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.
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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]