An On-Line Medical Imaging Management for Shared Research in the Web
using Pattern Features
Gustavo Molitor Porcides
Federal University of Parana - UFPR
Centro Politecnico campus, Curitiba, Brazil
Leandro Henrique Stein
Federal University of Parana - UFPR
Centro Politecnico campus, Curitiba, Brazil
Terumi Kamada
Federal University of Parana - UFPR
Centro Politecnico campus, Curitiba, Brazil
Luiz Antonio Pereira Neves
Federal University of Parana - UFPR
Centro Politecnico campus, Curitiba, Brazil
Gilson Antonio Giraldi
National Laboratory for Scientific Computing
Petropolis - RJ, Brazil
Abstract
This work aims to create a medical and biological imag-
ing management system that will be used to share images
and information between researchers via a client/server ar-
chitecture. It will be developed using the DBMS PostgreSQL
and PHP to create the web interface. In this system the im-
age attributes are automatically extracted and the pattern
features are obtained by image retrieval techniques for im-
proving the searches. These are the main advantages of the
proposed solution. Finally, we also present the development
methodology of the proposed tool.
1. Introduction
The objective of this research is the development of a
management system for medical and biological images that
includes resources for edition, registration and classification
of images. Moreover, the proposed system offers facilities
to store results of processing tasks. In addition, it allows re-
searchers to share information through the Web. The pro-
posed system uses web tools so that the data base can be re-
motely accessed via a web browser. Therefore, we get high
usability and interactivity, since web browsers are known
and portable user interfaces.
Many researchers have presented similar proposals in the
literature. For instance, Azevedo-Marques et al [2] suggests
the use of the PACS (Picture Archiving and Communica-
tion Systems), for storage, communication, processing and
edition of medical images and diagnoses in hospitals. The
proposed PACS system uses the DICOM standard for infor-
mation trade between hospitals. It is composed of a DICOM
server and web server. The use of web technologies, such as
HTML and ASP, allows a fast distribution and friendly in-
terface. The PACS utilizes a client/server architecture and
data model made in Oracle 8.1.7.0.0. and Delphi 5.
In Azevedo-Marques et al [3], authors propose another
kind of PACS for the Hospital das Clınicas from Ribeirao
Preto (FMRP-USP). This model, uses a Linux server for im-
age distribution through FTP using TCP/IP. The images are
stored in disk-arrays or CD-ROM. The authors comment
that the implementation of this system has a high cost and,
for this reason, the implementation must be well planned.
In another work, Pires et al [17] perform software de-
veloped with Delphi 6 and Interbase 6 using the BI-RADS
standard (”Breast Imaging Reporting and Data System”) for
the registration of mammographic images. This system of-
fers image visualization and training facilities for students
of the Federal University of Sao Paulo.
For image retrieval Carita et al [5] suggests the use of
the CBIR (Content Based Image Retrieval). In this case,
MySQL is used to store attributes like color, shape and tex-
ture. These features are extracted by a system component
written in C++ that will verify the images in a PACS server.
It has textual recovery with HTML and PHP and a Java soft-
ware to visualize the DICOM images.
On the other hand, d’Ornellas et al [7] suggests the use
of metadata from medical image. According to the W3C [6]
definition, metadata are information located in the web, in-
04-07 de Julho - FCT/UNESP - P. Prudente VI Workshop de Visão Computacional
36
telligible by the computer. So, the metadata is a data used
to describe a primary data. Information attached to an im-
age, or any other kind of document, are very useful for data
recovery and search in a data base. However, they may be
useless if they are not organized and structured. The use
of metadata is very complex and requires many computa-
tional resources due to the construction of its meaning. A
system of this nature is being developed by the PIGS group
and will be implemented at the Santa Maria University Hos-
pital (HUSM).
Santos M. and Furuie S. [21] propose a management sys-
tem in Java, with image processing algorithms for the stor-
age and manipulation of medical images. The authors indi-
cate an interactive architecture like an Internet portal, be-
ing useful as a tool for research, retrieval and data process-
ing. The images are stored in a PostgreSQL 8.0 database. It
can interact with other applications with distributed access
based on P2P and client/server. The proposed system sup-
ports many digital image formats, such as: DICOM, TIFF,
GIF, JPEG, BMP, etc.
