1. CLASSIFICATION OF BRAIN MRI SERIES
BY USING DECISION TREE LEARNING1
Yong Uk Kim, Juntae Kim, Ky Hyun Um, Hyung Je Jo
Dept. of Computer Engineering, Dongguk University
yukim@dgu.ac.kr, jkim@dgu.ac.kr, khum@dgu.ac.kr, chohj@dgu.ac.kr
Abstract image. It is because a diagnosis is conducted by looking
at the entire image series, not looking at any one image
In this paper we present a system that classifies brain among them.
MRI series by using decision tree learning. There are
two kinds of information that can be obtained from Conventional image retrieval systems can be classified
MRI. One is a set of low-level features that can be into annotation-based retrieval systems [1] and content-
obtained directly from the original image such as sizes, based retrieval systems [4][5][7]. In annotation-based
colors, textures and contours. The other is a set of high- retrieval systems, the opinions of experts are attached to
level features that be made through interpretation of the each image and are used for retrieval. These systems
segmented images. To classify images based on the can achieve relatively high accuracy due to the
semantic contents, learning and classification should be annotations, but providing annotations needs much time
performed based on high-level features. The proposed human intervention. In content-based retrieval systems,
system first classifies the image segments by using low- such textual information is not used. A system interprets
level features. Then the high-level features are images by analyzing the features of images. However,
synthesized and the whole MRI series are classified by usually these systems cannot achieve high accuracy
using those features. Experiments have been performed because it is generally difficult to interpret the semantic
to classify brain MRI series to normal brains, cerebral contents of an image by using the low-level information
infarctions and brain tumors, and the results are such as color, texture, and shape. So it is suggested that
discussed. the content-based image retrieval system extracts high-
level information such as spatial or logical relationships
Key Words and takes advantage of them [2][3][10].
Image Retrieval, Classification, Learning, Decision
Tree In this paper, we propose a content-based image
classification method based on the decision tree
learning [6][9][13] to achieve high accuracy in retrieval
of brain MRI series. The proposed system classifies a
1. Introduction1 MRI series to normal, cerebral infarction, or brain
tumor case. The decision tree learning is performed in
Due to the advances of computer and communication two separated levels. At the first level, segmented
technologies a lot of medical information systems such images are classified by using the low-level features. At
as HIS (hospital information system), RIS (radiology the second level, entire MRI series are classified by
information system), and PACS (picture archiving and using the high-level features synthesized by using the
communication system) have been studied and low-level classification results.
developed.
These medical information systems have been very
helpful in managing clinical documents and medical 2. Backgrounds
images, and sharing them through the localized network
or the internet. However, since the sizes of This chapter presents the characteristics of medical
computerized tomography (CT) or magnetic resonance images, especially that of the magnetic resonance image
imaging (MRI) are large, the time for information (MRI). Also we introduce several related researches.
retrieval becomes critical as the number of images
increases rapidly. As the amount of data increases, more
efficient and intelligent retrieval systems become 2.1 Characteristics of medical images
necessary [3][11][12]. Furthermore, classification or
retrieval should be performed on the entire series of Medical images are effective sources of information for
images (images of a patient that are photographed diagnosis of a disease and its location, size, and type.
multiple times on a regular interval), not on a single Various types of medical imaging are used including X-
ray, computerized tomography (CT) and magnetic
1 resonance image (MRI).
1. This research has been funded by the Korea Science
and Engineering Foundation.

2. MR images are generally gray-scaled, and their texture direction of each object on the entire image picture, the
characteristics are not easily noticeable. Also different extent of overlapping of objects, the location of objects,
parts of a brain such as cerebrum, midbrain, cerebellum etc.
and pituitary have unclear boundaries. The structural
shapes and relationship between parts are complicated. Recently there have been several studies in the effort to
The differences between the values of various features extract high-level information semi-automatically or
are also small. The MR images also have various automatically. KMeD (Knowledge-based Multimedia
image-filming parameters - spatial resolution, contrast Medical Distributed Database) system is one of such
resolution, filming angles, etc. Figure 1 shows examples examples [3]. KMeD system uses image and character
of brain MRI series [14][15]. to query the medical multimedia DB. It uses high-level
information for image retrieval such as contour, area,
circumference ratio, shape, direction of object pairs, etc.
