Pathological image compression for big data image analysis: Application to hotspot detection in breast cancer

M. Khalid Khan Niazi, Y. Lin, F. Liu, A. Ashok, M. W. Marcellin, G. Tozbikian, M. N. Gurcan, A. Bilgin

Research output: Contribution to journalArticle

2 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)82-87
Number of pages6
JournalArtificial Intelligence in Medicine
StatePublished - Apr 2019



  • Alpha shapes
  • Compression
  • Hotspot detection
  • JPIP
  • Ki-67
  • Pathology images

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Artificial Intelligence

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