NDLI: Content-Based Microscopic Image Retrieval System for Multi-Image Queries

Content Provider	IEEE Xplore Digital Library
Author	Akakin, H.C. Gurcan, M.N.
Copyright Year	1997
Abstract	In this paper, we describe the design and development of a multitiered content-based image retrieval (CBIR) system for microscopic images utilizing a reference database that contains images of more than one disease. The proposed CBIR system uses a multitiered approach to classify and retrieve microscopic images involving their specific subtypes, which are mostly difficult to discriminate and classify. This system enables both multi-image query and slide-level image retrieval in order to protect the semantic consistency among the retrieved images. New weighting terms, inspired from information retrieval theory, are defined for multiple-image query and retrieval. The performance of the system was tested on a dataset including 1666 imaged high power fields extracted from 57 follicular lymphoma (FL) tissue slides with three subtypes and 44 neuroblastoma (NB) tissue slides with four subtypes. Each slide is semantically annotated according to their subtypes by expert pathologists. By using leave-one-slide out testing scheme, the multi-image query algorithm with the proposed weighting strategy achieves about 93% and 86% of average classification accuracy at the first rank retrieval, outperforming the image-level retrieval accuracy by about 38 and 26 percentage points, for FL and NB diseases, respectively.
Sponsorship	IEEE Engineering in Medicine and Biology Society IEEE Computer Society
Page Count	12
File Size	1078845
Starting Page	758
Ending Page	769
File Format	PDF
ISSN	10897771
Volume Number	16
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Diseases Image color analysis Microscopy Databases Vectors Semantics weighting scores Content-based image retrieval (CBIR) information retrieval (IR) microscopy multi-image queries
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications Biotechnology

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