Pathological image compression for big data image analysis: Application to hotspot detection in breast cancer.
In this paper, we propose a pathological image compression framework to address the needs of Big Data image analysis in digital pathology. Big Data image analytics require analysis of large databases of high-resolution images using distributed storage and computing resources along with transmission...
| Publicado en: | Artificial Intelligence in Medicine Vol. 95; pp. 82 - 88 |
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| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Elsevier B.V.
Apr2019
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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=135437556&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135437556 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09333657 3HY jtl: Artificial Intelligence in Medicine issn: 09333657 maglogo: N pubinfo: dt: Apr2019 vid: 95 pid: 1004 pub: Elsevier B.V. artinfo: ui: 135437556 135437556 NLM30266546 10.1016/j.artmed.2018.09.002 NLM30266546 135437556 ppf: 82 ppct: 6 formats: tig: atl: Pathological image compression for big data image analysis: Application to hotspot detection in breast cancer. aug: au: Niazi, M. Khalid Khan Lin, Y. Liu, F. Ashok, A. Marcellin, M.W. Tozbikian, G. Gurcan, M.N. Bilgin, A. affil: Center for Biomedical Informatics, Wake Forest School of Medicine, Winston-Salem, NC, USA sug: subj: Image Processing, Computer Assisted Methods Breast Neoplasms Diagnosis Female Information Retrieval Arthritis Impact Measurement Scales Female ab: In this paper, we propose a pathological image compression framework to address the needs of Big Data image analysis in digital pathology. Big Data image analytics require analysis of large databases of high-resolution images using distributed storage and computing resources along with transmission of large amounts of data between the storage and computing nodes that can create a major processing bottleneck. The proposed image compression framework is based on the JPEG2000 Interactive Protocol and aims to minimize the amount of data transfer between the storage and computing nodes as well as to considerably reduce the computational demands of the decompression engine. The proposed framework was integrated into hotspot detection from images of breast biopsies, yielding considerable reduction of data and computing requirements. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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