Correlation Filters for Detection of Cellular Nuclei in Histopathology Images.
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...
| Publicado en: | Journal of Medical Systems Vol. 42; no. 1; pp. 1 - 9 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jan2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=127145173&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 127145173 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Jan2018 vid: 42 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 127145173 127145173 127145173 10.1007/s10916-017-0863-8 127145173 ppf: 1 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Correlation Filters for Detection of Cellular Nuclei in Histopathology Images. aug: au: Ahmad, Asif Asif, Amina Rajpoot, Nasir Arif, Muhammad Minhas, Fayyaz ul Amir Afsar affil: Biomedical Informatics Research Laboratory, Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, PO Nilore, Islamabad, Pakistan sug: subj: Cell Physiology Image Interpretation, Computer Assisted Human Image Processing, Computer Assisted Colorectal Neoplasms ab: 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: . pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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