Band classification based on chromosome shapes

Uttamatanin, Ravi; Intarapanich, Apichart; Kaewkamnerd, Saowaluck; Yuvapoositanon, Peerapol; Tongsima, Sissades

doi:10.1109/bmeicon.2013.6687672

Cited by 7 publications

(2 citation statements)

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“…In the method, a knowledge-based classifier based on the seven features is applied to delete non-chromosome objects. Uttamatanin et al [12] proposed a metaphase selection tool that classifies segmented objects into four classes based on width, height, and estimated . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.…”

Section: Introductionmentioning

confidence: 99%

“…In the method, a knowledge-based classifier based on the seven features is applied to delete non-chromosome objects. Uttamatanin et al [12] proposed a metaphase selection tool that classifies segmented objects into four classes based on width, height, and estimated area ratio. One (Class 4) of the four classified classes corresponds to non-chromosome objects.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Learning Method with High Specificity for Excluding Non-chromosome Objects Problem

Wu¹,

Kao

Hsieh

et al. 2022

Preprint

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Background: For automated analysis of metaphase chromosome images, the chromosome objects on the images need to be segmented in advance. However, the segmentation results often contain a lot of non-chromosome objects in the images. Hence, elimination of non-chromosome objects is an essential process in automated chromosome image analysis. This study aims to exclude non-chromosome objects and preserve as many chromosomes as possible. In this paper, we propose a hybrid deep learning method to exclude non-chromosome objects from metaphase chromosome images. Method: The proposed method consists of two phases. In the first phase, two classification results are obtained from feature-based and image-based convolutional neural networks (CNN) separately (the feature-based CNN uses the features of the images as input; the image-based CNN uses the images as input directly). In the second phase, the prediction results from the above two CNNs are combined and resent to another CNN to obtain final classification results. Results: The proposed method uses 18,757 non-chromosome objects and 43,398 chromosomes (including single and multiple overlapped chromosomes) from 1038 chromosome images to evaluate the performance. The experimental results show that the proposed method can detect and exclude 99.61% (18,683/18,757) of the non-chromosome objects and preserve 99.95% (43,375/43,398) of the chromosomes for further analysis. Conclusions: The proposed method has a high effectiveness on excluding non-chromosome objects and could be used as a preprocessing procedure for chromosome image analysis.

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Section: Introductionmentioning

confidence: 99%

“…In the method, a knowledge-based classifier based on the seven features is applied to delete non-chromosome objects. Uttamatanin et al [12] proposed a metaphase selection tool that classifies segmented objects into four classes based on width, height, and estimated area ratio. One (Class 4) of the four classified classes corresponds to non-chromosome objects.…”

Section: Introductionmentioning

confidence: 99%