Liana Stanescu, Dan Dumitru Burdescu
Faculty of Automation, Computers and Electronics,University of Craiova, Bvd. Decebal, Craiova, Romania
Keywords: Content-based visual retrieval, color, color texture, region detection, color set back-projection algorithm.
Abstract: The paper presents the results of some experiments effectuated in the content-based visual query process
applied on color medical images extracted from the DICOM files provided by the medical tools. The color
feature was considered first, and the study implied more quantization methods (HSV color space at 166
colors, RGB color space at 64 colors and CIE-LUV color space at 512 colors) and several methods of
computing the dissimilitude between the query and the target images (Euclidian distance, the histogram
intersection and the quadratic distance between histograms). The content-based visual query on color
texture feature was tested using two important methods of texture detection: the co-occurrence matrices and
the Gabor filters. Also, the accurateness of the color set back-projection in detecting color regions
representing sick tissue in medical images was studied. The effectuated statistics encourage the use of this
algorithm in keeping track of the patient evolution under a certain treatment, with performances both in
quality and speed.
Retrieving the visual information is important in
many applications starting with the artistic domain
(art galleries, museums), to security and medical
fields, which are in fact the most important (Del
Bimbo, 2001). The purpose of this process is to
retrieve from a database the relevant images for a
query. The extraction of data that describe as
accurately as possible the visual content is essential.
Visual elements such as color, texture, shape that
directly describe the visual content are used for
retrieving images with a similar content from the
database (Del Bimbo, 2001).
One of the domains in which the use of the
content-based visual retrieval is needed is the
medical one. This is mainly due to the fact that in
the process of patient diagnosis, medical tools that
offer images to the doctor are used on a large scale
(Muller et al, 2004). A big part of these medical
tools generates a standard DICOM file including
images and associated data. This process led to very
large medical image databases. Except for the
traditional information retrieval in these databases
(taking into account the patient name, the doctor
name, the diagnosis), it is necessary to have a
content-based visual query for the following
The diagnosis process can be clarified in
certain cases;
The education and research activity can be
improved by using the access visual methods;
The visual characteristics allow not only the
retrieving of the patients having the same disease,
but also the cases where the visual similitude exists,
but the diagnosis differs;
There are still few systems that are really
integrated into the medical diagnosis process. The
work for the application of the most suitable
algorithms in image processing and features
extraction continues (Muller et al, 2004). A large
part of the images given by the medical apparatus
are color, in which case the characteristics like color,
color texture and shape must be considered.
In this article, the research has been effectuated
on color images extracted from DICOM files
provided by medical tools used in the diagnosis
process. The images are stored in a database on
which is applied the content-based image query on
color and color texture features because there are
Stanescu L. and Dumitru Burdescu D. (2006).
In Proceedings of the International Conference on Signal Processing and Multimedia Applications, pages 347-350
DOI: 10.5220/0001572203470350
some diseases that are characterized by the change
of the color and the texture of the affected tissue, for
example: colitis, esophagitis, polyps, ulcer and
ulcerous tumor. Also, an important medical
application of the color set back-projection
algorithm, which is a method for detecting color
regions, is presented.
The color is the visual feature immediately
perceived on an image. In content-based visual
query on color feature is important the used color
space and the level of quantization, meaning the
maximum number of colors (Del Bimbo, 2001).
Because there is not a unanimously accepted
solution on the appropriate color space to be used in
the content–based image query on color feature, the
study realized on color images extracted from the
DICOM files takes into consideration three
solutions, like:
1.The transformation of the RGB color space to
HSV and the quantization at 166 colors (Smith,
2.The use of the RGB color space quantized at
64 colors – M2
3.The transformation of the RGB color space at
the CIE-LUV and the quantization at 512 colors
(Smith, 1997)– M3
There are considered different color spaces and
different levels of quantization to determine the way
they affect the retrieval quality. It should be
mentioned that in this study there are displayed three
sets of result images that correspond to calculation
mode of the distances between query image and
target image. There have been taken into
consideration the Euclidian distance (D1), the
intersection of histograms (D2) and the quadratic
distance between histograms (D3) (Smith, 1997).
