Pathological image compression for big data image analysis: Application to hotspot detection in breast cancer
MetadataShow full item record
PublisherELSEVIER SCIENCE BV
CitationNiazi, M. K. K., Lin, Y., Liu, F., Ashok, A., Marcellin, M. W., Tozbikian, G., ... & Bilgin, A. (2019). Pathological image compression for big data image analysis: Application to hotspot detection in breast cancer. Artificial intelligence in medicine, 95, 82-87.
Rights© 2018 Elsevier B.V. All rights reserved.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractIn 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.
Note12 month embargo; published online: 25 September 2018
VersionFinal accepted manuscript
SponsorsNational Institutes of Health [NCI 1U01CA198945-01]
- [JPIP-based wireless transmission and display of high resolution DICOM medical images].
- Authors: Tian Y, Zhang JG
- Issue date: 2006 Jul
- Effect of image compression and scaling on automated scoring of immunohistochemical stainings and segmentation of tumor epithelium.
- Authors: Konsti J, Lundin M, Linder N, Haglund C, Blomqvist C, Nevanlinna H, Aaltonen K, Nordling S, Lundin J
- Issue date: 2012 Mar 21
- Lossless compression of microarray images using image-dependent finite-context models.
- Authors: Neves AJ, Pinho AJ
- Issue date: 2009 Feb
- Linking whole-slide microscope images with DICOM by using JPEG2000 interactive protocol.
- Authors: Tuominen VJ, Isola J
- Issue date: 2010 Aug
- Digital image compression.
- Authors: Seeram E
- Issue date: 2005 Jul-Aug