Automated classification of protein crystallization images using support vector machines with scale-invariant texture and Gabor features
Automated classification of protein crystallization images using support vector machines with scale-invariant texture and Gabor features
复制标题
DOI：
10.1107/s0907444905041648
复制
发表时间：
2006-03-01
影响因子：
2.2
通讯作者：
Meldrum, D
中科院分区：
文献类型：
--
作者：
Pan, S;Shavit, G;Meldrum, D
蛋白质晶体学实验室正在进行越来越多的实验，以获得良好的衍射质量的晶体。更好的自动化使研究人员能够在更短的时间内准备和运行更多的实验。然而，确定哪些实验是成功的问题仍然很困难。事实上，大部分工作仍然由人类手动完成。因此，自动化这项任务是一个重要的目标。作为开发一种新的自动化高通量毛细管蛋白质晶体学仪器项目的一部分，开发了一种新的图像分类子系统，以大大减少需要人工查看的图像数量。该系统必须具有低的假阴性率（漏失晶体），可能以增加假阳性的数量为代价。图像分类系统采用支持向量机（SVM）学习算法来对构成每个图像的块进行分类。采用一种新的算法来找到包含液滴的图像内的区域。SVM使用基于纹理和Gabor小波分解的数值特征，这些特征是为每个块计算的。如果图像内的块被分类为包含晶体，则整个图像被分类为包含晶体。在一项对375张图像（其中87张包含晶体）的研究中，始终实现了小于4%的假阴性率和约40%的假阳性率。
Protein crystallography laboratories are performing an increasing number of experiments to obtain crystals of good diffraction quality. Better automation has enabled researchers to prepare and run more experiments in a shorter time. However, the problem of identifying which experiments are successful remains difficult. In fact, most of this work is still performed manually by humans. Automating this task is therefore an important goal. As part of a project to develop a new and automated high-throughput capillary-based protein crystallography instrument, a new image-classification subsystem has been developed to greatly reduce the number of images that require human viewing. This system must have low rates of false negatives ( missed crystals), possibly at the cost of raising the number of false positives. The image-classification system employs a support vector machine (SVM) learning algorithm to classify the blocks making up each image. A new algorithm to find the area within the image that contains the drop is employed. The SVM uses numerical features, based on texture and the Gabor wavelet decomposition, that are calculated for each block. If a block within an image is classified as containing a crystal, then the entire image is classified as containing a crystal. In a study of 375 images, 87 of which contained crystals, a false-negative rate of less than 4% with a false-positive rate of about 40% was consistently achieved.