Semantic query processing and annotation generation for content-based retrieval of histological images
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 22_Publication in policy or professional journal › Not applicable
Author(s)
Detail(s)
Original language | English |
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Pages (from-to) | 366-375 |
Journal / Publication | Proceedings of SPIE - The International Society for Optical Engineering |
Volume | 3980 |
Publication status | Published - 2000 |
Externally published | Yes |
Conference
Title | Medical Imaging 2000 - PACS Design and Evaluation: Engineering and Clinical Issues |
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City | San Diego, CA, USA |
Period | 15 - 17 February 2000 |
Link(s)
Abstract
In this paper we present a semantic content representation scheme and the associated techniques for supporting (a) query by image examples or by natural language in a histological image database and (b) automatic annotation generation for images through image semantic analysis. In this research, various types of query are analyzed by either a semantic analyzer or a natural language analyzer to extract high level concepts and histological information, which are subsequently converted into an internal semantic content representation structure code-named `Papillon'. Papillon serves not only as an intermediate representation scheme but also stores the semantic content of the image that will be used to match against the semantic index structure within the image database during query processing. During the image database population phase, all images that are going to be put into the database will go through the same processing so that every image would have its semantic content represented by a Papillon structure. Since the Papillon structure for an image contains high level semantic information of the image, it forms the basis of the technique that automatically generates textual annotation for the input images. Papillon bridges the gap between different media in the database, allows complicated intelligent browsing to be carried out efficiently, and also provides a well-defined semantic content representation scheme for different content processing engines developed for content-based retrieval.
Citation Format(s)
Semantic query processing and annotation generation for content-based retrieval of histological images. / Tang, Lilian H Y; Hanka, Rudolf; Ip, Horace H S; Cheung, Kent K T; Lam, Ringo.
In: Proceedings of SPIE - The International Society for Optical Engineering, Vol. 3980, 2000, p. 366-375.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 22_Publication in policy or professional journal › Not applicable