This document describes a proposed system called R&D Cloud CEIB that would provide bioimaging R&D services using data from the Valencian Biobank Medical Imaging (GIMC) in Spain as a basis. The system would have four main modules: a search engine, clinical trials manager, anonymizer, and Bioimage Knowledge Engine (BIKE). BIKE would include submodules for post-processing, defining biomarkers, studying DICOM headers, and bioimaging classification. The goal is to provide bioimaging expertise and services to improve patient information and quality in electronic health records. The system is based on open source technologies and would analyze imaging data to generate knowledge for use in clinical services.

1. CEIB: R&D services in bioimaging oriented to
integration of environments with EHR on Cloud
Computing
Maria de la Iglesia Vayá#*1, Jose Maria Salinas#*2, Rosa Valenzuela#3, Fernando Gomez#4, Luis Martí-Bonmatí#5
# CEIB
Valencian Health Service - Spain
1
delaiglesia_mar@gva.es
2
salinas_josser@gva.es
3
valenzuela_ros@gva.es
4
fernan.m.gomez@gmail.com
5
marti_lui@gva.es
* These authors have contributed equally to this work.
Abstract— The management system and knowledge extraction of & D to the scientific community through the implementation
bioimaging in the cloud (R & D Cloud CEIB) which is proposed of logic services and retrieval determined Bioimaging sets.
in this article will use the services offered by the centralization of For the exploitation of the valuable information stored in the
bioimaging through Valencian Biobank Medical Imaging (GIMC GIMC, there was designed a logical bus of R & D services in
in Spanish) as a basis for managing and extracting knowledge
from a bioimaging bank, providing that knowledge in the form of
the cloud based on opensource technologies, which we call R
services with high added value and expertise to the Electronic & D Cloud CEIB. This is intended to provide high service
Health Records (EHR), thus bringing the results of R & D to the expertise and excellence in biomedical imaging as a portfolio
patient, improving the quality of the information contained there in this area through service-oriented architecture (SOA) which
in. R & D Cloud CEIB has four general modules: Search engine is being implemented in the AVS.
(SE), manager of clinical trials (GEBID), anonymizer (ANON) Within R & D Cloud CEIB we can find services that enable
and Bioimage knowledge Engine (BIKE). The BIKE is the data mining on the information entered from traditional
central module and through its submodules analyzes and capture devices of medical bioimaging through the headings
generates knowledge to provide to the EHR through services. of the DICOM [12] standard format; advanced post processing
The technology used in R & D Cloud CEIB is completely based
on Open Source.
bioimaging techniques through open source libraries (FSL [1],
[2][3][4][5], etc.); and different tools for the diagnosis, among
I. INTRODUCTION which is the bioimaging classifier from the optimal selection
of visual biomarkers which will be discussed later on.
The bioimaging has now become one of the most
The article continues with an overview of the centralized
innovative multidisciplinary fields of medical research given
imaging system in AVS (GIMC) and a short description of the
the important role it plays in the diagnosis of diseases. New
proposed system R & D Cloud CEIB defining each of the
needs and improved technology require us to look for the best
modules to better understand the global system. Then, it
proposals to promote diagnostic based on medical imaging
comes the classifier bioimaging, establishing the general
with the help of innovative technologies and signal analysis
features of it. In the last section, conclusions and future work
through optimization of our platforms. That is why, among
will be outlined the main objectives of the system R & D
other display systems for medical imaging diagnosis is
Cloud CEIB.
considered cuttingedge medical devices, which are due to
incorporate advanced analysis techniques to meet the II. SYSTEM OVERVIEW
expectations that society expected of public services. Among
others, the standardization of imaging biomarkers, providing In the field of bioimaging, centralized storage systems are a
increasing value to aid imaging by obtaining objective reference within the strategic framework of the AVS and the
measures to identify, measure and monitor those underlying European Community (EuroBioimage, The Euro-Bioimaging
pathophysiologic processes not detectable by the observer’s Vision “to provide a clear path of access to imaging
subjectivity. technologies for every biomedical scientist in Europe”),
In Valencian Community, the Health Information System creating a Europe-wide plan for this type of infrastructure that
of the Valencian Health Service (AVS) is a particularly large are harmonized and coordinated among all the nodes involved.
portfolio that offers an assortment of highly specialized As a result of the creation of GIMC, images from patients
solutions. The centralization of bioimaging through from the entire population of the Valencian Community
Valenciano Biobank Medical Imaging (GIMC), will support R through archiving systems and departmental image

2. transmission (PACS) will form the basis of knowledge of the CEIB that enables the provision of indexed image blocks to
the knowledge engines and clinical trial manager.
