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Automatic Detection of Arrow Annotation Overlays in Biomedical Images

Automatic Detection of Arrow Annotation Overlays in Biomedical Images

Beibei Cheng, R. Joe Stanley, Soumya De, Sameer Antani, George R. Thoma
Copyright: © 2011 |Volume: 6 |Issue: 4 |Pages: 19
ISSN: 1555-3396|EISSN: 1555-340X|EISBN13: 9781613507360|DOI: 10.4018/jhisi.2011100102
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MLA

Cheng, Beibei, et al. "Automatic Detection of Arrow Annotation Overlays in Biomedical Images." IJHISI vol.6, no.4 2011: pp.23-41. http://doi.org/10.4018/jhisi.2011100102

APA

Cheng, B., Stanley, R. J., De, S., Antani, S., & Thoma, G. R. (2011). Automatic Detection of Arrow Annotation Overlays in Biomedical Images. International Journal of Healthcare Information Systems and Informatics (IJHISI), 6(4), 23-41. http://doi.org/10.4018/jhisi.2011100102

Chicago

Cheng, Beibei, et al. "Automatic Detection of Arrow Annotation Overlays in Biomedical Images," International Journal of Healthcare Information Systems and Informatics (IJHISI) 6, no.4: 23-41. http://doi.org/10.4018/jhisi.2011100102

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Abstract

Images in biomedical articles are often referenced for clinical decision support, educational purposes, and medical research. Authors-marked annotations such as text labels and symbols overlaid on these images are used to highlight regions of interest which are then referenced in the caption text or figure citations in the articles. Detecting and recognizing such symbols is valuable for improving biomedical information retrieval. In this research, image processing and computational intelligence methods are integrated for object segmentation and discrimination and applied to the problem of detecting arrows on these images. Evolving Artificial Neural Networks (EANNs) and Evolving Artificial Neural Network Ensembles (EANNEs) computational intelligence-based algorithms are developed to recognize overlays, specifically arrows, in medical images. For these discrimination techniques, EANNs use particle swarm optimization and genetic algorithm for artificial neural network (ANN) training, and EANNEs utilize the number of ANNs generated in an ensemble and negative correlation learning for neural network training based on averaging and Linear Vector Quantization (LVQ) winner-take-all approaches. Experiments performed on medical images from the imageCLEFmed’08 data set, yielded area under the receiver operating characteristic curve and precision/recall results as high as 0.988 and 0.928/0.973, respectively, using the EANNEs method with the winner-take-all approach.

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