This paper describes the participation of MIRACLE research consortium at the ImageCLEF Medical Image Annotation task of ImageCLEF 2008.



During the last year, our own image analysis system was developed, based on MATLAB. This system extracts a variety of global and local features including histogram, image statistics, Gabor features, fractal dimension, DCT and DWT coefficients, Tamura features and co-occurrence matrix statistics. A classifier based on the k-Nearest Neighbour algorithm is trained on the extracted image feature vectors to determine the IRMA code associated to a given image. The focus of our participation was mainly to test and evaluate this system in-depth and to compare among diverse configuration parameters such as number of images

for the relevance feedback to use in the classification module..

González Cristóbal, José Carlos

Goñi Menoyo, José Miguel

Lana Serrano, Sara

Villena Román, Julio

Archivo Digital UPM

MIRACLE at ImageCLEFannot 2008: Nearest Neighbour Classification of Image Feature Vectors for Medical Image Annotation

T. Deselaers

S. Lana-Serrano

I.H. Witten

Crossref

Servicio de Coordinación de Bibliotecas de la Universidad Politécnica de Madrid

C. Peters et al. (Eds.): CLEF 2008, LNCS 5706, pp. 728–731, 2009. 
© Springer-Verlag Berlin Heidelberg 2009 
MIRACLE at ImageCLEFannot 2008:  
Nearest Neighbour Classification of Image Feature 
Vectors for Medical Image Annotation 
Sara Lana-Serrano1,3, Julio Villena-Román2,3 
José Carlos González-Cristóbal1,3, and José Miguel Goñi-Menoyo1 
1
 Universidad Politécnica de Madrid  
2
 Universidad Carlos III de Madrid 
3
 DAEDALUS - Data, Decisions and Language, S.A. 
slana@diatel.upm.es, jvillena@it.uc3m.es 
josecarlos.gonzalez@upm.es, josemiguel.goni@upm.es 
Abstract. This paper describes the participation of MIRACLE research consor-
tium at the ImageCLEF Medical Image Annotation task of ImageCLEF 2008. 
During the last year, our own image analysis system was developed, based on 
MATLAB. This system extracts a variety of global and local features including 
histogram, image statistics, Gabor features, fractal dimension, DCT and DWT 
coefficients, Tamura features and co-occurrence matrix statistics. A classifier 
based on the k-Nearest Neighbour algorithm is trained on the extracted image 
feature vectors to determine the IRMA code associated to a given image. The 
focus of our participation was mainly to test and evaluate this system in-depth 
and to compare among diverse configuration parameters such as number of im-
ages for the relevance feedback to use in the classification module.  
Keywords: Information Retrieval, medical image, image annotation, classifica-
tion, IRMA code, axis, learning algorithms, nearest-neighbour, machine learn-
ing, ImageCLEF Medical Automatic Image Annotation task, CLEF, 2008. 
1   Introduction 
The MIRACLE team is a research consortium formed by research groups of three dif-
ferent Spanish universities (Universidad Politécnica de Madrid, Universidad 
Autónoma de Madrid and Universidad Carlos III de Madrid) along with DAEDA-
LUS, a private company founded as a spin-off of these groups and a leading company 
in the field of linguistic technologies in Spain.  
This paper reviews our participation [1] at the Medical Image Annotation task of 
ImageCLEF 2008 [2]. While in previous participations [3] [4] we approached this 
task as a domain-independent machine learning problem, as our areas of expertise did 
not include image analysis research, a lot of effort was invested during the last year to 
develop our own image analysis system. Thus, the main purpose of our participation 
in this task was to test and evaluate this system in-depth and determine the optimum 
settings to use in the classification module. In the following sections, we will give an 
overview of our approach and describe and analyze the results achieved. 
 MIRACLE at ImageCLEFannot 2008 729 
2   Description of the System 
Our system is composed of two different functional modules. First, the Feature Ex-
traction module is in charge of the extraction of a variety of feature descriptors for a 
given image. It has been entirely developed during the last year, using MATLAB.  
Each image is first converted to gray-scale and rescaled to 256x256 pixels. Then, 
