USING A COMPLEX NUMERICAL AND MULTIMEDIA

DATABASE IN INTERNAL MEDICINE

Pre-release Evaluations

Liana Stanescu, Dumitru Burdescu, Cosmin Stoica-Spahiu, Anca Ion

University of Craiova, Faculty of Automation, Computers and Electronics, Romania

Dorin Stanescu

S. C. EDA SOFT, Craiova, Romania

Keywords: Medical application, medical images, color and texture features, content-based image query.

Abstract: The paper presents a software tool implemented using Firebird and Delphi technologies, dedicated for

managing and querying medical multimedia databases. The database contains images related to the internal

medicine area. This on-line application allows creation of complex medical files of patients that can be

viewed and updated both by internist and general practitioner. The main functions of the application are:

managing patients contact information, examinations, imagery and personal folders; simple text based

query; content based query using color characteristic for images provided by medical devices. It can be used

in individual offices, laboratories or in the hospital clinics and departments. The application provides

security and confidentiality for patient’s data.

1 INTRODUCTION

Internal medicine is an important component of the

general medicine that can be regarded as basics for

many specialties: cardiology, pneumology,

gastroenterology, nephrology, haematology,

rheumatology, etc. It needs large amounts of

paraclinical exploration performed by different

devices that generates visual and numerical data

used for diagnosis and for follow-up of treatment or

evolution. Numerical information is given by

functional and biological assays while various

imagistic devices provide image data. In his reports,

the doctor is making qualitative descriptions of

abnormalities he found and elaborates reports that

will be forwarded to the patients or to his colleagues

from other departments. That is why internal

medicine departments usually accumulate huge

quantities of medical data, including thousands of

image files, millions of numerical values and

thousands of written reports.

Because most of the patients in these services are

chronically ill patients, with frequent visits and

frequent use ambulatory services, the rapid access to

an archive containing both numerical and imagistic

data would be an advantage. More than that, because

access to high tech medical services is quite limited

in some countries, many patients are frequently

investigated in geographically dispersed medical

centres. By using a dedicated application, the local

doctor will be able to check simultaneously all the

investigations and to give a diagnosis. Thus, he can

integrate better all diagnostic data, manage the visits

or the therapy taking into account all the

concomitant pathology, or send the patient to

another department for other investigations. As

consequence, other specialists are able to see

information from local or regional database and can

query the archives.

These are some reasons for creating a complex

application for managing and querying a database

containing information and images from medical

domain. Our database is implemented in Firebird

(Interbase) that is a free and modern database

management systems (Interbase, 2006).

The application is implemented using Delphi

programming language that has important facilities

for database management (Kermann, 2001). It can

192

Stanescu L., Burdescu D., Stoica-Spahiu C., Ion A. and Stanescu D. (2008).

USING A COMPLEX NUMERICAL AND MULTIMEDIA DATABASE IN INTERNAL MEDICINE - Pre-release Evaluations.

In Proceedings of the First International Conference on Health Informatics, pages 192-195

 SciTePress

be used on-line, so the users have access from

distance.

Besides managing patient information, including

their consultations, the application has the possibility

for managing and viewing the images provided by

medical devices.

An element of originality is content-based visual

query using color characteristic. It permits selection

of a query image and finding all similar images from

the database (Del Bimbo, 2001; Smith, 1997). This

option could be very helpful for establishing the

diagnosis.

2 THE DATABASE STRUCTURE

In this section, database structure used by the

application including tables and logical connections

between them is presented in detail. The database

contains a number of tables populated when

installing the application. The tables contain a series

of codes that makes easier the work of updating

patient information and the investigations. These

tables are:

- Medical units with a specific code and name:

the medical units can be an individual office, a

laboratory or hospital clinics and departments.

- Users groups: this table stores information

about user groups, administration rights for

each group (administrators, doctors, nurses

etc.). The table also contains a group identifier

and a group name that can be specified for each

group. Each unit has specific management

rights.

