Khmelinskii Artem, Ventura Rodrigo, Sanches João
Institute for Systems and Robotics / Instituto Superior Técnico, 1049-001 Lisbon, Portugal.
Annu Int Conf IEEE Eng Med Biol Soc. 2008;2008:1918-21. doi: 10.1109/IEMBS.2008.4649562.
Cytogenetics is a key tool in the detection of acquired chromosomal abnormalities and in the diagnosis of genetic diseases such as leukemia. The karyotyping is a set of procedures, in the scope of the cytogenetics, that produces a visual representation of the 46 chromosomes (called karyogram), paired and arranged in decreasing order of size. The pairing procedure aims to identify all pairs of homologous chromosomes.The pairing criterion is based on dimensional, morphological,and textural features similarity. This process is time consuming when performed manually, and demanding from a technical point of view. An automatic pairing algorithm would thus bring benefits, but it remains an open problem to date.In this paper a new strategy for automatic pairing of homologous chromosomes is proposed. Besides the traditional features described in the literature, the Mutual Information (MI) is used to discriminate chromosome textural differences. A supervised non-linear classifier is trained by using manual classifications provided by expert technicians, combining the different features computed from each pair.Simulations using 836 real chromosome images, obtained with a Leica Optical Microscope DM 2500, in a leave-one-out cross validation fashion, were performed for training and testing the algorithm.Promising and relevant results were found, despite the poor quality of the original chromosome images, contrasting with state-of the-art algorithms and datasets found in the literature.
细胞遗传学是检测获得性染色体异常以及诊断白血病等遗传疾病的关键工具。核型分析是细胞遗传学范畴内的一组程序,它能生成46条染色体的可视化图像(称为核型图),这些染色体成对排列,并按大小递减顺序排列。配对程序旨在识别所有同源染色体对。配对标准基于尺寸、形态和纹理特征的相似性。手动执行此过程既耗时,从技术角度来看要求也很高。因此,自动配对算法会带来诸多益处,但迄今为止这仍是一个未解决的问题。本文提出了一种同源染色体自动配对的新策略。除了文献中描述的传统特征外,互信息(MI)被用于区分染色体的纹理差异。通过使用专家技术人员提供的手动分类,结合从每对染色体计算出的不同特征,训练一个有监督的非线性分类器。使用徕卡光学显微镜DM 2500获取的836张真实染色体图像,以留一法交叉验证的方式进行模拟,用于训练和测试该算法。尽管原始染色体图像质量较差,但与文献中发现的现有算法和数据集相比,仍获得了有前景且相关的结果。