the following feature descriptors are extracted: gray histogram (128 levels of gray), 
image statistics (mean, median, variance, maximum singular value, skewness and kur-
tosis), Gabor features (4 scales, 6 filter orientations), fractal dimension, Discrete Co-
sine Transform, Discrete Wavelet Transform, Tamura features (coarseness, contrast, 
directionality), and co-occurrence matrix statistics (energy, entropy, contrast, homo-
geneity, correlation). Both global features for the whole image and local features for 
64x64 blocks are obtained. All features are linearly combined (weight=1), and no fea-
ture selection is carried out. The final feature vector contains 3,741 descriptors. 
On the other hand, the Classification module determines the IRMA code associated 
to a given image, basically comparing its feature vector and the feature matrix of the 
training set. The classifier is internally composed of two blocks: an initial module in 
charge of selecting those images in the training set whose vectors are at a distance 
lower than a given threshold from the vector associated to the image to classify, and 
then a second module that actually generates the IRMA code, depending on the codes 
and similarity of nearby images.  
3   Experimental Results and Discussion 
Four runs were submitted. All of them are based on the classical k-Nearest Neighbour 
algorithm [5] with a specific adaptation to generate the output class. The IRMA code 
is generated from the combination of the codes of the first k images in the training set 
that are most similar to the image to classify. The combination consists of a simple 
“addition” of code strings characters in which, if both characters are different, the re-
sult is the wildcard “*” representing the ambiguity (“hesitation” to choose).  
Additionally, two runs use relevance feedback (RF) with the first n images in the 
training set that are at a lowest distance. Feature vectors of those images are added 
and averaged to build a new feature vector that is used for querying the system again. 
Results are shown in Table 1. The “Error score” is defined [2] so as to penalize 
wrong decisions in which there are few possible choices over wrong decisions with 
many possible choices. Furthermore, it also penalizes wrong decisions higher up in 
the IRMA code hierarchy over wrong decisions lower down in the hierarchy. The 
“Well Classified” column shows the images with complete correct predicted code.  
Table 1. Results of experiments 
Run1 Error Score Well Classified 
2I-0F 190.38 219 
3I-0F 187.90 144 
2I-2F 190.38 219 
3I-2F 194.26 167 
1
 Run identifiers hold k (neighbours) and n (images for relevance feedback) 
730 S. Lana-Serrano et al. 
The best score is achieved by the run that combines the codes of the first 3 images, 
with no relevance feedback. Moreover, runs using the codes of the first 2 images 
seem to get the same final score no matter if relevance feedback is considered or not. 
Although it is not shown in the table, no image code was completely wrong, i.e., there 
was at least one valid code character for every annotated image.  
One important issue related to the classification algorithm is the fact that, as the 
cost of making an incorrect decision is higher than the cost of not actually making a 
decision, the choice  criteria of the system is biased for “hesitation”, i.e., the system is 
very cautious and assigns a wildcard “*” if there is any kind of ambiguity. As con-
firmed by results shown in Table 1, when the number of codes taken for generating 
the final IRMA code increases, so ambiguity does, thus the number of complete cor-
rect predictions decreases and also the error score.  
Strategies for improving this decision criterion must be found for next years. One 
possible strategy is to assign a different relevance to each result according to its posi-
tion P in the list, with different weighting factors, such as (1/P), (1-P), etc. 
Comparing to other groups, we achieve average results and rank 4th (out of 6). 
The analysis axis-by-axis shows interesting results. For each of the four axis of the 
IRMA code, the “Error Score” shown in Table 2 is calculated as the sum of the errors 
made for each image, and the number of images in which the full prediction of the 
axis (i.e., no wildcards in the output) is correct. 
Table 2. Axis-by-axis analysis 
 