Fig. 1 Centralized imaging system of AVS.
future community science in our society through R & D
services that are presented. The architecture defined in the R
& D Cloud CEIB is defined as the following elements:
bioimaging bank, scientific community, search engine,
anonymizer, clinical trials manager and knowledge engine. In Fig. 3 Centralized imaging system of AVS (GIMC) with TELVENT solution.
the following sections we describe each of those elements.
B. Scientific community (SC)
One of the main goals pursued by the proposed system is to be
able to offer to the scientific community a basis for clinical
trials from subsets of images from the GIMC. Scientific
community can make structured requests to the GIMC.
System will provide to the scientific community tools to
manage this information as well as a set of advanced
bioimaging postprocessing tools.
C. Anonymizer (ANON)
In compliance with the Data Protection Act, all images
provided from the GIMC must be provided in an anonymised
form, always preserving the anonimization of the patient
Fig. 2 Structure of R & D Cloud CEIB information. System allows different types of anonymity,
from the alteration of the existing text information in DICOM
A. Bioimaging bank (GIMC). headers up to image-level deformation of parts that can
Y The Valencian Medical Imaging Biobank (GIMC) is the identify the patient (especially in neuroimaging obtained by
system in charge of centralized storage of all the bioimaging magnetic resonance). This part provides a restricted module,
of the AVS, having as sources all the bioimaging generated in in which the information needed to reverse the anonymization
different health centers across the Valencian Community process is stored, so that, given the event that specific needs of
through the synchronized copy of their internal PACS. The a trial, if more information is needed of the patient under
GIMC is comprised in three blocks: The storage, which study, the system will be able to provide more information
manages the optimized storage of all the images collected in about the patient.
DICOM format; the database, which manages DICOM
headers of images received through a relational database D. R & D Bioimage trials manager system (GEBID)
(storage and index); the application server, which allows The R & D bioimaging trials manager system is responsible
abstracting the system of these two previous blocks from an for providing the scientific community a platform to help
application layer that facilitates the management of biobank them to manage information from clinical trials. The GEBID
image information. The GIMC provides access to all of the is based on the implementation of a customized instance of
AVS corporate applications, such as Orion Clinic (specialized XNAT [8] (eXtensible Neuroimaging Archive Toolkit).
care management), Abucasis management (primary care) and XNAT is an open source platform designed to facilitate the
other applications through DICOM web access services management of image sets and associated data (assestments,
(WADO). GIMC also forms the storage basis for the retrieval reconstructions and any other information). Initially it is
systems (search engines) implemented in the R & D Cloud

3. designed to work with neuroimaging, but the open data model architecture to enhance parallel processing. BIKE-
and customizable XML-based technologies allow to adapt the postprocessing serves as a basis for all necessary bioimaging
platform for any type of bioimaging. XNAT follows a three- analysis in other modules of BIKE such as bioimaging
tier architecture that includes a data file, an user interface and classifier and the module of defining and quantifying
a middleware engine. The data file can be incorporated into biomarkers.
the platform through different ways, such as XML files, web
forms, DICOM transfers from image capture devices or image 2. Module of defining and quantifying biomarkers (BIKE-
viewers like Oxiris and so on. Among its most important Image)
features are the personalized safe access to information, Image biomarkers define objective features extracted from
quality control processes of data and image information, medical images, related to normal biological processes,
classification and storage of data, ability to run custom diseases or therapeutic responses. In recent years it has been
searches, communication with bioimaging generating systems, shown that imaging biomarkers provide useful complementary
programmability of process flows using scripts (pipelines), the information to traditional radiologic diagnosis to establish the
incorporation of intermediate results and conclusions to the presence of a disturbance or injury; to measure biological
study, recording of all actions taken to control and monitor status; to define its natural history and progress; stratify the
quality, etc. All these features make XNAT an ideal platform abnormal phenotypes and to evaluate the effects of treatment.
for the management of clinical trials. To develop an imaging biomarker it must be performed a
series of steps designed to validate their relationship to the
reality studied and checking its reliability, both clinical and
technical. BIKE-Image module provides all the necessary
tools to carry out effectively from simple measurements of
size or shape to the implementation of complex models. This
facilitates the definition of proof of concept and mechanism,
standardized and optimized acquisition of anatomical images,
functional and molecular, analysis of data using computer
models, adequated visualization of the results, obtaining
appropriate statistical measures, and testing of principle,
efficacy and effectiveness. BIKE-Image module used as the
basis for these processes, tools provided by the BIKE-
Postprocessing.
