Mylona Eleftheria A, Savelonas Michalis A, Maroulis Dimitris, Kossida Sophia
Realtime Systems and Image Analysis Group, Department of Informatics and Telecommunications, University of Athens, 15784 Athens,Greece.
IEEE Trans Inf Technol Biomed. 2011 Jul;15(4):661-7. doi: 10.1109/TITB.2011.2140327. Epub 2011 Apr 7.
This paper introduces a novel computer-based technique for automated detection of protein spots in proteomics images. The proposed technique is based on the localization of regional intensity maxima associated with protein spots and is formulated so as to ignore rectangular-shaped streaks, minimize the detection of false negatives, and allow the detection of multiple overlapping spots. Regional intensity constraints are imposed on the localized maxima in order to cope with the presence of noise and artifacts. The experimental evaluation of the proposed technique on real proteomics images demonstrates that it: 1) achieves a predictive value ( PV) and detection sensitivity (DS ) which exceed 90%; 2) outperforms Melanie software package in terms of PV , specificity, and DS; 3) ignores artifacts; 4) distinguishes multiple overlapping spots; 5) locates spots within streaks; and 6) is automated and efficient.
本文介绍了一种用于蛋白质组学图像中蛋白质斑点自动检测的新型计算机技术。所提出的技术基于与蛋白质斑点相关的区域强度最大值的定位，并进行了如下设计：忽略矩形条纹，尽量减少假阴性的检测，并允许检测多个重叠斑点。为了应对噪声和伪像的存在，对局部最大值施加了区域强度约束。对所提出的技术在真实蛋白质组学图像上进行的实验评估表明，它：1）实现了超过90%的预测值（PV）和检测灵敏度（DS）；2）在PV、特异性和DS方面优于Melanie软件包；3）忽略伪像；4）区分多个重叠斑点；5）在条纹内定位斑点；6）自动化且高效。
