A cloud solution for medical image processing

The rapid growth in the use of Electronic Health Records across the globe along with the rich mix of multimedia held within an EHR combined with the increasing level of detail due to advances in diagnostic medical imaging means increasing amounts of data can be stored for each patient. Also lack of image processing and analysis tools for handling the large image datasets has compromised researchers and practitioner‟s outcome. Migrating medical imaging applications and data to the Cloud can allow healthcare organizations to realize significant cost savings relating to hardware, software, buildings, power and staff, in addition to greater scalability, higher performance and resilience. This paper reviews medical image processing and its challenges, states cloud computing and cloud computing benefits due to medical image processing. Also, this paper introduces tools and methods for medical images processing using the cloud. Finally a method is provided for medical images processing based on Eucalyptus cloud infrastructure with image processing software “ImageJ” and using improved genetic algorithm for the allocation and distribution of resources. Based on conducted simulations and experimental results, the proposed method brings high scalability, simplicity, flexibility and fully customizability in addition to 40% cost reduction and twice increase in speed.

[1]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[2]  Azween Abdullah,et al.  E-healthcare and data management services in a cloud , 2011, 8th International Conference on High-capacity Optical Networks and Emerging Technologies.

[3]  Mohamed Jmaiel,et al.  A Comparative Study of the Current Cloud Computing Technologies and Offers , 2011, 2011 First International Symposium on Network Cloud Computing and Applications.

[4]  Lei Ye,et al.  Medical Information Integration Based Cloud Computing , 2011, 2011 International Conference on Network Computing and Information Security.

[5]  Chen-Khong Tham,et al.  Evolutionary Optimal Virtual Machine Placement and Demand Forecaster for Cloud Computing , 2011, 2011 IEEE International Conference on Advanced Information Networking and Applications.

[6]  Sally McClean,et al.  Cloud-based Healthcare: Towards a SLA Compliant Network Aware Solution for Medical Image Processing , 2012 .

[7]  Kai Zhu,et al.  Hybrid Genetic Algorithm for Cloud Computing Applications , 2011, 2011 IEEE Asia-Pacific Services Computing Conference.

[8]  Hiroyasu Nagata,et al.  Formal: A Language with a Macro-Oriented Extension Facility , 1980, Comput. Lang..

[9]  Justin Smith Inside microsoft windows® communication foundation , 2007 .

[10]  Evan C. Crawford,et al.  An ImageJ plugin for the rapid morphological characterization of separated particles and an initial application to placer gold analysis , 2009, Comput. Geosci..

[11]  Marco Dorigo,et al.  Ant colony optimization theory: A survey , 2005, Theor. Comput. Sci..

[12]  Augusto Silva,et al.  A PACS Gateway to the Cloud , 2011, 6th Iberian Conference on Information Systems and Technologies (CISTI 2011).

[13]  W. Batchelor,et al.  Shape identification and particles size distribution from basic shape parameters using ImageJ , 2008 .

[14]  Xuejie Zhang,et al.  An Approach to Optimized Resource Scheduling Algorithm for Open-Source Cloud Systems , 2010, 2010 Fifth Annual ChinaGrid Conference.

[15]  Chengzhang Peng,et al.  Building a Cloud Storage Service System , 2011 .

[16]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[17]  Chia-Chi Teng,et al.  A medical image archive solution in the cloud , 2010, 2010 IEEE International Conference on Software Engineering and Service Sciences.

[18]  Wen-Chung Chiang,et al.  Bulding a cloud service for medical image processing based on service-orient archtecture , 2011, 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI).

[19]  Mahamat Issa Hassan,et al.  Cloud resource broker in the optimization of medical image retrieval system: A proposed goal-based request in medical application , 2011, 2011 National Postgraduate Conference.