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dc.contributor.authorChen, H
dc.contributor.authorHo, B
dc.contributor.authorWang, H
dc.contributor.authorTan, SH
dc.contributor.authorZhao, CX
dc.contributor.authorNguyen, NT
dc.contributor.authorGao, Y
dc.contributor.authorZhou, J
dc.date.accessioned2020-03-19T05:58:23Z
dc.date.available2020-03-19T05:58:23Z
dc.date.issued2019
dc.identifier.isbn9781728152943
dc.identifier.issn2158-6276
dc.identifier.doi10.1109/WHISPERS.2019.8921136
dc.identifier.urihttp://hdl.handle.net/10072/392482
dc.description.abstractClassification of live and dead human ovarian cancer cells (SKOV3) is an important task in biomedicine research. Fluorescence is the most common technique to distinguish live and dead cells. However, it requires physical contact to the cells, which affects the appearance of the cells and their features to a certain degree. There is an urgent need to develop a novel non-invasive technology for this application. This paper proposes the first hyperspectral image (HSI) based system to address this issue. A microscopic hyperspectral imaging system is built to capture cell images. Then morphological methods are employed to extract spectral-spatial features for an SVM classifier that is capable of automatically distinguishing live and dead SKOV3 cells. We show that tensor morphology profile (TMP) leads to the best discriminative capability when compared to other features. At the same time, our work also shows that HSI works better than RGB images for this novel application thanks to its fine spectral information.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherIEEE
dc.relation.ispartofconferencename10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS 2019)
dc.relation.ispartofconferencetitleWorkshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
dc.relation.ispartofdatefrom2019-09-24
dc.relation.ispartofdateto2019-09-26
dc.relation.ispartoflocationAmsterdam, Netherlands
dc.relation.ispartofpagefrom5 pages
dc.relation.ispartofpageto5 pages
dc.relation.ispartofvolume2019-September
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchcode0801
dc.titleAutomatic Live and Dead Cell Classification via Hyperspectral Imaging
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationChen, H; Ho, B; Wang, H; Tan, SH; Zhao, CX; Nguyen, NT; Gao, Y; Zhou, J, Automatic Live and Dead Cell Classification via Hyperspectral Imaging, Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing, 2019, 2019-September
dc.date.updated2020-03-19T05:56:11Z
dc.description.versionPost-print
gro.rights.copyright© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
gro.hasfulltextFull Text
gro.griffith.authorZhou, Jun
gro.griffith.authorNguyen, Nam-Trung
gro.griffith.authorGao, Yongsheng
gro.griffith.authorChen, He
gro.griffith.authorHo, Benjamin
gro.griffith.authorTan, Say Hwa H.


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