Hwee Kuan Lee
Papers
2
Total Citations
72
H-Index
2
About
Hwee Kuan Lee is a prominent researcher in computational imaging and bioinformatics, whose work bridges image analysis and biological discovery. His key research areas include quantitative image segmentation, high-content screening, and the development of geometric features for image understanding. Lee’s major contribution lies in advancing automated methods for analyzing neuronal morphology, particularly through his 2008 study on neurite outgrowth measurement. By integrating image segmentation with topological dependence, he enabled robust, high-throughput quantification of neuronal structures—a critical tool for neuroregeneration research. This work, cited 69 times, has become foundational for scientists studying nerve repair and drug screening. Additionally, Lee explored geometric global image features using region graph spectra (2009), offering novel approaches for characterizing image content. His research has empowered the analysis of thousands of images from robotic fluorescent microscopy, accelerating discoveries in neurobiology. Through his innovative fusion of imaging informatics and biological application, Lee has made lasting impacts on how researchers decode complex cellular behaviors, making him a key figure in computational life sciences.
Research Focus
Key Achievements
Top Papers
- 1
- 2Region Graph Spectra as Geometric Global Image Features3 citations · 2009