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Nuclei Segmentation for Quantification of Brain Tumors in Digital Pathology Images

Nuclei Segmentation for Quantification of Brain Tumors in Digital Pathology Images

Peifang Guo, Alan Evans, Prabir Bhattacharya
Copyright: © 2018 |Volume: 10 |Issue: 2 |Pages: 14
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781522544036|DOI: 10.4018/IJSSCI.2018040103
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MLA

Guo, Peifang, et al. "Nuclei Segmentation for Quantification of Brain Tumors in Digital Pathology Images." IJSSCI vol.10, no.2 2018: pp.36-49. http://doi.org/10.4018/IJSSCI.2018040103

APA

Guo, P., Evans, A., & Bhattacharya, P. (2018). Nuclei Segmentation for Quantification of Brain Tumors in Digital Pathology Images. International Journal of Software Science and Computational Intelligence (IJSSCI), 10(2), 36-49. http://doi.org/10.4018/IJSSCI.2018040103

Chicago

Guo, Peifang, Alan Evans, and Prabir Bhattacharya. "Nuclei Segmentation for Quantification of Brain Tumors in Digital Pathology Images," International Journal of Software Science and Computational Intelligence (IJSSCI) 10, no.2: 36-49. http://doi.org/10.4018/IJSSCI.2018040103

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Abstract

In this article, based on image transformation of HSV (Hue, Saturation, Value), the authors propose a method for cancer nuclei segmentation when such conflicts of cancer nuclei involve ‘omics' indicative of brain tumors pathologically. To constrain the problem space in the region of color information, i.e. cancer nuclei, they convert the images into the V component of HSV first, and then apply the threshold level-set segmentation and the sparsity technique (VTLS-ST) in segmentation. The combined technique of the proposed VTLS-ST is implemented using the real-time CBTC dataset in the validation stage. The proposed method exhibits an improved capability of searching recursively for the optimal threshold level-set in the working subsets via the sparsity representation in segmentation. The experimental results show the reliability and efficiency of the proposed approach in real-time applications with an average rate of 0.932 in terms of similarity index for segmentation of cancer nuclei in brain tumor detection.

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