3. Module of study of DICOM headers (BIKE-Datamining)
Fig. 4 Bioimage trials manager system (GEBID) Within the world of medical imaging, DICOM is the
standard format used. This format, file-level, also includes the
E. Bioimaging Knowledge Engine (BIKE)
image information obtained from the radiological procedure,
The knowledge engine of the R&D Cloud CEIB (BIKE) includes in its header information in text format such as
consists of a series of modules: bioimaging postprocessing, patient demographic information, clinical information, quality
defining and quantifying biomarkers aid, study of DICOM control data of the image, technical data of the capture device
header and bioimaging classifier. We describe this modules in and image type, and many more features. BIKEDatamining
the next sections. module provides tools to exploit that information in the
DICOM headers for creating dashboards for the analysis of
1. Module of bioimaging postprocessing aid (BIKE- various indicators of quality, radiation, etc. Using these data,
Postprocessing) specialized statistical reports can be generated, which allow
the quantification and control of processes, and reporting
The digital processing of data obtained by the medical corporate structured format using the DICOM-SR.
imaging adquire machines is a field that can extract
information which is beyond the simple observation of images 4. Bioimaging classifier module (BIKE-Classifier)
on film or on monitors of the diagnostics services. The digital Starting from the definition of clinical decision support
bioimaging processing allows to precise the anatomy of the system, an image decision support system (SADI) is a
area of study and obtain functional, and even molecular, computer system that provides specific knowledge for the
information. With this service, the BIKE equips the system interpretation of medical imaging for the diagnosis purpose,
with a set of tools based in opensource graphics libraries (FSL) prognosis, treatment or management processes of care. The
that helps the bioimaging postprocessing in clinical trials features of SADI search may include findings associated with
through GEBID. These tools may be used individually or the diagnosis or prognosis of the patient, therapy planning and
grouped sequentially through process management control and operations, quality control of biomedical signals
applications such as LONI Pipelines. Given the complexity of multicenter biobanks anomalous pattern matching. The use of
many of the postprocessing techniques required for the these systems can enhance the medical skills in the
calculation of results, the system will leverage the cloud

4. management of multiple variables in biomedical care III. CONCLUSIONS
processes and help achieve balance in the health service
through the optimal use of resources and knowledge available. The GIMC generated within the AVS is an ideal data
As experiences in other communities, there is a system of source for analyzing the images acquired with all bioimaging
computer-aided diagnosis (CAD) [17] for mammography, modalities. The proposed system, R & D Cloud CEIB, will
already implemented in some hospitals in Castilla La Mancha provide these bioimages available to the community science
community, which processes images from mammography through differents tools like search engine (SE), clinical trials
generating the same analysis in which indicate the possible manager (GEBID) and knowledge engine (BIKE). BIKE
injuries that may exist, thus helping the radiologist in their provides services to perform data mining activities at the
diagnosis. BIKE includes among its modules the DICOM header (BIKE-Datamining), image postprocessing
BIKEclassifier, a classification system that allows multiple (BIKE-Postprocessing), definition and quantification of
classification in a number of existing diagnostic groups. This biomarkers (BIKE-Image) and classification (BIKEclassifier).
classification is based on an optimal selection of biomarkers The main goal of R & D Cloud CEIB is that all knowledge
and visual characteristics. This selection of biomarkers acquired in the system will move the patient through the
(Feature Selection (FS [19])) can be performed using mutual publication of web services available to the electronic medical
information. FS is a combinatorial computational complexity record system of the patient (HSE).
problem. FS Methods Must Be oriented to find suboptimal
solutions in a feasible number of iterations. The BIKE-
Classifier uses the BIKE-Image to extract biomarkers and ACKNOWLEDGMENT
other visual indicators quantified, and the BIKE- The authors would like to thanks people from
Postprocessing for the extraction of visual features that are not Quantification Quirón for their feedback.
based on biomarkers. This system performs a supervised
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