M ICROCALCIFICATION
DETECTION USING NEURAL NETWORKS
László LASZTOVICZA
Advisor: Béla PATAKI
I.
Introduction
Breast cancer is a common form of cancer among women. The cause of this illness is unknown so
prevention is not possible. The only way for an effective treatment is to detect the disease in its early
state. Mammography is the best known method for early detection [1]. In a mammographic session at
least four images are taken and in order to make a reliable diagnosis each image have to be
evaluated. It therefore requires a large amount of human resources and money. The main goals of a
supporting computer-aided diagnosis (CAD) system is to increase the effectiveness of the
examination by drawing attention to the suspicious cases and decrease the costs by filtering out
normal images.
One of the most important symptoms of the cancer is the presence of microcalficitions. The
subject of this article is a method for the detection of microcalcifications. This method uses neural
networks to perform this task.
II. A neural method for microcalcification detection
Microcalcifications are very diverse in size, shape and brightness so an exact detection criterion
cannot be given easily. Neural networks have been proven to be useful in the solution of such pattern
recognition tasks. We used a previously presented [2] hierarchical neural network architecture with
some modifications and extensions [3]. This architecture is shown in Figure 1.
Processing
directions
Hierarchical Neural Network
Neural
network #1
Neural
network #2
Neural
network #3
Image pyramid
Feature extraction
Output
Neural classification #1
Figure 1: A hierarchical neural network architecture
This architecture is based on a multiscale decomposition of an image, and a hierarchy of neural
networks. First the input image is sampled and an image pyramid is built, the resolution is halved at
each level. Next a number of features are extracted from each image. The processing works pixel-bypixel so the feature extraction assigns a vector for each pixel which components are the differenct
features. The last step is the neural classification that is based on the vectors resulted from the feature
extraction. This architecture was embedded in an ensemble context to take advantage of the different
solutions given by neural networks trained diversely. Then this ensemble was modularized to further
improve performance and stability of the solution [3].
III. Experimental analysis
The performance of the system was analysed on two different image database. These were the
MIAS [4] and the DDSM [5] databases. The neural networks, the decision system and the location
procedure were thoroughly tested in the experiments. The results show that the system is capable of
detecting microcalcifications, however the parameters of the system should be changed in order to
ensure the same performance. We experienced that the decision system is much more sensitive to the
circumstances than the neural networks and therefore it should be reconsidered in the future research.
The performance of the system on 6440 ROIs (regions of interest) extracted from the MIAS
database was 82% true positive and 72% true negative. There are only preliminary tests on the
DDSM database. These show that the neural networks detect most of the calcifications without the
need of retraining, but the decision system performs poorly. The test set contained 1600 ROIs
extracted from different images containing microcalcifications. The neural networks detected almost
all of the calcifications while the decision system marked the ROIs as positive in less than 10% of the
cases.
IV. Conclusion and future work
A neural network based system has been presented that is capable of detecting microcalcifications.
The thorough analysis of this system showed that its parameters are sensitive to the properties of the
images like the size or the resolution. This stands especially to the decision system.
The future work includes the analysis of neural learning which has central influence on the
performance and the improvement of the decision system to decrease its sensitivity toward the
parameters of the images.
References
[1] R. Highnam, M. Brady. Mammographic Image Analysis. Kluwer Academic Publishers R, 1999.
[2] P. Sajda and C. Spence. Learning Contextual Relationships in Mammograms using a Hierarchical Pyramid Neural
Network, IEEE Transactions on Medical Imaging 21 (3) (2002)
[3] László Lasztovicza, Béla Pataki, Nóra Székely, Norbert Tóth, Neural Network Based Microcalcification Detection
in a Mammographic CAD System, Proceedings of the Second IEEE International Workshop on Intelligent Data
Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS'2003), Lviv, Ukraine, 810 September, 2003, pp. 319-323.
[4] J. Suckling et al. "The Mammographic Image Analysis Society Digital Mammogram Database" Exerpta Medica.
International Congress Series 1069, pp. 375-378, 1994. http://www.wiau.man.ac.uk/services/MIAS/MIASweb.html
[5] M. Heath K. Bowyer, D. Kopans, R. Moore and P. Kegelmeyer Jr., The Digital Database for Screening
Mammography, in The Proceedings of the 5th International Workshop on Digital Mammography (Toronto, Canada,
June 2000), Medical Physics Publishing (Madison, WI), ISBN 1-930524-00-5.