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Correlation filters for detection of cellular nuclei in histopathology images
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Ahmad, Asif, Asif, Amina, Rajpoot, Nasir, Arif, Muhammad and Minhas, Fayyaz ul Amir Afsar (2018) Correlation filters for detection of cellular nuclei in histopathology images. Journal of Medical Systems, 42 (7). doi:10.1007/s10916-017-0863-8
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Official URL: http://dx.doi.org/10.1007/s10916-017-0863-8
Abstract
Nuclei detection in histology images is an essential part of computer aided diagnosis of cancers and tumors. It is a challenging task due to diverse and complicated structures of cells. In this work, we present an automated technique for detection of cellular nuclei in hematoxylin and eosin stained histopathology images. Our proposed approach is based on kernelized correlation filters. Correlation filters have been widely used in object detection and tracking applications but their strength has not been explored in the medical imaging domain up till now. Our experimental results show that the proposed scheme gives state of the art accuracy and can learn complex nuclear morphologies. Like deep learning approaches, the proposed filters do not require engineering of image features as they can operate directly on histopathology images without significant preprocessing. However, unlike deep learning methods, the large-margin correlation filters developed in this work are interpretable, computationally efficient and do not require specialized or expensive computing hardware. Availability: A cloud based webserver of the proposed method and its python implementation can be accessed at the following URL: http://faculty.pieas.edu.pk/fayyaz/software.html#corehist.
| Item Type: | Journal Article | |||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QH Natural history R Medicine > RB Pathology |
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| Divisions: | Faculty of Science > Computer Science | |||||||||
| Library of Congress Subject Headings (LCSH): | Histology, Pathological -- Imaging, Cells—Morphology -- Analysis, Machine learning | |||||||||
| Journal or Publication Title: | Journal of Medical Systems | |||||||||
| Publisher: | Springer New York LLC | |||||||||
| ISSN: | 0148-5598 | |||||||||
| Official Date: | January 2018 | |||||||||
| Dates: |
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| Date of first compliant deposit: | 5 November 2019 | |||||||||
| Volume: | 42 | |||||||||
| Number: | 7 | |||||||||
| DOI: | 10.1007/s10916-017-0863-8 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Publisher Statement: | This is a post-peer-review, pre-copyedit version of an article published in Journal of Medical Systems. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10916-017-0863-8 | |||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||
| RIOXX Funder/Project Grant: |
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