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Image Processing in Case-Based Reasoning: Commentary
P. Perner, A. Holt, and M. Richter
Image processing is a challenging field. The unique data (images) and the necessary
computation techniques require extraordinary case-representations, similarity measures and
CBR strategies to be utilised. An overview of the challenges of image processing and image
interpretation by introducing CBR strategies is given in Perner [Pern01].
Image interpretation is the process of mapping the numerical representation of an image into a
logical representation such as suitable for scene description. An image interpretation system
must be able to extract symbolic features from the pixels of an image (for example, irregular
structure inside the nodule, area of calcification, and sharp margin). This is a complex process
as the image passes through several general processing steps before the final symbolic
description is obtained. Image interpretation systems are becoming increasingly popular in
medical and industrial applications. The existing statistical and knowledge-based techniques
lack robustness, accuracy, and flexibility. New strategies are necessary that can adapt to
changing environmental conditions, user needs and process requirements. Introducing CBR
strategies into image interpretation systems can satisfy these requirements. CBR provides a
flexible and powerful method for controlling the image processing process in all phases of an
image interpretation system to derive information of the highest possible quality. Beyond this
CBR offers different learning capabilities, for all phases of an image interpretation system,
that satisfy different needs during the development process of an image interpretation system.
Perner [Per00] proposes a system that uses CBR to optimize image segmentation at the low
level unit according to changing image acquisition conditions and image quality. The
intermediate-level unit extracts the case representation used by the high-level unit employed
to dynamically adapt image interpretation. The system works on different case representations
such as a graph-based representation for the cases of the high-level image description and the
raw image matrix for the low-level image representation. Therefore the system uses different
CBR strategies for the reasoning and learning part one is based on structural similarity and the
other is based on the digital image distance.
Grimnes and Aamodt [GrA96] present a system that integrates CBR into a task-oriented
model-based system for the interpretation of abdominal CT images. A case-based reasoner
working on a segment case-base contains the individual image segments. These cases with
labels are considered indexes for another case-based reasoner working on an organ
interpretation case-base. There system is based on a propose-critique-modify learning cycle.
Jarmulak [Jar98] presents a system for ultra-sonic B-scans that are one-dimensional signals.
He presents a tree-based retrieval strategy. Micarelli et al. [MNS00] applies CBR to scene
recognition. They calculated image properties from images and stored them into a case base.
They used the Wavelet transform because it is scale-independent, but this limits their
similarity measure to consider only object rotation. The application of CBR to image
segmentation for CT images from the brain is described in [Per99]. Different learning
strategies in a hierarchy of structural cases are presented in Perner [Per98] [Per03]. Learning
case representation and improving the system performance by controlling the similarity
measure is described in [PPM02]. There is also a need for mining raw information into more
general cases [PeJ04]. Making object recognition more robust against model variation is
described in [PeB04].
The application of CBR image interpretation to health monitoring and biotechnology is
described in [PGPFE03].
A new challenging application field are geographical information systems. This kind of
application requires special spatial problem solving techniques and spatial similarity
measures. The first application of CBR to geographic information systems was reported by
Holt and Benwell [HoB99]. Their approach consists of combining case-based reasoning with
geographical information systems to form a hybrid system to solve spatial problems in soil
classification. Carswell et al. [CWDB02] described in their paper a spatial similarity measure
for comparing the location objects in different images.
Several systems have been developed which apply CBR for image retrieval and interpretation
at the symbolic level. Usually the images are not processed rather the symbolic terms are
user-specified. There is a system by Haddad et al. [HaA97].for the detection of coronary heart
disease. Another system has been developed by Macura and Macura [MaM95] for retrieval of
radiological images. Jaulent et al [JBZD98] apply the system for the diagnosis of breast
cancer in histopatology.
Literature
A completely different application of CBR to image processing has been described by Ficet-
Cauchard et al. [FPR99].They apply CBR for the development of the image processing steps
of formerly unknown image processing problems by using past experiences and plan
adaption.
Finally, in Perner [Per02] is built a bridge between the work in CBR and the one in
dissimilarity classification which became recently important in pattern recognition.
Most of the work described here has been presented at the ICCBR and ECCBR. There has
also been established a working group for CBR in image processing under the umbrella of
IAPR Technical committee TC 17 Machine Learning and Data Mining (www.ibai-
research.de/html/TC17) that has a major conference called Intern. Conference on Machine
Learning and Data Mining MLDM.
[Jar98] Jarmulak, J. (1998). Case-based classification of ultrasonic B-Scans: Case-base
organisation and case retrieval. In B. Smyth & P. Cunningham (Eds.) Advances in Case-
Based Reasoning (pp. 100-111). Berlin: Springer Verlag.
