Paper Info
Title
Cloud-Based Whole Slide Image Analysis Using MapReduce.
Abstract
Systematic analysis of high resolution whole slide images enables more effective diagnosis, prognosis and prediction of cancer and other important diseases. Due to the enormous sizes and dimensions of whole slide images, the analysis requires extensive computing resources which are not commonly available. Images have to be divided into smaller regions for processing due to computer memory limitations, which will lead to inaccurate results due to the ignorance of boundary crossing objects. In this paper, we propose a highly scalable and cost effective MapReduce based image analysis framework for whole slide image processing, and provide a cloud based implementation. The framework takes a grid-based overlapping partitioning scheme, and provides parallelization of image segmentation based on MapReduce. It provides graceful handling of boundary objects with a highly efficient spatial indexing based matching method, thus avoiding loss of accuracy due to partitioning. We demonstrate that the system achieves high scalability and is cost-effective --- our experiments demonstrate that it costs less than fifteen cents to analyze one image on average using Amazon Elastic MapReduce.
Year
DOI
Venue
2016
10.1007/978-3-319-57741-8_5
DMAH@VLDB
Keywords
Field
DocType
Whole slide images, Pathology image analysis, MapReduce, Cloud computing
Data mining,Computer science,Search engine indexing,Image processing,Image segmentation,Computer memory,Database,Grid,Scalability,Cloud computing
Conference
Volume
ISSN
Citations 
10186
0302-9743
0
PageRank 
References 
Authors
0.34
3
7
Name
Order
Citations
PageRank
Hoang Vo122713.17
Jun Kong210617.74
Dejun Teng302.70
Yanhui Liang4166.50
Ablimit Aji527714.26
George Teodoro615022.18
Fusheng Wang7102679.28