Piotr Szul

Galaxy + Hadoop: Toward a Collaborative and Scalable Image Processing Toolbox in Cloud

With emergence and adoption of cloud computing, cloud has become an effective collaboration platform for integrating various software tools to deliver as services. In this paper, we present a cloud-based image processing toolbox by integrating Galaxy, Hadoop and our proprietary image processing tools. This toolbox allows users to easily design and execute complex image processing tasks by sharing various advanced image processing tools and scalable cloud computation capacity. The paper provides the integration architecture and technical details about the whole system. In particular, we present our investigations to use Hadoop to handle massive image processing jobs in the system. A number of real image processing examples are used to demonstrate the usefulness and scalability of this class of data-intensive applications.

Image Classification to Support Emergency Situation Awareness

Recent advances in image classification methods, along with the availability of associated tools, has seen their use become widespread in many domains. This paper presents a novel application of current image classification approaches in the area of emergency situation awareness. We discuss image classification based on low level features as well as methods built on top of pre-trained classifiers. The performance of the classifiers are assessed in terms of accuracy along with consideration to computational aspects given the size of the image database. Specifically, we investigate image classification in the context of a bush fire emergency in the Australian state of NSW where images associated with Tweets during the emergency were used to train and test classification approaches. Emergency service operators are interested in having images relevant to such fires reported as extra information to help manage evolving emergencies. We show that these methodologies can classify images into fire and not fire related classes with an accuracy of 86%.

Productivity Frameworks in Big Data Image Processing Computations - Creating Photographic Mosaics with Hadoop and Scalding☆

Abstract In the last decade, Hadoop has become a de-facto standard framework for big data processing in the industry. Although Hadoop today is primarily applied to textual data, it can be also used to process binary data including images. A number of frameworks have been developed to increase productivity of developing Hadoop based solutions. This paper demonstrates how such a framework (Scalding) can be used to create a concise and efficient solution to a big data image-processing problem of creating photographic mosaics and compares it to a Hadoop API based implementation.

Biomedical image analysis and processing in clouds

Cloud-based Image Analysis and Processing Toolbox project runs on the Australian National eResearch Collaboration Tools and Resources (NeCTAR) cloud infrastructure and allows access to biomedical image processing and analysis services to researchers via remotely accessible user interfaces. By providing user-friendly access to cloud computing resources and new workflow-based interfaces, our solution enables researchers to carry out various challenging image analysis and reconstruction tasks. Several case studies will be presented during the conference.

Cloud based toolbox for image analysis, processing and reconstruction tasks.

This chapter describes a novel way of carrying out image analysis, reconstruction and processing tasks using cloud based service provided on the Australian National eResearch Collaboration Tools and Resources (NeCTAR) infrastructure. The toolbox allows users free access to a wide range of useful blocks of functionalities (imaging functions) that can be connected together in workflows allowing creation of even more complex algorithms that can be re-run on different data sets, shared with others or additionally adjusted. The functions given are in the area of cellular imaging, advanced X-ray image analysis, computed tomography and 3D medical imaging and visualisation. The service is currently available on the website www.cloudimaging.net.au .

Cloud Based Toolbox for Image Analysis, Processing and Reconstruction Tasks

This chapter describes a novel way of carrying out image analysis, reconstruction and processing tasks using cloud based service provided on the Australian National eResearch Collaboration Tools and Resources (NeCTAR) infrastructure. The toolbox allows users free access to a wide range of useful blocks of functionalities (imaging functions) that can be connected together in workflows allowing creation of even more complex algorithms that can be re-run on different data sets, shared with others or additionally adjusted. The functions given are in the area of cellular imaging, advanced X-ray image analysis, computed tomography and 3D medical imaging and visualisation. The service is currently available on the website www.cloudimaging.net.au .

Cloud Based Toolbox to Carry Out Image Analysis, Processing and Reconstruction Tasks

This chapter describes a novel way of carrying out image analysis, reconstruction and processing tasks using cloud based service provided on the Australian National eResearch Collaboration Tools and Resources (NeCTAR) infrastructure. The toolbox allows users free access to a wide range of useful blocks of functionalities (imaging functions) that can be connected together in workflows allowing creation of even more complex algorithms that can be re-run on different data sets, shared with others or additionally adjusted. The functions given are in the area of cellular imaging, advanced X-ray image analysis, computed tomography and 3D medical imaging and visualisation. The service is currently available on the website www.cloudimaging.net.au .

Cloud Computing for High Performance Image Analysis on a National Infrastructure

Cloud computing services offer highly reliable, scalable and efficient solutions with a large pool of easily accessible, virtualized resources. They are becoming an increasingly prevalent delivery model. We have developed a cloud-based image analysis toolbox to provide a wide user base with easy access to the software tools we have developed over the last decade. The toolbox is provided as a service on an Australian national cloud infrastructure. The design and implementation of the cloud-based service are presented, including its architecture, key components and some image analysis and visualization examples showing the capabilities of the service for biomedical image analysis.