- Users: is a table that contains user

identification, name, password, and

corresponding group.

The data confidentiality is ensured by user name

and password that are provided separately for each

unit. In order to increase the data security, the

password is encrypted. Each doctor has access to

information regarding his own patients, but he can

share some data that can be seen by other specialists.

He can also access both statistical and scientific data

regarding all patients in database but in this case the

identification data about patients is hidden and the

ID number, name and address are blinded. At

information management level, anonimizing all

information concerning clinical data ensures

confidentiality; diagnosis, paraclinical information,

treatments are also blinded and only statistical data

can be viewed. On the other hand, the office

secretary can see personal information about patients

and referring doctors, but no diagnosis and treatment

elements are accessible.

- Diagnosis table is used for storing the diagnosis

code and name.

- Analyses table codifies paraclinical and

biological data. It has the following structure:

code, description, and minimal and maximal

value.

- Clinical examination table includes the

following elements: code, description, and

value.

- Patient groups include code and description.

The patients can be grouped by the category of

disease (digestive, cardiology, renal etc.), by

the medical insurance category or by

participation to different programs (national

health programs or clinical studies).

The following tables are the most important in

the database because they store information about

patients, examinations, investigations and results:

- Patients table is used for storing information

about patient’s visits: personal ID number,

name, doctor, county, city, address, phone/fax

number, email and program – if any.

- A patient might have several examinations, for

each of them storing in the Consulting table,

the diagnosis, date and treatment. Each

examination might contain one or several

clinical examinations (it is stored code,

description, a series of analyses identified by

code and obtained value).

- Images table is storing information about still

or moving images, obtained from a patient

during his whole disease history. These data

are: path and name of the image file, type of

image and color information automatically

extracted for later content-based image query

on color feature.

3 THE FUNCTIONS OF THE

APPLICATIONS

3.1 Set-up

This function permits updating auxiliary tables in

database as for example tables containing diagnosis

codifications, clinical examinations, analyses

codifications, departments, user groups and users.

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3.2 Patients Information Management

This function is one of the main functions in the

application, and the information about patients has

the following organization:

a) Contact information (personal ID number,

name, address, phone, fax, email, category and

National License Number of the examining doctor).

b) Examinations

The management of this information is

implemented in a window that contains several

secondary windows, as seen in figure 1. The first

secondary window contains a record for each patient

examination with the following information:

examination date, diagnosis and results of the visit

(solved/unsolved). This window is associated with

four secondary ones, having the following functions:

1. Collecting data from clinical examination. For

each analyzed system or segment the user can

specify if he found normal or abnormal relations.

A short description of the found abnormalities

can also be added.

2. Collecting numerical data from laboratories

and adding other information about important

data.

3. Storing, as descriptive text, the results of

various investigations: radiological, echography,

endoscopy descriptions.

4. Storing treatment recommendations and

prescriptions resulting from diagnosis.

Data from each secondary window can be easily

updated using coding tables that were created in the

set-up phase. The solution with secondary windows

was chosen because the doctor should have an

instant and easy access to the whole evaluation of

the patient from one examination to another.

c) Imagery

This option gives access to all the functions of

the application referring to the imagistic data

concerning a patient, images provided by different

devices (echograph, endoscope, MRI, CT, etc).

These images can be loaded from saved files or can

be imported directly from medical devices using a

real time acquisition system. The system can be

launched directly from this window. Imported

images will be saved directly in the patient folder. It

is possible to see the images directly, or to select one

as a query image and to execute a content-based

image query for the whole database to search similar

images.

Figure 1: Window for managing patient examination data.

3.3 Database Query

It is one of the most important functions of the

application. There are two types of queries: text-

based query and content-based image query on color

feature.

In the first case there are several search criteria

that might be composed using “and”. These criteria

are: patient name, personal numerical number,

address, clinical exam and analyses. For the first

three criteria it is used the “LIKE pattern’’ operator.