The experiments were performed in the
following conditions:
1. The images and alphanumeric data were
extracted from DICOM files by applying the
necessary algorithms (DICOM, 2006, LEAD, 2006).
2. It was created the test database with 920 color
medical images extracted from the DICOM files,
representing stomach and duodenum ulcers, ulcerate
cancer, hernias and esophagus varicosis.
3. Each image from the database was processed
before the execution of any query.
4. For each experimental query, an image was
chosen like query image and there were established
by a human factor, specialist medic, the images
considered relevant for the respective query.
5. Each of the relevant images for the considered
query was utilized, one by one, for querying the
database containing images. The final values of the
precision and the recall represent an average of the
values resulted in the case of each image taken one
by one as query image.
6. For each experimental query was constructed
the graphic of the precision reported to the recall for
each of the three distances in the case of each
quantization method. Also there were presented
under a tabular form the values that represent the
number of relevant images, existing in the first 5,
respectively 10 retrieved images, on the one hand,
and on the other hand the number of images which
can be retrieved for retrieving among them the first
5, respectively 10 relevant images.
The values from table 1 represent an average of
the resulted values in the case of each image taken,
one by one, as query image for the query that
considers the stomach and duodenum ulcers images.
Table 1: Stomach and Duodenum Ulcers.
D1 D2 D3
5(9) 5(9) 5(9) M1
8(13) 6(11) 7(12)
5(9) 5(9) 5(9) M2
7(18) 6(11) 5(11)
4(7) 5(9) 4(7) M3
8(23) 6(13) 7(17)
Together with color, texture is a powerful
characteristic of an image, existing in nature and
medical images, where a disease can be indicated by
changes in the color and texture of a tissue. A series
of methods have been studied to extract texture
feature (Del Bimbo, 2001). Among the most
representatives methods of texture detection are the
co-occurrence matrices and Gabor representations,
studied in this paper (Del Bimbo, 2001, Palm et al.,
2000). There are many techniques used for texture
extraction, but there isn’t a certain method that can
be considered the most appropriate, this depending
on the application and the type of images taken into
account. Over the past few years, research has been
done in color textures recognition, proving that the
color information improves the color texture
classification (Palm et al., 2000).
The experiments were performed in the
following conditions:
1. the same database with 920 color images
was used;
2. the co-occurrence matrices are computed
for each component R,G,B and three vectors
containing the 6 sizes (energy, entropy, maximum
probability, contrast, inverse difference moment,
correlation) are generated (Del Bimbo, 2001); the
matrices are computed for d=1 and φ=0; in this case
the characteristics vector has 18 values;
3. the Gabor characteristics vector containing
12 values (computed for 3 scales and 4 orientations)
is generated (Palm et al., 2000);
4. the characteristics vectors generated at
points 2 and 3 are stored in the database;
In order to make the query the procedure is:
1. a query image is chosen;
2. the dissimilitude between the query image
and every target image from the database is
computed for each specified criteria (the vector
generated on the basis of the co-occurrence matrices
and the vector for Gabor method);
3. 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 its 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
2. Met 1 represents the query on color texture
feature using Gabor method and Met 2 represents
the query on color texture feature using co-
occurrence matrices.
The values in the table represent the number of
relevant images in the first 5 images retrieved for
each query and each method.
Table 2: The experimental results.
Met 1 Met 2
Polyps 2.2 2.8
Colitis 1.7 1.7
Ulcer 1.8 2.2
1.1 1.5
Esophagitis 1.7 2.5
The application of an automated algorithm for
detecting the color regions in medical images has
two important utilizations:
1. in content-based region query on medical
images collections, the specialist chooses one or
more of the detected regions for querying the
database, the purpose being the retrieval of images
with similar color regions; this can be useful for
clarifying some uncertain diagnosis. This problem
was studied in Stanescu et al., 2004, interesting
results being presented;
2. during the evolution in time of the disease
of patients that follow a certain treatment;
For detecting color regions it was chosen the
color set back-projection algorithm, introduced
initially by Swain and Ballard and then developed in
the research projects at Columbia University (Smith,
1997). For each detected regions the color set that
generated it, the area and the localization are stored
in the database. All the information is necessary
further on in studying the evolution of the patients.
