A STUDY OF TWO COLOR SYSTEMS USED IN

CONTENT-BASED IMAGE QUERY ON MEDICAL IMAGERY

Liana Stanescu, Dumitru Burdescu, Cosmin Stoica and Marius Brezovan

University of Craiova, Faculty of Automation, Computers and Electronics

Keywords: Image feature extraction, image processing, content-based visual quer

y, color, histogram, HSV color space,

l color space, medical images.

Abstract: The article presents a comparative study over two methods used in content-based visual query. The two

methods refer at two different color systems used for representing color information from images: HSV

quantized to 166 colors and l1l2l3 quantized to 64 colors. The originality of the study comes from the fact

that it was made on a database with medical images from digestive tract area, captured by an endoscope.

The scope was to check the quality of the content-based visual query on images representing five different

diagnoses (colitis, ulcer, esophagitis, polyps and ulcerous tumor) and taking into consideration that some

parameters were modified during the capturing process: viewing direction, intensity and direction of the

illumination, parameters that affect mostly the medical images captured during the diagnosis process.

1 INTRODUCTION

As the world is in the middle of the digital era, the

quantity of visual information is increasing (Sebe

and Lew, 2001). More than 2700 digital pictures are

made in every second (in total 85 billion images

yearly). For example, PhotoWorks includes tens of

millions of images on its web site. The Internet

allows us to have access to a big part of these

images. The common images are completed by

images with special purpose, like medical images

with an estimation of 2 billion per year. Because of

the tendency for digital (television, movies) and

because everybody will have access to everything,

the number of images will be increasing. The world

production of digital information in 2007 is

estimated to be more than 10

GB (250 MB for each

man on the planet, ignoring his technological

development). It is estimated that in the next 10

years, each of us will manipulate terabytes of

information (video, static images, music, photos and

documents) every day.

These image databases are associated with the

problem of the content-based retrieval, solved in two

steps (Sebe and Lew, 2001).

In the first step, when inserting a new image, it

will be

pre-processed and some features will be

automatically extracted: color, texture and shape.

The result will be a characteristics vector that will be

stored in the database.

In the second step the content based retrieval is

made, by choosing a query

image, calculating the

characteristics vector, comparing this vector with

each vector of the images stored in the database and

viewing the most similar images.

The color is one of the base image properties. In

content based retrieval on color feature it is desired

to find the images from the database having the

color composition closest to the color composition

of the query image (Del Bimbo, 2001, Gevers and

Smeulders, 1999).

The color content of an image is best represented

color histograms.

Comparing color histograms of the query image

and

target image is done by histograms intersection

or by the quadratic distance between histograms that

takes into consideration the conceptual similitude

between two colors (Sebe and Lew, 2001, Smith,

1997).

As was said before, one of the domains where a

large numb

er of images are accumulated is the

medical domain. The advantages of using content-

based visual query on medical images are on the

following directions (Muller et al, 2004):

• Medical teaching

• Medical resea

• Diagnostic aid

• Electronic patient records

337

Stanescu L., Burdescu D., Stoica C. and Brezovan M. (2007).

A STUDY OF TWO COLOR SYSTEMS USED IN CONTENT-BASED IMAGE QUERY ON MEDICAL IMAGERY.

In Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics, pages 337-340

DOI: 10.5220/0001621303370340

 SciTePress

The medical images are being produced directly

by medical equipment used in patient diagnosis, or

by digitalizing the images stored on other devices.

In each of these methods some changes can

occur:

• Changes in viewing direction

• Changes in direction of the illumination

• Changes in the intensity of the illumination

As a result, the purpose of the paper is to make a

comparative study of the content-based query results

effectuated on medical images database where the

color information is represented by HSV and l1l2l3

color systems.

The originality of the study is given by the fact

that the experiments are made on medical images

from digestive area produced by an endoscope. The

ill area is seen from different directions and in

different illumination intensity. This study, unlike

the others made on CorelDraw images, uses images

produced in real condition, in patient diagnosis.

The paper has the following structure: In section

2 the two color systems are presented. In section 3

the conditions and the results of the experiments are

presented, and in section 4 the conclusions of the

comparative study are discussed.

2 CONTENT-BASED IMAGE

QUERY ON COLOR FEATURE

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 modeled and

interpreted in different ways. Each system has its

own color models that represent the system

parameters (Gevers, 2004).

There were created several color spaces, for

different purposes: RGB (for displaying process),

XYZ (for color standardization), rgb, xyz (for color

normalization and representation), CieLuv, CieLab

(for perceptual uniformity), HSV (intuitive

description) (Gevers, 2001, Gevers, 2004). The color

systems were studied taking into consideration

different criteria imposed by content-based visual

query (Gevers and Smeulders, 1999):

• The independence of the imaging device

• Perceptual uniformity

• Linear transformation

• Intuitive for user

• Robust against imaging conditions: invariant to a

change in viewing direction, invariant to a

change in object geometry, invariant to a change

in direction and intensity of the illumination and

invariant to a change in the spectral power

distribution of the illumination.

The studies have shown that two of these color

systems can be used, with good results in a content-

based visual query process, namely HSV and l1l2l3

(Gevers et al, 2006).

It was proved that the HSV color system has the

following properties (Gevers, 2004):

• It is close to the human perception of colors

• It is intuitive

• It is invariant to illumination intensity and

camera direction

The studies made on nature and medical images

have shown that in the case of the HSV, RGB and

CieLuv color systems, the HSV color space

produces the best results in content based retrieval

(Stanescu et al, 2006).