Marchiori P. Z. [12] suggests the use of virtual li-
braries to improve the process of management of infor-
mation supported by data bases and web resources. Fur-
thermore, they can access other libraries by the Internet,
trading data through the use of protocols. Another ad-
vantage is the possibility of remote access, so a user
can take part of a discussions and trade data with the li-
brary.
The use of communication protocols becomes necessary
to create a connection between data bases and virtual li-
braries. A communication protocol is a convention or pat-
tern that controls and allows a connection and transference
of data between two computer systems [23]. Simply, a pro-
tocol can be defined as ”the rules that control” the syntax,
semantic and synchronization of communication. The pro-
tocols can be implemented by the hardware, software or a
combination of both. Rosetto M. [20] indicates the use of
the Z39.50, a communication protocol that allows access to
multiple systems using a single interface, with client/server
technology operating over the Internet. Its goal is to sim-
plify the manipulation of information in distributed sys-
tems.
Brito [4] proposes the use of the system called MicroI-
SIS. This system has fields that store digital images for your
records. It simplifies the management of collections because
it performs searches based on keywords. However, MicroI-
SIS has problems when dealing with large databases.
This research meets many challenges, such as: the lack
of medical image data base standards, the great variability
of image formats and the definition of a secure architecture
with the main server. Therefore, the great challenge faced
by our work is the development of a management system
for storage, manipulation, and sharing of medical and bio-
logical images using web technologies.
This work is organized as follows. The methodology is
described in section 2. Next, in the section 3, the proposed
system is analyzed. The conclusions are given in section 4.
2. Methodology
The methodology applied in this work is organized in
four stages, as shown in the Figure 1.
Figure 1. Methodology applied for the system
development.
2.1. First Stage: Definition of The Data Base Man-
agement System
The first phase consists of choosing the Data Base Man-
agement System (DBMS) that will be used in this work. In
this way, three freeware DBMS are analyzed and compared
accordingly to the maximum table load and its features.
2.2. Second Stage: Data Modeling
The second stage is the definition of the data model, us-
ing the UML tool to describe the input and output and rela-
tional modeling to identify all the system’s information.
2.3. Third Stage: Implementation
The third stage consists of the data model implementa-
tion, using the tools defined in stage 1 and the analyzes of
the features of three biotechnology web data bases, NCBI
(National Center for Biotechnology Information) [11], Soft-
Berry [22] and Addgene [1].
04-07 de Julho - FCT/UNESP - P. Prudente VI Workshop de Visão Computacional
37
2.4. Fourth Stage: Validation Tests
In the fourth stage tests are made to validate the sys-
tem. For this phase, the validation protocol is defined via
the methods proposed by Pressmann [19] and by the use of
a checklist evaluation to identify the user’s perception of the
system.
3. Analysis of Obtained Results
In this section the results of the methodology are pre-
sented.
3.1. Results of the Data Base Management System
In this section the features of MySQL [16], FirebirdSQL
[8] and PostgreSQL [18] are shown and comparisons are
made between them.
MySQL is a DBMS written in C and C++, multitask, fo-
cused in threads [6], multiuser, optimized for web appli-
cations, specially if used with PHP. It’s easy to use and
it is portable to most computer platform with support for
several programming languages. It has an excellent perfor-
mance and stability, and it can be used in critical mission
systems [14].
PostgreSQL is client/server DBMS and has transactions,
triggers, views, foreign key referential integrity and locking
[6].
FirebirdSQL is a high performance relational DBMS
with trigger and procedures support [9].
In table 1 the maximum table load sizes of the three an-
alyzed DBMS are reported [13] [10] [15].
Maximum Table Load PostgreSQL MySQL FirebirdSQL
Windows 32TB 2TB 32TB
Linux 32TB 4TB 32TB
Table 1. Maximum Table Load
According to Table 1, PostgreSQL and FirebirdSQL has
support to greater table loads, offering up to 32TB of phys-
ical space for each table
As shown by Chen and Xie [6] PostgreSQL 8.2 supports
more features than MySQL 5.0 and Firebird 2.0. It sup-
ports associated integrity, database transactions, unicode,
indexes, temporary tables, table partion and clusters. Al-
though MySQL supports many of these features, it doesn’t
have GiST index support. Firebird supports only associated
integrity, database transactions and Unicode.
Therefore, by using the information shown in table 1
and by analyzing the features that these data base manage-
ment systems supports, PostgreSQL has been chosen as the
DBMS to be used in this project due to a greater support to
all the features required for the implementation of the pro-
posed system.