The region localization is given by the minimal
bounding rectangle (MBR). The region area is
represented by the number of color pixels, and can
be smaller than the minimum bounding rectangle.
relevant region relevant region
area=367;color=162; area=251; color=126;
MBR=(40,25) MBR=(28,24)
Figure 1: The detected color regions for the image.
The experiments were effectuated on 202 color
images extracted from DICOM files and
representing patients with peptic ulcer disease. The
color set back-projection algorithm is applied on
each image for detecting the single color regions,
and the region (s) that represents the sick zone were
marked as relevant. By applying a certain drugs
treatment on some patients, at some time intervals
strictly established by the physician, the images
were again collected from the same patients and the
same algorithm is applied for detecting the color
regions. The relevant region(s) that represents the
sick tissue is marked again. The comparison
between the new and old regions detected as
relevant for the same patient, taking into
consideration the number of pixels, can help the
physician to establish in what percentage the sick
region is reduced because of the administrated
drugs. This approach may lead to a more rapid
estimation and correct enough of the percentage in
which the medication has a good effect in the ulcer
diagnosis. This may come in the help of the patients,
specialists, and the drugs producers that intend to
test a medical product. The experiments showed that
there were slight differences between human
observer and computer system in order to appreciate
healing staging. The speed of the retrieval process
was also tested, comparing the time spent by the
observer and the computer to find each patient’s
record in the database. This process was
electronically measured and stored in the computer
for statistics. The result is that the software has a
significantly higher speed than human observer with
no significant decrease of the retrieval quality.
As a result of effectuating an important number of
experiments (synthetically presented here) in the de
content-based visual retrieval process on databases
with images extracted from DICOM files generated
by medical tools, some conclusions are clear. In the
case of the content-based image query on color
feature, the series of effectuated tests indicated that
the best results were obtained for the color space
HSV quantized at 166 colors and using the
histogram intersection for computing the image
similitude. In the case of the content-based image
query on color texture feature, better results are
obtained using the co-occurrence matrices. The two
algorithms (Gabor filters and co-occurrence
matrices) have the same complexity O(n
) where n
represents the maximum dimension of the image
(Burdescu, 1998). As a result, the co-occurrence
matrices method is recommended in this type of
query. The retrieval system can combine two
methods: one based on color feature and the other
based on color texture detected with co-occurrence
matrices, which complete one another. The statistic
studies for the color set back-projection algorithm in
keeping track of the patient evolution, during the
treatment of a certain disease, emphasize a superior
speed in sick region detection and a similar quality
between the computerized mode and the process
effectuated by the specialist.
In the future, the studies will be extended on
more types of color medical images and new
methods will be implemented and compared, taking
into consideration the same factors: the complexity
of the algorithm and the retrieval quality.
Burdescu, D.D., 1998. Analiza complexitatii algoritmilor,
Ed. Albastra. Cluj-Napoca.
Del Bimbo, A., 2001. Visual Information Retrieval,
Morgan Kaufmann Publishers. San Francisco USA.
DICOM Homepage, 2006.
LEAD Technologies, 2006.
Muller, H., Michoux, N., Bandon, D., Geissbuhler, A.,
2004. A Review of Content_based Image Retrieval
Systems in Medical Application – Clinical Benefits
and Future Directions. Int J Med Inform. 73(1)
Palm, C., Keysers, D., Lehmann, T., Spitzer, K., 2000.
Gabor Filtering of Complex Hue/Saturation Images
For Color Texture Classification. In: JCIS2000, 5th
Joint Conference on Information Science. Atlantic
City, 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.
Stanescu, L., Burdescu, D., Mocanu, M., 2004. Detecting
Color Regions and Content-based Region Query in
databases with Medical Images. Periodica
Politechnica, Transactions on Automatic Control and
Computer Science. 49(63)