Abstract
In cytogenetics, karyotype analysis is used to assess the presence of genetic defects by visualization chromosomes structure from microscopic images. A key step in this process is image thresholding, used to detect and extract objects of interest from background, as it affects the performance of further processing steps in image analysis. In this paper, an adaptive local thresholding for Qband chromosome image segmentation is presented. A re-threshold process based on the Sauvola’s local adaptive technique is applied to extract chromosomes from background. Local adaptive histogram equalization is added between thresholding steps to enhance chromosome segments to reduce the chances of pixel misclassification. The proposed thresholding approach provides 93 % of precision, which is better than other similar approaches when evaluated on a reference image dataset.
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