Super-Thresholding: Supervised Thresholding of Protein Crystal Images.
Super-Thresholding: Supervised Thresholding of Protein Crystal Images.
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DOI：
10.1109/tcbb.2016.2542811
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发表时间：
2017-07
期刊：
影响因子：
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通讯作者：
Aygun RS
中科院分区：
文献类型：
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作者：
Dinc I;Dinc S;Sigdel M;Sigdel MS;Pusey ML;Aygun RS
通常，开发或增强单个阈值化技术以针对图像域将前景对象与背景分离。这种想法可能不会为数据集中的所有图像生成令人满意的结果，因为不同的图像可能需要不同类型的阈值方法来进行适当的二值化或分割。为了克服这一限制，在这项研究中，我们提出了一种新的方法，称为“超阈值”，利用监督分类器来决定一个适当的阈值方法为特定的图像。该方法提供了一个通用的框架，允许选择最好的阈值处理方法之间的不同的阈值处理技术，是有益的问题域。通过分析阈值化方法的输出和原始图像，利用仅从原始图像中提取的先验特征或后验特征建立分类器模型。该模型被应用于识别域的新图像的阈值化方法。我们对蛋白质结晶图像进行了我们的方法，然后我们将我们的结果与6种阈值技术进行了比较。使用4种不同的正确性测量提供数值结果。超阈值算法的性能比最好的单阈值算法提高了10%左右，并且在我们的实验中对蛋白质结晶数据集的性能最好。
In general, a single thresholding technique is developed or enhanced to separate foreground objects from background for a domain of images. This idea may not generate satisfactory results for all images in a dataset, since different images may require different types of thresholding methods for proper binarization or segmentation. To overcome this limitation, in this study, we propose a novel approach called “super-thresholding” that utilizes a supervised classifier to decide an appropriate thresholding method for a specific image. This method provides a generic framework that allows selection of the best thresholding method among different thresholding techniques that are beneficial for the problem domain. A classifier model is built using features extracted priori from the original image only or posteriori by analyzing the outputs of thresholding methods and the original image. This model is applied to identify the thresholding method for new images of the domain. We performed our method on protein crystallization images, and then we compared our results with 6 thresholding techniques. Numerical results are provided using 4 different correctness measurements. Super-thresholding outperforms the best single thresholding method around 10%, and it gives the best performance for protein crystallization dataset in our experiments.