3.2. Results of the Image Manager’s Data Model-
ing
The data base has been modeled in a way that users are
divided in several levels by the actions of the administra-
tor as moderator. Each one has an access level with its priv-
ileges, such as remove, add, modify or only visualize the
images. Non-registered users can only view the images. On
the registration, the user must insert his name, address, in-
stitution, password and Social Security Number.
The image attributes are width, height, resolution, type
of compression, format and the quantity and type of chan-
nels. These attributes may be used to index the images.
These attributes will be extracted automatically during the
upload.
We have studied CBIR (Content-Based Image Retrieval)
algorithms as alternative to help the search for images. The
algorithm analyzes the actual contents of the image, such as
keywords, tags and descriptions, rather than the metadata.
The contents analyzed are colors, shapes, textures, or any
other information that can be derived from the image itself.
The main tables are ”image” and ”historical”. The first
one will keep all the data about the images. The second one
will combine the information about the user and operations
to have a complete historical of the data base used. Figure 2
shows the relational model.
3.3. Implementation of the Proposed System
During stage 3, a friendly user interface has been imple-
mented, which looks like the ones used by the biotechnol-
ogy information bases NCBI (National Center for Biotech-
nology Information) [11], SoftBerry [22] and Addgene [1].
NCBI and Addgene have a keyword search engine that sim-
plifies the access to the content that the user wants to view.
They also have menus divided in types categories as pro-
teins and genes. NCBI has a ”How To” page that shows to
navigate through the web site and access the content in an
easy and understandable way. Moreover, NCBI has a regis-
tration module that allows registered members to save their
researches, results, citations and offers several search fil-
ters and other benefits. In SoftBerry’s main page, the links
to the latest case studies are shown, to make the access eas-
ier. These websites are shown in Figures 3, 4, and 5.
These functionalities are implemented in the proposed
system to facilitate the interaction between the researcher
and the system, offering a pleasant, organized and objec-
tive environment.
04-07 de Julho - FCT/UNESP - P. Prudente VI Workshop de Visão Computacional
38
Figure 2. Relational Model of the Proposed System.
Figure 3. NCBI Webpage.
3.4. Tests of Validation
For the system’s validation, three test methods proposed
by Pressman [19] will be used: black box test, white box test
and real-time test. The black box test verifies if the input is
adequately accepted and the output is correctly produced,
moreover it verifies if the external information integrity is
maintained. Three test methodologies are used. The equiva-
lency partitioning divides the output domain in equivalency
classes for tests. This minimizes the test cases, limiting each
class to one case. The limit value analysis verifies the neigh-
boring values, since many errors may occur in the input lim-
its of a module.
The white box test aims to define test cases that exercise
specific blocks of code of the web interface. The control
structure test verifies the logical conditions, the data flux
test takes the variables locations to define several test paths
and the execution paths that are tested.
The real-time test takes into account the actions timing
and aims to determine the reaction of the systems in sev-
eral states that vary with the time and can make the results
obtained vary too. The real-time tests are divided into four
stages. The first one, called task test, each task is tested
with white and black box test individually, revealing logi-
cal and function problems, but not behavioral or timing er-
rors. During the second stage the system’s behavior is simu-
04-07 de Julho - FCT/UNESP - P. Prudente VI Workshop de Visão Computacional
39
Figure 4. SoftBerry Webpage.
Figure 5. Addgene Webpage.
lated using CASE (Computer-Aided software Engineering)
tools to test the system’s behavior as a consequence of exter-
nal events. These events are tests to detect errors and flaws.
After that they are tested in random sequences and frequen-
cies. The intertask test, the third stage, is realized after the
detection of behavioral and individual tasks errors. It aims
to detect timing errors. Several tasks communicate among
themselves with varying data and processing loads to de-
tect synchronization errors. In the last stage, the software
and hardware are integrated and then several tests are made
to discover errors in the hardware/software interface.
Besides these methods, checklist evaluation will be made
to identify the acceptance rate of the system. This evalua-
tion will made based on several questions about the system,
as shown if Figure 6.
4. Conclusions
This research presents a system for image sharing among
researchers in the Web, using feature patterns from images.
Figure 6. Proposed Checklist.
Currently, the web interface is being developed using
PHP language and integrated with PostgreSQL database
management as illustrated in figure 7.
Figure 7. Proposed Web Application.