Patient 1 Patient 2 ��� Patient n It used instance-based MDISC algorithm in
classification..
•••
3. Classification of MRI Series
This chapter presents the methods of extracting low-
level and high-level information, and two-level learning
and classification algorithm.
Figure 1. Examples of brain MRI series
3.1 Decision Tree
2.2 Retrieval of medical images
The Decision Tree is used to classify data based on
To classify and retrieve images based on their contents, selected features [6][9][13]. In learning, a tree is
a image retrieval system utilizes various information generated from training examples by divide and
from images. There are systems that use low-level conquer method. In order to determine the order of
information, and that use high-level information. choosing features, the concept of entropy used. Entropy
of a data set S is high if the data are evenly distributed
2.2.1 Use of Low-level Information over the target classes. The decision tree learning
algorithm computes the information gain for a feature
The low-level information is the primitive or A, which is the amount of expected entropy reduction
fundamental features obtained directly from an image when A is chosen to classify data at the present state.
such as color, texture, shape, etc. The low-level The formula for the entropy and the gain are as follows:
information doesn't represent the semantic contents of
an image. Such low-level information is used in the c
conventional method of CBIR(content-based image Entropy ( S ) ≡ ∑ ( − pi log 2 pi ) (1)
i =0
retrieval). CBIR is a method to automatically classify
| Sv |
and retrieve images based on the surface characteristics Gain( S , A) ≡ Entropy ( s) − ∑
v∈Values ( A ) | S |
Entropy ( S v ) (2)
extracted from an image itself. CBIR has the advantage
that it is possible to build an automatic system that does
not need human experts. However, since it excludes the The Decision Tree learning is usually strong against
aspect of semantic contents of images, it is difficult to noise and the result can be easily converted into rules.
retrieve the images with same semantic contents but In this paper, we used the Weka library, in which the
have different shapes or colors. C4.5 decision tree algorithm is realized in Java [13].
The systems that use the content-based image retrieval
are QBIC, VIR, Visual Retrieval Ware, etc. QBIC 3.2 Separation of learning and classification
(query by image content) is an image retrieval engine level
developed by IBM [4]. In QBIC, a user can retrieve an
image by means of the image texture expressed as color The proposed method performs separate levels of
ratio, distribution, location and graphics. learning and classification - object learning/
classification and image series learning/classification.
2.2.2 Use of High-level Information Each level extracts content-based low-level and high-
level features and applied the decision tree learning
The high-level information is the logical relationship separately. The two level learning is used because it can
between images or the semantics shown by image series extract high-level features more effectively. The logical
such as the distance between image objects, the high-level features are synthesized based on the

3. semantic interpretation of the segmented images. Figure Innercircle
Roundness = (7)
2 shows the diagram of the two level process. Outtercircle
Images are preprocessed and segmented into several
objects. The object classification rules are learned from The result of learning is a decision tree that can be
training data (manually classified segments) by using represented as rules. Figure 3 shows an example of
the low-level features. The image series classification segmented image objects and the learned object
rules are learned from a set of classified MRI data by classification rules.
using the high-level features. When a new MRI series is
given, each image is first segmented into several
objects. Then the object classification rules are applied
to classify them, and then the high-level features for Feature Name Content
entire series are generated based on the results. The ID Image Id of patient
MRI series that is represented as a set of high-level OID Objects Id of image
features is then classified by using the image series Bright Color histogram
classification rules. Area Area of each object
Extrusive Extrusion of object
Round Roundness of object
Medical Image
Center_X
Center of object
Center_Y
Segmentize MBR_ULX
Segment Training Data MBR_ULY Minimum bounding
Extract Low-Level
Features
MBR_DRX rectangle of object
Object Classification MBR_DRY
Object Learning Rules
Classify Object
Low-level
Table 1. Low-level features used in learning.
