Dinc Imren, Dinc Semih, Sigdel Madhav, Sigdel Madhu S, Pusey Marc L, Aygun Ramazan S
IEEE/ACM Trans Comput Biol Bioinform. 2017 Jul-Aug;14(4):986-998. doi: 10.1109/TCBB.2016.2542811. Epub 2016 Mar 16.
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 six thresholding techniques. Numerical results are provided using four different correctness measurements. Super-thresholding outperforms the best single thresholding method around 10 percent, and it gives the best performance for protein crystallization dataset in our experiments.
一般来说，会开发或改进一种单一的阈值处理技术，以便在图像领域中将前景对象与背景分离。对于数据集中的所有图像而言，这种想法可能无法产生令人满意的结果，因为不同的图像可能需要不同类型的阈值处理方法才能进行适当的二值化或分割。为了克服这一局限性，在本研究中，我们提出了一种名为“超级阈值处理”的新方法，该方法利用监督分类器为特定图像确定合适的阈值处理方法。此方法提供了一个通用框架，允许在对问题领域有益的不同阈值处理技术中选择最佳的阈值处理方法。分类器模型是使用仅从原始图像先验提取的特征或通过分析阈值处理方法的输出和原始图像后验提取的特征构建的。该模型用于识别该领域新图像的阈值处理方法。我们在蛋白质结晶图像上执行了我们的方法，然后将我们的结果与六种阈值处理技术进行了比较。使用四种不同的正确性度量提供了数值结果。超级阈值处理比最佳的单一阈值处理方法性能高出约10%，并且在我们的实验中，它在蛋白质结晶数据集上表现最佳。
