Institute for Systems and Robotics, Instituto Superior Técnico, 1049-001 Lisbon, Portugal.
IEEE Trans Biomed Eng. 2010 Jun;57(6):1420-9. doi: 10.1109/TBME.2010.2040279. Epub 2010 Feb 17.
Karyotyping is a set of procedures, in the scope of the cytogenetics, that produces a visual representation of the 46 chromosomes observed during the metaphase step of the cellular division, called mitosis, paired and arranged in decreasing order of size. Automatic pairing of bone marrow cells is a difficult task because these chromosomes appear distorted, overlapped, and their images are usually blurred with undefined edges and low level of detail. In this paper, a new metric is proposed to compare this type of chromosome images toward the design of an automatic pairing algorithm for leukemia diagnostic purposes. Besides the features used in the traditional karyotyping procedures, a new feature, based on mutual information , is proposed to increase the discriminate power of the G-banding pattern dissimilarity between chromosomes and improve the performance of the classifier. The pairing algorithm is formulated as a combinatorial optimization problem where the distances between homologous chromosomes are minimized and the distances between nonhomologous ones are maximized. The optimization task is solved by using an integer programming approach. A new bone marrow chromosome dataset--Lisbon-K1 (LK1) chromosome dataset with 9200 chromosomes---was build for this study. These chromosomes have much lower quality than the classic Copenhagen, Edinburgh, and Philadelphia datasets, and its classification and pairing is therefore more difficult. Experiments using real images from the LK(1) and Grisan et al. datasets based on a leave-one-out cross-validation strategy are performed to test and validate the pairing algorithm.
核型分析是细胞遗传学领域的一组程序，它产生了在细胞分裂中期（称为有丝分裂）观察到的 46 条染色体的视觉表示，这些染色体按大小降序排列并配对。骨髓细胞的自动配对是一项艰巨的任务，因为这些染色体看起来扭曲、重叠，其图像通常边缘模糊、细节不清晰。在本文中，提出了一种新的度量标准，用于比较这种类型的染色体图像，以便为白血病诊断目的设计自动配对算法。除了传统核型分析程序中使用的特征外，还提出了一种基于互信息的新特征，以提高染色体 G 带模式之间的可区分性，并提高分类器的性能。配对算法被表述为一个组合优化问题，其中同源染色体之间的距离最小化，非同源染色体之间的距离最大化。优化任务通过整数编程方法解决。为这项研究构建了一个新的骨髓染色体数据集——里斯本-K1（LK1）染色体数据集，其中包含 9200 条染色体。这些染色体的质量远低于经典的哥本哈根、爱丁堡和费城数据集，因此分类和配对更加困难。基于留一交叉验证策略，使用来自 LK1 和 Grisan 等人的数据集的真实图像进行实验，以测试和验证配对算法。
