Feature-Centric Content Analysis for Bioimaging Informatics
Summary
This study aims to develop novel algorithms for content analysis in microscopic images, such as segmentation of cell nuclei, detection of certain cell structures, and tracing of cell changes over time. Such algorithms would be valuable to turn image data into useful biological knowledge. This study will focus on computer vision methodologies in feature extraction and learning-based modeling.
Supervisor(s)
Associate Professor Tom Weidong Cai
Research Location
Program Type
Masters/PHD
Synopsis
Great advances in biological tissuelabeling and automated microscope imaging have revolutionized how biologists visualize molecular, sub-cellular structures and study their respective functions. How to interpret such image datasets in aquantitative and automative way has become a major challenge in current computational biology. The essential methods of bioimaging informatics involve generation, visualization, analysis and management.
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Keywords
Bioimaging informatics, microscopic imaging, Computer vision, Image processing
Opportunity ID
The opportunity ID for this research opportunity is: 1883
Other opportunities with Associate Professor Tom Weidong Cai
- Content-based Retrieval and Management of Multi-dimensional Biomedical Imaging Data
- Object-based Volumetric Texture Feature Extraction for Biomedical Image Retrieval
- Semantic Feature Description and Similarity Measurement for Large-scale Image Search
- Neuroimaging Computing for Early Detection of Alzheimer’s Disease
- Visual Feature Extraction for Image Pattern Classification
- Kinetic Characterization and Mapping for Whole-body Molecular Image Retrieval