期刊
ARTIFICIAL INTELLIGENCE IN MEDICINE
卷 95, 期 -, 页码 82-87出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.artmed.2018.09.002
关键词
Compression; Pathology images; JPIP; Ki-67; Hotspot detection; Alpha shapes
资金
- National Institutes of Health [NCI 1U01CA198945-01]
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.
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