Automatic Detection of Arrow Annotation Overlays in Biomedical Images
Automatic Detection of Arrow Annotation Overlays in Biomedical Images
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DOI：
10.4018/jhisi.2011100102
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发表时间：
2011-10-01
影响因子：
1.1
通讯作者：
Thoma, George R.
中科院分区：
文献类型：
--
作者：
Cheng, Beibei;Stanley, R. Joe;Thoma, George R.
生物医学文章中的图像通常用于临床决策支持，教育目的和医学研究。作者标记的注释（如覆盖在这些图像上的文本标签和符号）用于突出显示感兴趣的区域，然后在文章的标题文本或图引用中引用这些区域。检测和识别这些符号对于提高生物医学信息检索的效率具有重要意义。在本研究中，整合影像处理与计算智慧的方法来进行物件分割与判别，并应用于这些影像上的箭枝侦测问题。进化人工神经网络（EANNs）和进化人工神经网络集成（EANNEs）基于计算智能的算法被开发用于识别医学图像中的叠加，特别是箭头。对于这些鉴别技术，EANNs使用粒子群优化和遗传算法进行人工神经网络（ANN）训练，EANNs利用集成中生成的ANN数量和基于平均和线性矢量量化（LVQ）赢家通吃方法的负相关学习进行神经网络训练。在imageCLEFmed'08数据集的医学图像上进行的实验，使用EANNEs方法和赢家通吃方法，分别产生了高达0.988和0.928/0.973的接收器操作特征曲线下的面积和精确度/召回率结果。
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.