KIDS's evaluation in medical image retrieval task at ImageCLEF 2004

Pei Cheng Cheng; Been Chian Chien; Hao Ren Ke; Wei Pang Yang

KIDS's evaluation in medical image retrieval task at ImageCLEF 2004

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

Research output: Contribution to journal › Conference article › peer-review

Abstract

We describe our participation in the medical retrieval task of the ImageCLEF 2004. This task aims at finding images that are similar with respect to modality (CT, radiograph, MRI, and so on). We propose several image features, including color histogram, gray-spatial histogram, coherence moment, and gray correlogram, to facilitate the retrieval of similar images. The initial retrieval results are obtained via visual feature analysis. An automatic feedback mechanism clusters visually and textually similar images among these initial results to help refine the query. In this paper, we present the system used, focusing on novel and newly developed aspects. The evaluated result shows that the automatic feedback mechanism improves the precision by 15%.

Original language	English
Journal	CEUR Workshop Proceedings
Volume	1170
Publication status	Published - 2004
Externally published	Yes
Event	2004 Cross Language Evaluation Forum Workshop, CLEF 2004, co-located with the 8th European Conference on Digital Libraries, ECDL 2004 - Bath, United Kingdom Duration: 2004 Sept 15 → 2004 Sept 17

Keywords

Color histogram
Medical image retrieval
Relevance feedback

ASJC Scopus subject areas

Computer Science(all)

Cite this

@article{efcff7e259974eb3980180df410af4d3,

title = "KIDS's evaluation in medical image retrieval task at ImageCLEF 2004",

abstract = "We describe our participation in the medical retrieval task of the ImageCLEF 2004. This task aims at finding images that are similar with respect to modality (CT, radiograph, MRI, and so on). We propose several image features, including color histogram, gray-spatial histogram, coherence moment, and gray correlogram, to facilitate the retrieval of similar images. The initial retrieval results are obtained via visual feature analysis. An automatic feedback mechanism clusters visually and textually similar images among these initial results to help refine the query. In this paper, we present the system used, focusing on novel and newly developed aspects. The evaluated result shows that the automatic feedback mechanism improves the precision by 15%.",

keywords = "Color histogram, Medical image retrieval, Relevance feedback",

author = "Cheng, {Pei Cheng} and Chien, {Been Chian} and Ke, {Hao Ren} and Yang, {Wei Pang}",

year = "2004",

language = "English",

volume = "1170",

journal = "CEUR Workshop Proceedings",

issn = "1613-0073",

publisher = "CEUR-WS",

note = "2004 Cross Language Evaluation Forum Workshop, CLEF 2004, co-located with the 8th European Conference on Digital Libraries, ECDL 2004 ; Conference date: 15-09-2004 Through 17-09-2004",

}

TY - JOUR

T1 - KIDS's evaluation in medical image retrieval task at ImageCLEF 2004

AU - Cheng, Pei Cheng

AU - Chien, Been Chian

AU - Ke, Hao Ren

AU - Yang, Wei Pang

PY - 2004

Y1 - 2004

N2 - We describe our participation in the medical retrieval task of the ImageCLEF 2004. This task aims at finding images that are similar with respect to modality (CT, radiograph, MRI, and so on). We propose several image features, including color histogram, gray-spatial histogram, coherence moment, and gray correlogram, to facilitate the retrieval of similar images. The initial retrieval results are obtained via visual feature analysis. An automatic feedback mechanism clusters visually and textually similar images among these initial results to help refine the query. In this paper, we present the system used, focusing on novel and newly developed aspects. The evaluated result shows that the automatic feedback mechanism improves the precision by 15%.

AB - We describe our participation in the medical retrieval task of the ImageCLEF 2004. This task aims at finding images that are similar with respect to modality (CT, radiograph, MRI, and so on). We propose several image features, including color histogram, gray-spatial histogram, coherence moment, and gray correlogram, to facilitate the retrieval of similar images. The initial retrieval results are obtained via visual feature analysis. An automatic feedback mechanism clusters visually and textually similar images among these initial results to help refine the query. In this paper, we present the system used, focusing on novel and newly developed aspects. The evaluated result shows that the automatic feedback mechanism improves the precision by 15%.

KW - Color histogram

KW - Medical image retrieval

KW - Relevance feedback

UR - http://www.scopus.com/inward/record.url?scp=84961302278&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84961302278&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:84961302278

SN - 1613-0073

VL - 1170

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

T2 - 2004 Cross Language Evaluation Forum Workshop, CLEF 2004, co-located with the 8th European Conference on Digital Libraries, ECDL 2004

Y2 - 15 September 2004 through 17 September 2004

ER -

KIDS's evaluation in medical image retrieval task at ImageCLEF 2004

Abstract

Keywords

ASJC Scopus subject areas

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