Image Series Training Data Extract High-Level
Features
Image Series White
Image Series LearningClassification Rules Classify Image Series matter
High-level
Gray matter
Result Class Unknown
Object
Figure 2. Two level learning and classification process
3.3 Learning object classification rules
The purpose of object learning is to generate rules for
anatomic classification of image segments. The
decision tree learning is performed on the training data
that is represented by low-level features. For each
object, contour length, brightness, area, center,
extrusion, roundness and MBR(minimum bounding
rectangle) are used as low-level features as shown in Figure 3. Examples of segmented objects
Table 1. The equations for computing extrusion and and object classification rules
roundness are as follows.
n 3.4 Learning image series classification
∑ Distance(center, contour ( x )) i
(3) rules
Average = i =1
n
n
The purpose of image series learning is to generate the
Extrusive = ∑ ( Average − Distance(center , contour ( xi ))) 2 (4)
i =1 rules to classify entire image series. The learning is
Innercircle = based on the high-level features that are generated
MIN (π × Distance(center , contour ( xi ))2 ) (5) based on the low-level classification results. The
Outtercircle = generation of high-level features of image series
MAX (π × Distance(center , contour ( xi )) 2 ) (6) consists of two phases. The first phase is to compute
logical features by using the direction and location
information of the classified objects. The second phase

4. is to compute other features that can be directly The brightness ratio between objects are used as
obtained from the entire images. features because the brightness value depends on
image-filming devices. The direction information shows
The high-level features for image series are shown in the direction of object from the center of head. As
Table 2. They are the patient information, the existence Figure 4-(a) shows, the brain is divided into 8 directions
of cerebrospinal medulla fluid, the distance between the from the center, and then the direction of an object is
center of brain and the center of UO(unknown object), determined by examining which of the 8 directions the
the direction of UO, the closeness between the UO and center of the object belongs to. These 8 directions
the cerebrospinal fluid, the brightness and area ratio indicate the frontal lobe, temporal lobes and occipital.
between objects, etc. The other features for entire image The spatial relationship between cerebrospinal fluid and
series are computed by averaging or summing the UO determines whether the UO infiltrated into the
values of features of each image. The vertical object cerebrospinal fluid or not as in Figure 4-(b). The
locations are also computed. vertical position expressed the vertical location of an
object in the entire image series in terms of the ratio to
Feature Feature Name Content the top. In the vertical position, the central
Information Age Age of patient cerebrospinal fluid and UO are used to indicate where
of Patient Sex Sex of patient the possible disease area is located in three dimensions.
Exist of ExistOfCsf Is CSF exist
Object ExistOfUO Is UO exist Generated high-level features are applied to the learning
Ratio of AreaRatio_UO UO area / Area Sum of image series classification rules. Figure 5 shows the
area examples of image series and the learned classification
White matter / Gray
between AreaRatio_W_G
matter rules. Each of image series are assigned to one of the
objects
Ratio of BrightRatio_ UO / White matter three general categories – normal, infarct, and tumor.
bright UO_W bright
between BrightRatio_ UO / Gray matter
objects UO_G birght
Direction UO_Direction Direction of UO
Spatial SpatialRel_ Normal
Join of CSF and UO
Relationship CSF_UO
Distance between UO
Distance Distance_UO_C
and brain center
Sum of UO area of all Infarct
Total_Area_UO
image series
Sum of CSF area of
Total_Area_CSF
all image series
Sum of White matter Tumor
Series
Total_Area_White area of all image
series
Sum of Gray matter
Total_Area_Gray area of all image
series
Vertical position of
Vertical Vertical_CSF
CSF in image series
position of
Vertical position of
objects Vertical_UO
UO in image series
Table 2. High-level features used in learning
Figure 5. Examples of image series and
N image series classification rules
NE
NW
E
4. Experimental Results
W
We have implemented the proposed system prototype,
SW S SE
and the experiments have been performed by using a set
of real brain MRI series collected from local hospital.
(a) (b) The dataset consists of 1400 MR images of 72 persons,
10 of which were normal, 33 were infarction, and 29
Figure 4. Examples of (a) direction, (b) spatial relationship were tumor cases.

5. Table 3 shows the results of object classification in cases showed 93.1% accuracy on MRI series
terms of precision. The classification accuracy of classification.
GM(gray matter) and WM(white matter) are relatively
high. About 17% of the CSF(cerebrospinal fluid) Currently, our system classifies one MRI series taken
segments were misclassified to GM or UO, and 11% of on a certain time. Extending the system to classify
the UO were misclassified to GM. This is because GM temporal series of MRIs that is taken on a certain time
usually has a mid-feature value between CSF and UO. interval can be a future research direction. Also, further
The overall object classification accuracy was 97.9%. study should be made on selecting features and
introducing more complicated high-level features.
