An Iconic and Semantic Content Based Retrieval System for Histological Images

  • Ringo W. K. Lam
  • Kent K. T. Cheung
  • Horace H. S. Ip
  • Lilian H. Y. Tang
  • R. Hanka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1929)


This paper describes an intelligent image retrieval system based on iconic and semantic content of histological images. The system first divides an image into a set of subimages. Then the iconic features are derived from primitive features of color histogram, texture and second order statistics of the subimages. These features are then passed to a high level semantic reasoning engine, which generates hypotheses and requests a number of specific fine feature detectors for verification. After iterating a certain number of cycles, a final histological label map is decided for the submitted image. The system may then retrieve images based on either iconic or semantic content. Annotation is also generated for each image processed.


Image Retrieval Query Image Color Histogram Histological Image CBIR System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Ringo W. K. Lam
    • 1
  • Kent K. T. Cheung
    • 1
  • Horace H. S. Ip
    • 1
  • Lilian H. Y. Tang
    • 2
  • R. Hanka
    • 2
  1. 1.Image Computing Group, Computer ScienceCity University of Hong KongHK
  2. 2.Medical Informatics Unit, Medical SchoolUniversity of CambridgeUK

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