NCTU-DBLAB@Imageclefmed 2005: Medical image retrieval task

Pei Cheng Cheng*, Been Chian Chien, Hao Ren Ke, Wei Pang Yang

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review


In this paper, we use Support Vector Machine (SVM) to learn image feature characteristics for assisting the task of image classification. The ImageCLEF 2005 evaluation offers a superior test bed for medical image content retrieval. Several image visual features (including histogram, spatial layout, coherence moment and gabor features) have been employed in this paper to categorize the 1,000 test images into 57 classes. Based on the SVM model, we can examine which image feature is more promising in medical image retrieval. The result shows that the spatial relationship of pixels is a very important feature in medical image data, because medical image data always have similar anatomic regions (lung, liver, head, and so on); therefore image features emphasizing spatial relationship have better result than others.

Original languageEnglish
JournalCEUR Workshop Proceedings
Publication statusPublished - 2005
Externally publishedYes
Event2005 Cross Language Evaluation Forum Workshop, CLEF 2005, co-located with the 9th European Conference on Digital Libraries, ECDL 2005 - Wien, Austria
Duration: 2005 Sept 212005 Sept 22


  • Medical Image Classification
  • Support Vector Machine

ASJC Scopus subject areas

  • General Computer Science


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