T-Axis D-Axis B-Axis A-Axis 
Run Error Score 
Well 
Classified 
Error 
Score 
Well 
Classified
Error 
Score 
Well 
Classified
Error 
Score 
Well 
Classified 
2I-0F 5.24 852 318.04 381 362.56 283 75.6 789 
3I-0F 6.32 808 309.78 293 367.82 206 67.67 735 
2I-2F 5.24 852 318.04 381 362.56 283 75.67 789 
3I-2F 6.12 818 322.09 318 374.74 235 74.07 744 
The Technical axis achieves the best error score. However, this is somehow mis-
leading, as the whole image database holds only 4 possible values (codes) for this axis 
(“1121”, “1123”, “1124” and “112d”), thus making the decision easier than for the 
other axes. In fact, 93% of the images have the value “1121”, i.e., it is possible to 
achieve a good error score even with a simple majority classifier (such as ZeroR [5]). 
Obviously, these different distribution and frequency of code values for each axis will 
be taken into account in future participations. 
Moreover, the prediction for the Anatomical (A) axis shows a significant differ-
ence with respect to the Direction (D) and Biological (B) axes. We must conclude that 
the chosen features are not useful for modelling the image concepts that are intrinsic 
to those axes. Particularly, in the case of the D-axis the differences among images are 
very subtle and strongly rely on different brightness or contrast areas in the left or 
right side (or top or bottom) of the image. A possible approach for improving the 
classification of those axes is to give more weight to local features with respect to the 
global analysis, as local feature can model the differences among image regions. 
 MIRACLE at ImageCLEFannot 2008 731 
4   Conclusions and Future Work 
Based on the analysis performed over each axis, the first conclusion to be drawn is 
that the first weak point of our experiments is the prediction of the Direction (D) and 
Biological (B) axis. Some extra effort must be invested on determining which image 
features could be most useful to predict those axis. 
Another aspect is that the calculation of the distance among vectors assigns the 
same weight to every dimension of the vectors, regardless of the nature of the feature 
to which this component belongs and/or the number of components belonging to that 
feature. This was actually our mistake when carrying out the experiments and the fea-
ture matrix should have been divided into the different feature sub-matrixes that em-
ploy different distances for calculating similarity and are combined to each other us-
ing different weight strategies. This fact will be taken into account for next years. 
Acknowledgements. This work has been partially supported by the Spanish R+D 
National Plan, project BRAVO (Multilingual and Multimodal Answers Advanced 
Search–Information Retrieval), TIN2007-67407-C03-03, and by the R+D Regional 
Plan of Madrid, project MAVIR (Enhancing the Access and the Visibility of 
Networked Multilingual Information), S-0505/TIC/000267. 
References 
1. Lana-Serrano, S., Villena-Román, J., González-Cristóbal, J.C., Goñi-Menoyo, J.M.: 
MIRACLE at ImageCLEFannot 2008: Classification of Image Features for Medical Image 
Annotation. In: Working Notes of the 2008 CLEF Workshop, Aarhus, Denmark (2008) 
2. Deselaers, T., Deserno, T.M.: Medical Image Annotation in ImageCLEF 2008. In:  
Peters, C., et al. (eds.) CLEF 2008. LNCS, vol. 5706, pp. 523–530. Springer, Heidelberg 
(2009) 
3. Lana-Serrano, S., Villena-Román, J., González-Cristóbal, J.-C., Goñi-Menoyo, J.M.: 
MIRACLE at ImageCLEFanot 2007: Machine Learning Experiments on Medical Image 
Annotation. In: Peters, C., Jijkoun, V., Mandl, T., Müller, H., Oard, D.W., Peñas, A., Petras, 
V., Santos, D. (eds.) CLEF 2007. LNCS, vol. 5152, pp. 597–600. Springer, Heidelberg 
(2008) 
4. Villena-Román, J., González-Cristóbal, J.C., Goñi-Menoyo, J.M., Martínez Fernández, J.L.: 
MIRACLE’s Naive Approach to Medical Images Annotation. In: Working Notes for the 
CLEF 2005 Workshop, Vienna, Austria (2005) 
5. Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd 
edn. Morgan Kaufmann, San Francisco (2005) 


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MIRACLE at ImageCLEFannot 2008: Nearest Neighbour Classification of Image Feature Vectors for Medical Image Annotation

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