation (mathematics)
Mathematics
Microscopy
Radiological and Ultrasound Technology
Contextual image classification
business.industry
Dimensionality reduction
Pattern recognition
Computer Science Applications
Data set
ComputingMethodologies_PATTERNRECOGNITION
030104 developmental biology
Feature (computer vision)
Artificial intelligence
business
Algorithms
Software
Subjects
Details
- ISSN :
- 1558254X and 02780062
- Volume :
- 36
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Medical Imaging
- Accession number :
- edsair.doi.dedup.....66798a3990bd81cb7c72636a3755805e