Using this operator the Select command permits

searching a string in all the values existing in

database. For the last criteria, where information is

codified in the database, the user can display a list of

options to select one for search. Some useful queries

that can be used are: list all the patients with a

specified diagnosis, or list of all the patients that

undergone a certain investigation or examination.

Visual data needs more evolved access methods.

Such a method is content-based retrieval, which

takes into consideration attributes or characteristics

extracted from multimedia information. If we take

into consideration the images, the technique is called

content-based visual retrieval (Del Bimbo, 2001;

Smith, 1997). This type of query implies selecting

an image as query image, and finding all the images

in database that are similar with it.

The medical areas where content-based visual

queries methods can bring advantages are well

known (Müller et al, 2004; Lehmann et al, 2004;

Shyu et al, 1999):

• Diagnostic aid

• Medical teaching

• Medical research

• Electronic patient records

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The reasons presented above generate the need

to implement in the application the content-based

visual query methods using color characteristic.

The color is the visual feature that is immediately

perceived on an image. The color space used for

representing color information in an image has a

great importance in content-based image query, so

this direction of research was intensely studied (Del

Bimbo, 2001).

There is no color system that it is universal used,

because the notion of color can be modelled and

interpreted in different ways (Gevers, 2004).

It was proved that the HSV color system has the

following properties: it is close to the human

perception of colors; it is intuitive; it is invariant to

illumination intensity and camera direction;

The operation of color system quantization is

needed in order to reduce the number of colors used

in content-based visual query: from millions to tens.

The quantization of the HSV color space to 166

colors, solution proposed by J.R. Smith, is the idea

used in this application (Smith, 1997).

The intersection of the histograms is used for

computing the similitude between the query image Q

and the target image T for color feature (Smith,

1997):

Figure 2: Window for content-based visual query.

The results of the experiments performed on a

database with 960 images from the field of the

digestive area are summarized in table 1.

The values in the table represent the number of

relevant images in the first 5 retrieved images. For

these five types of images (each type representing

one diagnosis) the results are encouraging, but the

experiments must be performed on a larger imagistic

database and taking into account more types of color

images. Only this way the conclusions can be

reliable.

Table 1: Content-based image query experimental results.

Quer

r. of retrieved ima

Pol

ps 3

Colitis 4

Ulce

Ulcerous Tumo

Esophagitis 4

4 CONCLUSIONS AND FUTURE

WORK

The paper presents the design of a Firebird database,

containing medical alphanumerical and imagistic

information. The application is implemented in

Delphi and can be accessed on-line. The main

functions of the application are:

- Managing information about patients: contact

information, visits, imagistic recordings,

laboratory results, treatment

- Simple text based query that might combine

several criteria

- Content-based visual query using color

characteristic.

- Generates complete or synthetic reports

REFERENCES

Interbase Documentation,

http://info.borland.com/techpubs/interbase/ , 2006.

Kermann, M., 2001. Programming and Problem Solving

with Delphi, Addison-Wesley.

Del Bimbo, A., 2001. Visual Information Retrieval,

Morgan Kaufmann Publishers. San Francisco USA.

Smith, J.R., 1997. Integrated Spatial and Feature Image

Systems: Retrieval, Compression and Analysis, Ph.D.

thesis, Graduate School of Arts and Sciences,

Columbia University.

Müller, H. et al., 2004. A review of content-based image

retrieval systems in medicine-clinical benefits and

future directions. International Journal of Medical

Informatics 73, 1-23.

Lehmann, T.M. et al., 2004. IRMA - Content-based Image

Retrieval in Medical Applications. Proceedings of the

14th World Congress on Medical Informatics, 842-

848.

Shyu, C. et al., 1999. ASSERT, A physician-in-the-loop

content-based image retrieval system for HRCT image

databases. Computer Vision and Image Understanding

75, 111-132.

Gevers, T., 2004. Image Search Engines: An Overview.

Emerging Topics in Computer Vision, Prentice Hall.

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