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  Computer Vision and Pattern Discovery
 
Dr. LEE Hwee Kuan
Principal Investigator

Details
 
 
Research Group Members
 
NameTitle
 Dr. LEE Hwee Kuan  Principal Investigator
 Dr. CHENG Li  Assistant Principal Investigator
 Dr. CELIK Turgay  Postdoctoral Fellow
 Dr. HUANG Chao Hui  Postdoctoral Fellow
 Dr. KOH Yang Wei, Patrick  Postdoctoral Fellow
 Dr. LAW Yan Nei, Ivy  Postdoctoral Fellow
 Mr. YE Ning  Postdoctoral Fellow
 Mr. YAP Choon Kong  Research Associate
 Mr. Mohamed Hanifa  Software Engineer
 
Research Projects

The group of Computer Vision and Pattern Discovery for Bioimages focuses on applying advanced computer vision, machine learning and mathematical models to elucidate the complex behavior of biological systems. The group analyses images from wide-field and confocal microscopes, including image data sets from high-throughput screens. The trend towards quantitative biology has spawned new areas of research, especially in the area of digital imaging where thousands of images are acquired automatically through robotic systems of chemical and cell assays handling. These images are then analyzed and used to create new biological hypotheses that are further validated using other experimental means. The group's contributions to high throughput, high content imaging are to provide accurate and fast computational methods for the data mining of large image data sets.
 
Cellular Phenotype Recognition   Texture Segmentation Using the Subspace Mumford-Shah Model
     
Entropy Regularization Feature Relevance for Texture and its Applications in Bioimage Processing   Field Theoretical Method for Image Restoration
     
Automatic and Quantitative Measurement of Neural Cell Outgrowths   Biomedical Image Segmentation of Semi-transparent Objects Using a Variant of the Mumford-Shah Model
     
A Multi-resolution Stochastic Level Set Method for the Segmentation of Bioimages   Automated Nucleus and Cells Detection using a Region Based Ellipse Detector
     
Automated Protein Distribution Detection in Images from High-throughput Image-based siRNA Library Screens      
 
 

 
Journal Publications
 
  1. Yan Nei Law, Andy M. Yip and Hwee Kuan Lee
    Automatic Measurement of Volume Percentage Stroma in Endometrial Images using Texture Segmentation
    Journal of Microscopy (in press)


  2. Y.N.Law, H. K. Lee, A. M. Yip
    Semi-supervised Subspace Learning for Mumford-Shah Model Based Texture Segmentation
    Optics Express, 2010; 18(5): 4934-4448


  3. W. Yu, H. K. Lee et al
    Evolving Generalized Voranoi Diagram of Active Contours for Accurate Cellular Image Segmentation
    Cytometry Part A, 2010; 77A: 379-386


  4. Hwee Kuan Lee, Mohammad Shorif Uddin, Shvetha Sankaran, Srivats Hariharan, Sohail Ahmed
    A field theoretical restoration method for images degraded by non-uniform light attenuation : an application for light microscopy
    Optics Express, 2009; 17: 11294-11308
    (selected to be published again in the Virtual Journal of Biomedical Optics, 2009; 4: 11294-11308)


  5. W. Yu, H. K. Lee, S. Hariharan, W. Y. Bu, S. Ahmed
    Quantitative Neurite Outgrowth Measurement Based on Image Segmentation with Topological Dependence
    Cytometry A, 2008; 75A: 289-297


  6. Yan Nei Law, Hwee Kuan Lee and Andy M. Yip
    A Multi-resolution Stochastic Level Set Method for Mumford-Shah Image Segmentation
    IEEE Trans. Img. Proc., 2008; 17: 2289-2300
    CAM Report 07-43 (2007) http://www.math.ucla.edu/applied/cam/


  7. Y. N. Law, S. Ogg, et al.
    Automated Protein Distribution Detection in High-Throughput Image-Based siRNA Library Screens
    Journal of Signal Processing Systems, 2009; 55: 1-13


 
Conference Proceedings
 
  1. Yan Nei Law, Hwee Kuan Lee and Andy M. Yip
    Subspace Descriptor for Texture and its Applications
    Proc. of the 2009 Intl. Conf. on Image Processing, Computer Vision and Pattern Recognition (IPCV10).


  2. Yan Nei Law, Hwee Kuan Lee and Andy M. Yip
    A Subspace Clustering Model for Image Texture Segmentation
    Proc. of 5th European Conference on Computational Fluid Dynamics


  3. W. Yu, H. K. Lee et al
    Segmentation of Neural Stem/Progenitor Cells Nuclei within 3-D Neurospheres
    Int. Sym. Visual Computing, (ISVC 2009). Lecture Notes in Computer Science, 2009; 5875: 531-543


  4. Q. Ho, W. Yu , H. K. Lee
    Region Graph Spectra as Geometric Global Image Features
    Int. Sym. Visual Computing, (ISVC 2009). Lecture Notes in Computer Science, 2009; 5875: 253-264


  5. W. Yu, H.K. Lee et al
    Detection and Quantitative Measurement of Neuronal Outgrowth in Fluorescence Microscopy Images
    Proc. of the Medical Image Understanding and Analysis (MIUA) 2009.


  6. YN Law, HK Lee, AM Yip
    Supervised Texture Segmentation Using the Subspace Mumford-Shah Model
    Proc. of the 2009 Intl. Conf. on Image Processing, Computer Vision and Pattern Recognition (IPCV09).


  7. YN Law, HK Lee, C Liu, AM Yip
    Segmentation of Semi-transparent Objects Using a Variant of the Mumford-Shah Model
    Proc. of the 2009 Intl. Conf. on Image Processing, Computer Vision and Pattern Recognition (IPCV09).


  8. Choon Kong Yap, Hwee Kuan Lee
    Identification of Cell Nucleus Using a Mumford-Shah Ellipse Detector
    Int. Sym. Visual Computing (ISVC 2008). Lecture Notes in Computer Science, 2008; 5358: 582-593


  9. Weimiao Yu, Hwee Kuan Lee, Srivats Hariharan, Wenyu Bu and Sohail Ahmed
    Level Set Segmentation of Cellular Images Based on Topological Dependence
    Int. Sym. Visual Computing (ISVC 2008). Lecture Notes in Computer Science, 2008; 5358: 540-551 (Best Paper Award in ISVC2008)
 
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