biomedical imaging modality classification using combined visual features and textual terms

Clicks: 190
ID: 218926
2011
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Abstract
We describe an approach for the automatic modality classification in medical image retrieval task of the 2010 CLEF cross-language image retrieval campaign (ImageCLEF). This paper is focused on the process of feature extraction from medical images and fuses the different extracted visual features and textual feature for modality classification. To extract visual features from the images, we used histogram descriptor of edge, gray, or color intensity and block-based variation as global features and SIFT histogram as local feature. For textual feature of image representation, the binary histogram of some predefined vocabulary words from image captions is used. Then, we combine the different features using normalized kernel functions for SVM classification. Furthermore, for some easy misclassified modality pairs such as CT and MR or PET and NM modalities, a local classifier is used for distinguishing samples in the pair modality to improve performance. The proposed strategy is evaluated with the provided modality dataset by ImageCLEF 2010.
Reference Key
han2011internationalbiomedical Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Xian-Hua Han;Yen-Wei Chen
Journal Chemistry, an Asian journal
Year 2011
DOI
10.1155/2011/241396
URL
http://dx.doi.org/10.1155/2011/241396
Keywords
medical physics nuclear medicinemedical technology medical radiology

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