[GrA96] Grimnes, M. & Aamodt, A.(1996). A two layer case-based reasoning architecture for
medical image understanding, In I. Smith & B. Faltings (Eds.) Advances in Case-Based
Reasoning (pp. 164-178). Berlin: Springer Verlag.
[MNS00] Micarelli, A. Neri, A., & Sansonetti, G. (2000). A case-based approach to image
recognition, In E. Blanzieri & L. Portinale (Eds.) Advances in Case-Based Reasoning (pp.
443-454). Berlin: Springer Verlag.
[FPR99] Ficet-Cauchard, V., Porquet, C., & Revenu, M. (1999). CBR for the reuse of image
processing knowledge: A recursive retrieval/adaption strategy. In K.-D. Althoff, R.
Bergmann, & L.K. Branting (Eds.) Case-Based Reasoning Research and Development (pp.
438-453). Berlin: Springer.
[CWDB02] Carswell, James D., Wilson, David C., and Bertolotto, Michela. (2002). Digital
Image Similarity for Geo-Spatial Knowledge Management. In proceedings of 6th European
Conference on Case-Based Reasoning, Springer Verlag Lecture Notes in Artificial
Intelligence (ECCBR2002), Aberdeen, Scotland, September 2002
[HoB99] Holt A. and G.L. Benwell G.L., Applying Case-Based Reasoning Techniques in
GIS, Journal of Geograpical Information Science, 13(1):9-25, 1999
[HaA97] M. Haddad, K.-P. Adlassnig, G. Porenta, Feasibility analysis of a case-based
reasoning system for automated detection of coronary heart disease from myocardial
scintigrams, Artificial Intelligence in Medicine 9 (1997), 61-78.
[MaM95] R. Macura and K. Macura, MacRad: Radiology Image Resource with a Case-Based
Retrieval System, In: M. Veloso and A. Aamodt (eds.), Case-Based Reasoning: Research and
Development, Springer 1995, p. 43-45.
[JBZD98] Jaulent MC, Le Bozec C, Zapletal E, Degoulet P. Case based diagnosis in
histopathology of breast tumours. Medinfo. 1998;9 Pt 1:544-8.
[Per03] P. Perner, Incremental Learning of Retrieval Knowledge in a Case-Based Reasoning
System, In: K.D. Ashley and D.G. Bridge (Eds.), Case-Based Reasoning – Research and
Development, lnai 2689, Springer Verlag, Berlin, 2003, p. 422-436.
[Per02] P. Perner, Are case-based reasoning and dissimilarity-based classification two sides of
the same coin? Journal Engineering Applications of Artificial Intelligence, vol. 15/3, 2002,
pp. 205-216.
[PPM02] P. Perner,H. Perner, and B. Müller, Similarity Guided Learning of the Case
Description and Improvement of the System Performance in an Image Classification System,
In: S. Craw and A.Preece (Eds.), Advances in Case-Based Reasoning, ECCBR2002, Springer
Verlag, lnai 2416, 2002, p. 604-612.
[Per00] P. Perner. CBR Ultra Sonic Image Interpretation. In: E. Blanzieri and Lugio Portinale
(Eds.), Advances in Case-based Reasoning, Springer Verlag 2000, lnai 1898, 2000, p. 479-
481
[Per99] P. Perner, An Architeture for a CBR Image Segmentation System, Journal on
Engineering Application in Artificial Intelligence, Engineering Applications of Artificial
Intelligence, vol. 12 (6), 1999, p. 749-759
[Per98] P. Perner, Different Learning Strategies in a Case-Based Reasoning System for Image
Interpretation, Advances in Case-Based Reasoning, B. Smith and P.
Cunningham (Eds.), LNAI 1488, Springer Verlag 1998, S. 251-261
[Per01] P. Perner, Why Case-Based Reasoning is Attractive for Image Interpretation, D. Aha
and I. Watson (Eds.), Case-Bases Reasoning Research and Developments, Springer Verlag
2001, lnai 2080, p. 27-44
[PGPFE03] P. Perner, Th. Günther, H. Perner, G. Fiss, R. Ernst, Health Monitoring by an
Image Interpretation System - A System for Airborne Fungi Identification, In: Petra Perner,
Rüdiger Brause, Hermann-Georg Holzhütter (Eds.), Medical Data Analysis, Springer Verlag,
lncs 2868, 2003, p. 64-77
[PeJ04] P. Perner and S. Jähnichen, Case Acquisition and Case Mining for Case-Based Object
Recognition, In: P. Funk and P.A. González Calero, Advances in Case-Based Reasoning,
Springer Verlag 2004, lnai 3155, 616-629.
[PeB04] P. Perner and A. Bühring, Case-Based Object Recognition, In: P. Funk and P.A.
González Calero, Advances in Case-Based Reasoning, Springer Verlag 2004, lnai 3155, 375-
388.