The genomic sequencing revolution has led to rapid growth in sequencing of genes and proteins, and attention is now turning to the function of the encoded proteins. In this respect, microscope imaging of a protein's subcellular location is proving invaluable. High-throughput methods mean that it is now possible to capture images of hundreds of protein localisations quickly and relatively inexpensively, and hence genome-wide protein localisation studies are becoming feasible. However, to a large degree the analysis and localisation classification are still performed by the slow, coarse-grained and possibly biased process of manual inspection. As a step towards dealing with the fast growth in subcellular image data the Automated Sub-cellular Classification system (ASPiC) has been developed: a pipeline for taking cell images, generating statistics and classifying using SVMs. Here, the pipeline is described and correct classification rates of 93.5% and 86.5% on two 8-class subcellular localisationdatasets are reported. In addition we present a survey of other important applications of cell image statistics. The complete image sets are being made available with the aim of encouraging further research into automated cell image analysis and classification.
机译:“ MTB细胞计数器”是一种多功能工具,用于半自动定量荧光显微镜图像中的亚细胞表型。质体,核和过氧化物酶体的案例研究
机译:一种两步登记分类方法,以实现高通量温室植物表型多峰图像的自动分割
机译:图像分析管线,用于对成像光条件进行自动分类,并在田间表型分析中对小麦冠层覆盖时间序列进行量化
机译:自动化的亚细胞表型分类
机译:通过自动对齐实现自动语音节奏分类。
机译:一种两步登记分类方法以实现高通量温室植物表型多峰图像的自动分割
机译:一种两步登记分类方法,以实现高通量温室植物表型多峰图像的自动分割