Automatically Finding Images for Clinical Decision Support

Emara, Wael; Karnstedt, Mehmed Kantardzic Marcel; Sattler, Kai-Uwe; Habich, Dirk; Lehner, Wolfgang

doi:10.1109/icdmw.2007.12

Cited by 26 publications

(33 citation statements)

References 11 publications

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“…Image data, figure captions, and paragraphs surrounding figure mentions in text were used in classification. Automatic image classification achieved 84.3% accuracy using image captions for modality and 76.6% accuracy combining captions and image data for utility [10].…”

Section: Introductionmentioning

confidence: 99%

“…Encouraged by success achieved in various informatics applications through combining textual and image data, we explored a new area of biomedical image annotation using textual and image data -that of classifying images in biomedical articles with respect to their utility for clinical decision support and if such images could be reliably extracted from the original articles [10]. We selected 2004 --2005 issues of the British Journal of Oral and Maxillofacial Surgery, manually annotating 743 images by utility and modality (radiological, photo, etc.)…”

Section: Introductionmentioning

confidence: 99%

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<title>Exploring use of images in clinical articles for decision support in evidence-based medicine</title>

Demner‐Fushman

Srinivasan

et al. 2008

SPIE Proceedings

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Essential information is often conveyed pictorially (images, illustrations, graphs, charts, etc.) in biomedical publications. A clinician's decision to access the full text when searching for evidence in support of clinical decision is frequently based solely on a short bibliographic reference. We seek to automatically augment these references with images from the article that may assist in finding evidence.In a previous study, the feasibility of automatically classifying images by usefulness (utility) in finding evidence was explored using supervised machine learning and achieved 84.3% accuracy using image captions for modality and 76.6% accuracy combining captions and image data for utility on 743 images from articles over 2 years from a clinical journal. Our results indicated that automatic augmentation of bibliographic references with relevant images was feasible. Other research in this area has determined improved user experience by showing images in addition to the short bibliographic reference. Multi-panel images used in our study had to be manually pre-processed for image analysis, however. Additionally, all image-text on figures was ignored.In this article, we report on developed methods for automatic multi-panel image segmentation using not only image features, but also clues from text analysis applied to figure captions. In initial experiments on 516 figure images we obtained 95.54% accuracy in correctly identifying and segmenting the sub-images. The errors were flagged as disagreements with automatic parsing of figure caption text allowing for supervised segmentation. For localizing text and symbols, on a randomly selected test set of 100 single panel images our methods reported, on the average, precision and recall of 78.42% and 89.38%, respectively, with an accuracy of 72.02%.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

<title>Exploring use of images in clinical articles for decision support in evidence-based medicine</title>

Demner‐Fushman

Srinivasan

et al. 2008

SPIE Proceedings

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“…In this study, images in various modalities were examined from the ImageCLEFMed 2010 dataset [52] from 2004-2006 issues of Radiology and RadioGraphics biomedical publications; these images were previously investigated by Demner-Fushman et al in [33] for feature development and classification. …”

Section: Data Set Examinedmentioning

confidence: 99%

“…. These quantities are applied to a set of medical publication illustrations and modalities examined in previous research [33]. The remainder of the paper is organized as follows: 1) description of the features and feature groups investigated, 2) modality classification experiments performed, 3) results and discussion, and 4) conclusions.…”

Section: Introductionmentioning

confidence: 99%

Features Advances to Automatically Find Images for Application to Clinical Decision Support

Stanley¹

2016

MRAJ

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Filtering through ever increasing sources of information to find relevant information for clinical decisions is a challenging task for clinicians. In biomedical publications, there are a variety of items that can provide evidence to aid the decision making process. One example is illustration image analysis and classification, which has been used to characterize and distinguish specific image modalities; this capability in turn has been used to assist in the evidence gathering process. This paper examines clinical decision support applications and extends previous research for illustration modality discrimination analysis.Specifically, global, HSV histogram-based, and Gabor filter-based features are compared to histogram-based features for modality classification on a set of 12,056 images from 2004-2006 biomedical publication issues of Radiology and RadioGraphics that were manually annotated by modality (radiological, photo, etc.). Using a nearest neighbor classifier, average modality discrimination results were obtained as high as 99.98% using correlated features computed from Gabor filter spectral coefficients. These experimental results indicate that image features, particularly correlationbased features, can provide modality discrimination useful for clinical decision support applications.

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“…Refer to Section 5 for work related to our method in general. Demner-Fushman et al (2007) developed a machine learning approach to identify images from biomedical publications that are relevant to clinical decision support. In this work, the authors utilized both image and textual features to classify images based on their usefulness in evidencebased medicine.…”

Section: Image Retrieval: Recent Workmentioning

confidence: 99%

Using non-lexical features to identify effective indexing terms for biomedical illustrations

Simpson

Demner‐Fushman

Sneiderman

et al. 2009

Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics on - EACL '09

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Automatic image annotation is an attractive approach for enabling convenient access to images found in a variety of documents. Since image captions and relevant discussions found in the text can be useful for summarizing the content of images, it is also possible that this text can be used to generate salient indexing terms. Unfortunately, this problem is generally domainspecific because indexing terms that are useful in one domain can be ineffective in others. Thus, we present a supervised machine learning approach to image annotation utilizing non-lexical features 1 extracted from image-related text to select useful terms. We apply this approach to several subdomains of the biomedical sciences and show that we are able to reduce the number of ineffective indexing terms.

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Automatically Finding Images for Clinical Decision Support

Cited by 26 publications

References 11 publications

<title>Exploring use of images in clinical articles for decision support in evidence-based medicine</title>

<title>Exploring use of images in clinical articles for decision support in evidence-based medicine</title>

Features Advances to Automatically Find Images for Application to Clinical Decision Support

Using non-lexical features to identify effective indexing terms for biomedical illustrations

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