ArticlePDF Available

Image Processing in Case-Based Reasoning

September 2005
The Knowledge Engineering Review 20(3):311-314

DOI:10.1017/S0269888906000671

Source
DBLP

Authors:

Petra Perner

Institute of Computer Vision and applied Computer Sciences IBaI

Alec Holt

University of Otago

Michael Richter

Technische Universität Kaiserslautern, University of Calgary, Germany

This commentary summarizes case-based reasoning (CBR) research applied to image processing. It includes references to the systems, workshops, and landmark publications. Image processing includes a variety of image formats, from computer tomography images to remote sensing and spatial data sets.

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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.

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The development of image interpretation systems is concerned with tricky problems such as a limited number of observations, environmental influence, and noise. Recent systems lack robustness, accuracy, and flexibility. The introduction of case-based reasoning (CBR) strategies can help to overcome these drawbacks. The special type of information (i.e., images) and the problems mentioned above provide special requirements for CBR strategies. In this paper we review what has been achieved so far and research topics concerned with case-based image interpretation. We introduce a new approach for an image interpretation system and review its components.

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. The development of an Image Processing (IP) application is a complex activity, which can be greatly alleviated by user-friendly graphical programming environments. Our major objective is to help IP experts reuse parts of their applications. A first work towards knowledge reuse has been to propose a suitable representation of the strategies of IP experts by means of IP plans (trees of tasks, methods and tools). This paper describes the CBR module of our interactive system for the development of IP plans. After a brief presentation of the overall architecture of the system and its other modules, we explain the distinction between an IP case and an IP plan, and give the selection criteria and functions that are used for similarity calculation. The core of the CBR module is a search/adaptation algorithm, whose main steps are detailed: retrieval of suitable cases, recursive adaptation of the selected one and memorization of new cases. The system’s implementation is presently completed; its functioning is described in a session showing the kind of assistance provided by the CBR module during the development of a new IP application.

Are Case-Based Reasoning and Dissimilarity-Based Classification Two Sides of the Same Coin?

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ENG APPL ARTIF INTEL

Petra Perner

Case-based reasoning (CBR) is used when generalized knowledge is lacking. The method works on a set of cases formerly processed and stored in the case base. A new case is interpreted based on its similarity to cases in the case base. The closest case with its associated result is selected and presented as output of the system. Recently, dissimilarity-based classification (DSC) has been introduced due to the curse of dimensionality of feature spaces and the problem arising when trying to make image features explicitly. The approach classifies samples based on their dissimilarity value to all training samples. In this paper we are reviewing the basic properties of these two approaches. We show the similarity of dissimilarity-based classification to case-based reasoning. Finally, we conclude that dissimilarity-based classification is a variant of case-based reasoning and that most of the open problems in dissimilarity-based classification are research topics of case-based reasoning.

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Image Segmentation is a crucial step if extracting information from a digital image. It is not easy to set up the segmentation parameter so that it fits best over the entire set of images, which should be segmented. In the paper, we propose a novel architecture for image segmentation method based on CBR, which can adapt to changing image qualities and environmental conditions. We describe the whole architecture, the methods used for the various components of the systems and show how it performs on medical images.

Image mining: Issues, framework, a generic tool and its application to medical-image diagnosis

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A tool and a methodology for data mining in picture-archiving systems are presented. It is intended to discover the relevant knowledge for picture analysis and diagnosis from the data base of image descriptions. Knowledge-engineering methods are used to obtain a list of attributes for symbolic image descriptions. An expert describes images according to this list and stores descriptions in the data base. Digital-image processing can be applied to improve imaging of specific image features, or to get expert-independent feature evaluation. Decision-tree induction is used to learn the expert knowledge, presented in the form of image descriptions in the data base. A constructed decision tree presents effective models of decision-making, which can be learned to support image classification by the expert. A tool for data mining and image processing is presented and its application to image mining is shown on the task of Hep-2 cell-image classification. However, the tool and the methodology are generic and can be used for other image-mining tasks. We applied the developed methodology of data mining in other medical tasks, such as in lung-nodule diagnosis in X-ray images, lymph-node diagnosis in MRI and investigation of breast MRI.

Incremental Learning of Retrieval Knowledge in a Case-Based Reasoning System

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Petra Perner

Case-base maintenance typically involves the addition, removal or revision of cases, but can also include changes to the retrieval knowledge. In this paper, we consider the learning of the retrieval knowledge (organization) as well as the prototypes and the cases as case-based maintenance. We address this problem based on cases that have a structural case representation. Two approaches for organizing the case base are proposed. Both are based on approximate graph subsumption.

MacRad: Radiology Image Resource with a Case-Based Retrieval System.

Conference Paper

Oct 1995
Lect Notes Comput Sci

We have compiled a case-based retrieval system for radiology, MacRad, that is structured around descriptors for radiology image findings. Our goal is to provide a feature-coded image resource that allows the user to formulate image content-based queries when searching for reference images. MacRad is implemented as a relational database with an image archive. Each image in the library is indexed according to its radiologic content. We structured an index for coding image content as a hierarchical image description index using the relational format. The hierarchical index of radiologic findings allows multilevel query formulation that depends upon the user's level of experience. The system uses rules to control the search direction within the case library and generate lists of diagnostic hypotheses for decision support. Rules are embedded within the database structure. At present, the case library consists of 300 cases and 3,000 images that present intracranial masses on skull X-rays, CTs, MRIs, and angiograms.