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Localization of Nuclei in Breast Cancer Using Whole Slide Imaging System Supported by Morphological Features and Shape Formulas

Authors Kumar A, Prateek M

Received 3 February 2020

Accepted for publication 25 May 2020

Published 16 June 2020 Volume 2020:12 Pages 4573—4583


Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 2

Editor who approved publication: Dr Eileen O'Reilly

Anil Kumar, Manish Prateek

School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, India

Correspondence: Anil Kumar Email

Purpose: Cancer rates are exponentially increasing worldwide and over 15 million new cases are expected in the year 2020 according to the World Cancer Report. To support the clinical diagnosis of the disease, recent technical advancements in digital microscopy have been achieved to reduce the cost and increase the efficiency of the process. Food and Drug Administration (FDA or Agency) has issued the guidelines, in particular, the development of digital whole slide image scanning system. It is very helpful to the computer-aided diagnosis of breast cancer.
Methods: Whole slide imaging supported by fluorescence, immunohistochemistry, and multispectral imaging concepts. Due to the high dimension of WSI images and computation, it is a challenging task to find the region of interest (ROI) on a malignant sample image. The unsupervised machine learning and quantitative analysis of malignant sample images are supported by morphological features and shape formulas to find the correct region of interest. Due to computational limitations, it starts to work on small patches, integrate the results, and automated localize or detect the ROI. It is also compared to the handcrafted and automated region of interest provided in the ICIAR2018 dataset.
Results: A total of 10 hematoxylins and eosin (H&E) stained malignant breast histology microscopy whole slide image samples are labeled and annotated by two medical experts who are team members of the ICIAR 2018 challenge. After applying the proposed methodology, it is successfully able to localize the malignant patches of WSI sample images and getting the ROI with an average accuracy of 85.5%.
Conclusion: With the help of the k-means clustering algorithm, morphological features, and shape formula, it is possible to recognize the region of interest using the whole slide imaging concept.

Keywords: unsupervised machine learning, morphological features, shape formulas, ROI, WSI, H&E stained images, breast cancer

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