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.
|Journal||CEUR Workshop Proceedings|
|Publication status||Published - 2005|
- Medical Image Classification
- Support Vector Machine
ASJC Scopus subject areas
- Computer Science(all)