Still, the HSV color space has several problems

(Gevers, 2004) :

• Nonlinear (but still simple) transformation from

RGB to HSV

• Device dependent

• the H component becomes instable when S is

close to 0

• the H component is dependent of the

illumination color

Gevers and Smeulders have proposed a new

color system, named l, whose components are

defined using the equations (Gevers and Smeulders,

1999):

(1)

Where R, G, B are the color values in the RGB

color space. They also showed that the l color

system is invariant to viewing direction, illumination

direction and intensity. In this case it is also a

nonlinear, but simple, transforming from RGB space

to l space.

In case of medical images the main problems are

regarding changing illumination intensity and

viewing direction. That is why the two color spaces

presented above are chosen.

222

)()()(

)(

),,(1

BGBRGR

BGR

−+−+−

−

222

)()()(

)(

),,(2

BGBRGR

BGR

−+−+−

−

222

)()()(

)(

),,(3

BGBRGR

BGR

−+−+−

−

ICINCO 2007 - International Conference on Informatics in Control, Automation and Robotics

338

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 study (Smith, 1997). For the color space

l1l2l3 the solution of quantization to 64 colors is

chosen, keeping 4 values for each component of the

system. The fact that a color system is quantized to

166 colors and the other to 64 colors does not

influence the quality of the content-based image

query process, the research studies showing clearly

this aspect (Stanescu et al, 2006). The color

histograms represent the traditional method of

describing the color properties of the images. They

have the advantages of easy computation and up to

certain point are insensitive to camera rotating,

zooming, and changes in image resolution (Del

Bimbo, 2001). In case of both color systems, to

compute the distance between the color histograms

of the query image and the target image, the

intersection of the histograms is used (Smith, 1997).

The studies have also shown that using this metric in

content-based visual query gives very good results

as quadratic distance between histograms that is

more difficult to calculate (Smith, 1997, Stanescu et

al, 2006).

3 EXPERIMENTS

The experiments were performed in the following

conditions.

A database with 520 color images from the field

of the digestive apparatus was created. The images

are from patients with the following diagnosis:

polyps, ulcer, esophagitis, ulcerous tumors and

colitis. For each image there are several images with

affected area captured from 3 or 4 viewing

directions. For each image in the database there is

another identical image, but having the illumination

intensity changed.

A software tool that permits the processing of

each image was created. The software tool executes

the following steps:

1. the transformation of image from RGB

color space to HSV color space and the

quantization to 166 colors

2. the transformation of image from RGB

color space to l1l2l3 color space and the

quantization to 64 colors

3. calculation of the two color histograms

with 166, respectively 64 values, that

represent the characteristics vectors and

storing them in the database

In order to make the query the procedure is:

• a query image is chosen

• the dissimilitude between the query image

and every target image from the database is

computed, for each two specified criteria

(color histograms with 166 colors and the

color histogram with 64 colors);

• the images are displayed on 2 columns

corresponding to the 2 methods in

ascending order of the computed distance.

For each query, the relevant images have been

established. Each of the relevant images has become

in turn a query image, and the final results for a

query are an average of these individual results.

The experimental results are summarized in table

1. Method 1 represents the query using the HSV

color space quantized at 166 colors and Method 2

represents the query on color using the l1l2l3 color

space quantized at 64 colors. The values in the table

represent the number of relevant images of the first 5

images retrieved for each query and each of the

methods, as an average of the values obtained on

each executed query.

Table 1: Experimental results.

It must be mentioned that the queries were made

for each of the 5 diagnostics in part. The notion of

relevant image was strictly defined. The images

from the same patient captured at different

illumination intensity and from different points of

view were considered relevant for a query, and not

the ones with the same diagnosis. The quality of the

content-based image query process was strictly

analyzed. In figure 1 there is an example of content-

based image query considering the two specified

methods for images categorized as colitis. The first

column contains 5 images retrieved by Method1 and

the second contains the images retrieved using

Method2. In the first case there are 5 relevant

images and in the second case, 4 relevant images.

4 CONCLUSION

The paper presents the condition in which the

quality of the content-based visual query process

was studied, using a collection of medical images

from digestive tract. The quality was measured

Query Method 1 Method 2

Polyps 3.6 3.2

Colitis 3.5 3.1

Ulcer 3.2 2.9

Ulcerous Tumor 3.5 3.1

Esophagitis 3.4 3.1

A STUDY OF TWO COLOR SYSTEMS USED IN CONTENT-BASED IMAGE QUERY ON MEDICAL IMAGERY

339

Figure 1: The retrieved images using the two specified

methods.

calculating the precision and recall parameters. HSV

system, quantized to 166 colors and l1l2l3 color

system quantized to 64 colors were considered

highlighting the way they influence the process of

content-based visual query if some important

parameters that often affects medical images are

modified: viewing direction, direction and intensity

of the illumination.

Several conclusions can be formulated after the

experimental results were analyzed:

1. to find images representing the same ill area,

that were captured by an endoscope from

several viewing directions, the solution that

uses HSV color system quantized to 166

colors gives the best results

2. for images representing the same ill area,

captured to different illumination intensities,

the solution that uses l1l2l3l color system

quantized to 64 colors, gives the best results

in querying process

3. globally, the solution that uses HSV color

space gives most satisfying results, because

the database includes both types of images

In general, for medical images, the first case,

with images representing ill area captured from

different angles is the most frequent case. So, that is

why the use of HSV color space, quantized to 166

colors, is recommended. The situation in the

database that was studied was the same, namely, the

number of images captured from different angles

was higher than the number of images where only

the illumination intensity was different.

In the future the study will be extended by using

a bigger database with much more images in order to

see if this conclusion will be also confirmed. New

experiments with images from other parts of the

human body or images produced by other medical

devices will be effectuated.

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Gevers, T., 2001. Color in Image Search Engines. In

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