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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\u201908 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.<\/p>","DOI":"10.4018\/jhisi.2011100102","type":"journal-article","created":{"date-parts":[[2011,11,16]],"date-time":"2011-11-16T07:47:14Z","timestamp":1321429634000},"page":"23-41","source":"Crossref","is-referenced-by-count":10,"title":["Automatic Detection of Arrow Annotation Overlays in Biomedical Images"],"prefix":"10.4018","volume":"6","author":[{"given":"Beibei","family":"Cheng","sequence":"first","affiliation":[{"name":"Missouri University of Science and Technology, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R. 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