The proposed research has advantages over others be-
cause the attributes are extracted automatically, the pattern
features are obtained by image retrieval techniques and im-
age database is public for any researchers in Digital Image
Processing.
Therefore, this project innovates by allowing image re-
search using its attributes and metadata, what makes the
search more efficient and effective. Furthermore, the im-
ages stored in the proposed system are registered with a
historical that simplifies the analysis of empirical results.
In future studies, several other functionalities will be im-
plemented, such as algorithms for image manipulation and
Content-Based Image Retrieval for improving the searches.
References
[1] Addgene. Available on http://www.addgene.org/pgvec1. ac-
cessed on april 21th, 2010.
[2] P. M. Azevedo-Marques, E. C. Carita, A. A. Benedicto, and
P. R. Sanches. Implantacao de um ris/pacs no hospital das
04-07 de Julho - FCT/UNESP - P. Prudente VI Workshop de Visão Computacional
40
clınicas de ribeirao preto: Uma solucao baseada em web. Ra-
diol Bras 2005, Sao Paulo, pages 37–43, 2005.
[3] P. M. Azevedo-Marques, C. S. Trad, J. E. Junior, and A. C.
Santos. Implantacao de um mini-pacs (sistema de arquiva-
mento e distribuicao de imagens) em hospital universitario.
Radiol Bras 2001, Sao Paulo, pages 221–224, 2001.
[4] C. J. Brito. Gerencia de bases de imagens usando microisis.
[5] E. C. Carita, E. Seraphim, M. O. Honda, and P. M. Azevedo-
Marques. Implementaca e avaliacao de um sistema de geren-
ciamento de imagens medicas com suporte a recuperacao
baseada em conteudo. Radiol Bras 2008, Sao Paulo, pages
331–336, 2008.
[6] R. Chen and J. Xie. Open Source Approaches In Spatial Data
Handling. Springer, New York.
[7] M. C. d’Ornellas, S. R. Mussoi, and A. P. Dias. Avaliacao
e gerenciamento de qualidade de metadados de imagens
medicas. XVIII Congresso Brasileiro de Engenharia
Biomedica Santa Maria, 2004.
[8] FirebirdSQL. Available on http://www.firebirdsql.org/. ac-
cessed on april 25th, 2010.
[9] FirebirdSQL. Available on
http://www.firebirdsql.org/index.php?id=about-
firebird&nosb=1. accessed on april 26th, 2010.
[10] FirebirdSQL. Available on
http://www.firebirdsql.org/index.php?op=guide&id=techspec.
accessed on may 4th, 2010.
[11] N. N. C. for Biotechnology Information. Available on
http://www.ncbi.nlm.nih.gov/. accessed on april 21th, 2010.
[12] P. Z. Marchiori. Ciberteca ou biblioteca virtual: Uma per-
spectiva de gerenciamento de recursos de informacao. 1997.
[13] A. Milani. PostgreSQL : Guia do Programador. Novatec,
Sao Paulo, 2008.
[14] MySQL. Available on
http://dev.mysql.com/doc/refman/5.0/en/features.html .
accessed on april 26th, 2010.
[15] MySQL. Available on
http://dev.mysql.com/doc/refman/5.0/en/full-table.html.
accessed on may 4th, 2010.
[16] MySQL. Available on http://www.mysql.com/. accessed on
april 25th, 2010.
[17] S. R. Pires, R. B. Medeiros, and H. Schiabel. Banco de ima-
gens mamograficas para treinamento na interpretacao de im-
agens. Radiol Bras 2004, Sao Paulo, pages 239–244, 2004.
[18] PostgreSQL. Available on http://www.postgresql.org/. ac-
cessed on april 25th, 2010.
[19] R. S. Pressman. Engenharia de Software. Pearson Makron
Books, Sao Paulo, 1995.
[20] M. Rosetto. Uso do protocolo z39.50 para recupercao de
informcoes em redes eletronicas. 1997.
[21] M. Santos and S. S. Furuie. Base de imagens para avaliacao
de algoritmos de processamento de imagens medicas. IV
SBQS - V Workshop de Informatica Medica, 2005.
[22] SoftBerry. Available on
http://linux1.softberry.com/berry.phtml. accessed on april
21th, 2010.
[23] L. B. Sousa. Redes de Computadores: Guia Total. Editora
Erica, Sao Paulo, 2009.
04-07 de Julho - FCT/UNESP - P. Prudente VI Workshop de Visão Computacional
41
Top Related