A New Pivoting and Iterative Text Detection Algorithm for Biomedical Images
Identifieur interne : 000611 ( Main/Exploration ); précédent : 000610; suivant : 000612A New Pivoting and Iterative Text Detection Algorithm for Biomedical Images
Auteurs : Songhua Xu [États-Unis] ; Michael Krauthammer [États-Unis]Source :
- Journal of Biomedical Informatics [ 1532-0464 ] ; 2010.
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Abstract
There is interest to expand the reach of literature mining to include the analysis of biomedical images, which often contain a paper’s key findings. Examples include recent studies that use Optical Character Recognition (OCR) to extract image text, which is used to boost biomedical image retrieval and classification. Such studies rely on the robust identification of text elements in biomedical images, which is a non-trivial task. In this work, we introduce a new text detection algorithm for biomedical images based on iterative projection histograms. We study the effectiveness of our algorithm by evaluating the performance on a set of manually labeled random biomedical images, and compare the performance against other state-of-the-art text detection algorithms. In this paper, we demonstrate that a projection histogram-based text detection approach is well suited for text detection in biomedical images, with a performance of F score of .60. The approach performs better than comparable approaches for text detection. Further, we show that the iterative application of the algorithm is boosting overall detection performance. A C++ implementation of our algorithm is freely available through email request for academic use.
Url:
DOI: 10.1016/j.jbi.2010.09.006
PubMed: 20887803
PubMed Central: 3265968
Affiliations:
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Le document en format XML
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Pathology & Yale Center for Medical Informatics, 300 Cedar Street, New Haven, CT 06510</nlm:aff><country xml:lang="fr">États-Unis</country><placeName><region type="state">Connecticut</region></placeName><wicri:cityArea>Department of Pathology & Yale Center for Medical Informatics, 300 Cedar Street, New Haven</wicri:cityArea></affiliation></author></analytic><series><title level="j">Journal of Biomedical Informatics</title><idno type="ISSN">1532-0464</idno><idno type="eISSN">1532-0480</idno><imprint><date when="2010">2010</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Image Interpretation, Computer-Assisted (methods)</term><term>Information Storage and Retrieval (methods)</term><term>Pattern Recognition, Automated (methods)</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Image Interpretation, Computer-Assisted</term><term>Information Storage and Retrieval</term><term>Pattern Recognition, Automated</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p id="P2">There is interest to expand the reach of literature mining to include the analysis of biomedical images, which often contain a paper’s key findings. Examples include recent studies that use Optical Character Recognition (OCR) to extract image text, which is used to boost biomedical image retrieval and classification. Such studies rely on the robust identification of text elements in biomedical images, which is a non-trivial task. In this work, we introduce a new text detection algorithm for biomedical images based on iterative projection histograms. We study the effectiveness of our algorithm by evaluating the performance on a set of manually labeled random biomedical images, and compare the performance against other state-of-the-art text detection algorithms. In this paper, we demonstrate that a projection histogram-based text detection approach is well suited for text detection in biomedical images, with a performance of F score of .60. The approach performs better than comparable approaches for text detection. Further, we show that the iterative application of the algorithm is boosting overall detection performance. 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