Classification Result
Incorrect Precision
CSF GM WM UO
References
CSF 97 15 0 5 20 82.9%
298
GM 13
0
23 16 52 98.3% [1] Chang, N. S. and Fu, K. S., "Query-by
Data
175 pictorial example," IEEE Transactions on
WM 0 28 0 28 98.4%
5 Software Engineering, SE-6(6):519-524, Nov.
UO 0 7 0 59 7 89.4% 1980.
Total 4998 107 97.9% [2] Chang, S. K. and Hsu, A., ”Image
information systems: Where do we go from
Table 3. Results of objects classification
here?,” IEEE Transactions on Knowledge and
Table 4 is the result of the image series classification. In Data Engineering, 4(5): 431-442, 1992
the case of brain tumor, there were strange parts [3] Chu, W. W., Cardenas, A. F. and Taira, R.
(classified as UO) that could be distinguished in most K., "KMeD: A knowledge-based multimedia
cases. In the case of brain infarction, there were cases me dical distributed database system,"
that have no strange part. But they can be classified Information Systems, 20(2): 75-96, 1995
based on other features such as the color ratio between [4] Flickner, M. et al, "Query by image
objects and the shape of cerebrospinal fluid. The overall Content: The QBIC System," IEEE Computer
classification accuracy was 93.1%. Special issue on Content Based Retrieval, Vol.
28, No.9, 1995.
Classification Result
[5] Gudivada, V. N. and Raghavan, J. V.,
Normal Infarct Tumor
Incorrect Precision “special issue on content-based image retrieval
Normal 9 0 1 1 90.0%
systems,” IEEE Computer Magazine,
Infarct 0 31 2 2 93.9% 28(9):18-62, 1995
Data
Tumor 0 2 27 2 93.1% [6] Mitchell, T. M., Machine learning,
Total 72 5 93.1% McGraw- Hill, New York, NY, 1997.
[7] Niblack, W., Barber, R., Equitz, W.,
Table 4. Result of image series classification Flickner, M., Glasman, E., Petkovic, D.,
Yanker, P., Faloutsos, C. and Taubin, G.,
The experiments on classification of three different “Efficient and Effective Querying by Image
kinds of brain tumor were also performed, but the result Content,” Journal of Intelligent Information
showed only 63.3% accuracy due to the insufficient Systems, 3:231-262, 1994
data for learning. [8] Ogle, V. E. and Stonebraker, M., "Chabot :
Ret rieval from a relational database of
images," In Proceedings of International
5. Conclusion Conference on IEEE Computer, 28(9),
September 1995
This paper presents a system for classification of brain [9] Quinlan, J. R., C4.5: Programs for machine
MRI series. To learn the classification rules, the learning, Morgan Kaufmann, 1993.
decision tree algorithm is used in two-levels. In low- [10] Rui, Y., Huang, T. S. and Chang, S. F.,
level, each image is segmented into smaller objects and “Image Retrieval: Current Techniques,
represented by the low-level features, and then the Promising Directions and Open Issues,”
object classification rule is learned. In high-level, the Journal of Visual Communication and Image
logical features such as the relationship between Representation, 10:39-62, 1999
different objects were generated, and the image series [11] Sauer, F. and Kabuka, M., "Multimedia
classification rules are learned. Unlike the conventional technology in the radiology department,"
image retrieval system, low-level features and high- Procee dings of the second ACM international
level features were applied separately in classification conference on Multimedia, Pages 263-269,
in order to effectively perform learning and 1994
classification. The preliminary experiments with 72 [12] Sonka, M., Hlavac, V., Boyle, R., Image
Processing Analysis and Machine Vision, PWS
Publishing, 1999

6. [13] Witten, I. H. and Frank, E., Data Mining:
Practical Machine Learning Tools and Techn
iques with JAVA Implementations, Morgan
Kaufmann, 2000
[14] The Whole Brain Atlas,
http://www.med.harvard.edu/A
ANLIB
[15] The Korean Neurosurgical Society,
Neurosurgery, 2000