Analysis of the chromosome images plays an important role in discovering one's genetic information and possible genetic disorders. Segmentation has a very substantial place in the chromosome analysis and without an automatic solution, it is a time-consuming and error-prone procedure. Many researchers tried to automate the segmentation process. However, background noise, objects other than chromosomes in the image, touching and overlapped chromosomes are still current issues. To address these issues, the authors proposed fully-automatic raw G-band chromosome image segmentation, which aims to segment every single chromosome with a minimal error. The proposed algorithm contains the following steps: clearing the background noise, eliminating the objects other than chromosomes, distinguishing single chromosomes and chromosome clusters, separating touching and overlapping chromosomes. The proposed algorithm is tested on 508 raw images and achieved an accuracy of 94.7% for touching chromosome separation, 96.3% for overlapped chromosome separation, and 98.94% for segmentation of all chromosomes. The whole segmentation process takes 2–7 s for one image, depending on the number of touching and overlapping chromosomes. The segmentation results showed that compared to the previously proposed methods, their algorithm achieved better accuracy.

中文翻译：

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全自动原始G波段染色体图像分割

染色体图像的分析在发现一个人的遗传信息和可能的遗传疾病中起着重要的作用。分割在染色体分析中占有非常重要的位置，并且没有自动解决方案，这是一个耗时且容易出错的过程。许多研究人员试图使分割过程自动化。但是，背景噪声，图像中除染色体以外的物体，接触和重叠的染色体仍然是当前的问题。为了解决这些问题，作者提出了全自动的原始G带染色体图像分割方法，该方法旨在以最小的误差分割每个单个染色体。该算法包括以下步骤：清除背景噪声，消除染色体以外的物体，区分单个染色体和染色体簇，分离接触和重叠的染色体。该算法在508张原始图像上进行了测试，触摸染色体分离的准确度为94.7％，重叠染色体分离的准确度为96.3％，所有染色体的分割准确度为98.94％。一幅图像的整个分割过程需要2–7 s，具体取决于接触和重叠染色体的数量。分割结果表明，与先前提出的方法相比，它们的算法具有更好的准确性。取决于接触和重叠染色体的数量。分割结果表明，与先前提出的方法相比，它们的算法具有更好的准确性。取决于接触和重叠染色体的数量。分割结果表明，与先前提出的方法相比，它们的算法具有更好的准确性。