Combining Textual and Visual Features for Cross-Language Medical Image Retrieval View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2006

AUTHORS

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

ABSTRACT

In this paper we describe the technologies and experimental results for the medical retrieval task and automatic annotation task. We combine textual and content-based approaches to retrieve relevant medical images. The content-based approach containing four image features and the text-based approach using word expansion are developed to accomplish these tasks. Experimental results show that combining both the content-based and text-based approaches is better than using only one approach. In the automatic annotation task we use Support Vector Machines (SVM) to learn image feature characteristics for assisting the task of image classification. Based on the SVM model, we analyze which image feature is more promising in medical image retrieval. The results show that the spatial relationship between pixels is an important feature in medical image data because medical image data always has similar anatomic regions. Therefore, image features emphasizing spatial relationship have better results than others. More... »

PAGES

712-723

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/11878773_78

DOI

http://dx.doi.org/10.1007/11878773_78

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1048156226


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