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An Architecture for Implementing Customizable Medical Image Processing Systems

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An Architecture for Implementing Customizable Medical Image Processing Systems MEDINF098 B. Cesnik et al. (Eds) Amsterdam: lOS Press © 1998 1M/A. All rights reserved An Architecture for Implementing Customizable Medical Image Processing Systems Athanasios M. Demiris, Carlos E. Cardenas S., Manuela H. Makabe, Hans-Peter Meinzer Deutsches Krebsforschungszentrum, Heidelberg, Germany Abstract nario in system development. According to this scenario smaller systems addressing specific topics are created sepa­ Monolithic image processing systems containing a superset of rately on demand and may be integrated into a larger system in imaging algorithms are difficult to use and require specialized a' component-like manner. Creating specialized components knowledge ofimage processing. Thus they increase the work­ addressing specific problems instead of all-purpose systems load ofmedical personnel instead ofmaking the work situation enforces user centered design and development as well as easier. Customizable medical image processing systems on the encapsulation of overcomplicated functionality, which is feasi­ other hand may be easily adapted to address various problems ble, only when a concrete and restricted application domain is . in the medical image processing domain integrating only the addressed. necessary subset ofimage processingfunctionality presented in an intuitive way. One possible instantiation ofthat scenario is when an end user using an extensible platform system, which we characterize as a In this work we present an architecture for creating customiza­ customizable medical image processing system, contacts soft­ ble image processing systems for the medical domain. We ware developers every time the functionality needs to be address three major topics: 1.) easy, goal-oriented customiza­ extended to target a new problem, like volumetric measure­ tion ofimaging systems by using a generalized algorithm model ments for pathologic changes in the liver, cancer detection in and repository, 2.) dynamic, data-oriented parameterization of mammography, visualization ofthe beating heart etc. In order the selected algorithms and 3.) semi-automated generation of for developers to react to these needs in a shorter time and thus user interface components for each new algorithm to be reduce the period oftime from demand to delivery (lead time), inserted in an imaging system based on cognitive ergonomics. an advanced, easy to customize building tool is needed. A fur­ We conclude with the presentation ofan initial implementation ther advantage ofthe use of such a development tool is the con­ ofthe architecture in form ofan object-orientedframework for sistency of the developed applications. the creation ofcomponents for customizable medical imaging When customizing or creating a new software system for the systems. medical imaging domain there are some aspects that delay the response to a request for such a system. An example could be Keywords the request for a volumetric system in liver surgery. Such a sys­ Object-Oriented Frameworks; Algorithm Repository; Imaging tem requires methods for retrieving and displaying the image Systems; User Interfaces; Ergonomics data, algorithms for the segmentation, i.e. identification of healthy and pathologic tissue in the images, the selection of the Introduction appropriate algorithms for image processing, subsequently the estimation of optimal parameter sets for these algorithms to The image related part ofthe work of physicians and especially operate in a meaningful way on the given data, and fmally the radiologists and surgeons has been continuously enhanced by creation of a user interface for the resulting system. An archi­ computer supported components within the past decades. One tecture that supports developers in this process offers means for goal ofthe developers ofsuch systems was to ensure flexibility reducing the time spent with all these steps. In this paper we are by supplying their end users with a maximum of functionality presenting such an architecture and its partly implementation in in order to cover for all possible cases that might arise in the form of an object-oriented framework [1]. future. The results were monolithic systems containing too many features and being difficult to use. The level on which the Materials and Methods use of these systems was taking place was closer to the image processing part and farther from the actual end user needs. The work presented here started with the creation ofa volumet­ The advances.in software engineering within the past years and ric system for liver resection planning. We developed VolMes, especially in object-oriented technology gave rise to a new see- a system for image segmentation and quantitative measure- 1100 1101 A.M Demiris Developer of image processing routines Medical End User query algorithms or query/store parametersets insert new algorithm ~~ stausuca/observauons GUI creation support Repository and dynamic parameter calculations store/retrieve L-f-~--F-.f-----+ Database management system Abstract database connection interface Figure 2 - The global scenario ofthe architecture ments in contrast enhanced images of liver tissue (CT or MR) Instead ofcustomizing VolMes statically by extending and rec­ containing tumors or metastatic regions (see figure 1). ompiling we decided to design a dynamic solution that would The system contains image and DICOM-information display, address the quick replacement of algorithms by looking up semiautomatic and manual segmentation facilities. It was cre­ alternative algorithms' according to the new goal and a flexible ated according to ergonomic design criteria and in continuous estimation of default parameters for these algorithms every time cooperation with clinicians (user centered design). in order to be the kind ofdata set changes. These aspects are dealt with in our smoothly integrated in clinical routine. architecture by an algorithm repository and a component for dynamically estimating parameters for various types of image data of different anatomical regions. Subsequently we decided to integrate the new algorithms with pre-customized user inter­ face components for the control of these algorithms. We address this problem in our architecture with a model for auto­ matically creating user interface elements according to ergo­ nomic criteria for every new algorithm. In figure 2 we show the all-round scenario and in the next paragraphs we explain how the corresponding parts ofthe architecture are designed. A generic medical image processing algorithm model and repository When trying to select the algorithms needed to solve a given problem the developers usually look up possible solutions in their own "arsenal" (or the ones oftheir teams). Usually devel­ opers get acquainted with a small set of algorithms they use over and over and thus may skip other solutions. The documen­ tation of the algorithms takes place in a rather informal way thus allowing discrepancies in the way it is done among various developers. Less experienced developers sometimes do not Figure 1 - A screenshotofthe image segmentation andvolumet- have the chance to profit from existing solutions due to the ric system VolMes complete lack of documentation especially when the developers The system was subsequently used in other medical domains of existing algorithms have left the team. (e.g. heart segmentation) which led to the conclusion that for A central algorithm repository is definitely a way to overcome these cases other default settings for the parameters of the tools these barriers and reduce the amount of time needed for the and in many cases completely different tools were needed. selection of algorithmic material. Such a repository allows a 1102 Imaging andImage Management faster lookup of algorithms according to several criteria espe­ repository along with the algorithm. When a new algorithm is cially goal-oriented and not only implementation-oriented ones. used or an existing one is to be applied to a new case there are For an efficient repository to be created a flexible data model is no default values for the algorithm's parameter set (its signa­ neededIn order to ensure a general character of such a reposi­ ture). For these cases we developed a component estimating tory model we concentrated on extending existing models of parameter sets in an iterative process. This component looks up general algorithms foundin programming languages and speci- initial settings in the repository or calculates random ones. It fications of standards like the reflective! object-oriented pro­ applies the algorithm to the image data and evaluates the result­ gramming languages Smalltalk [2] and Java [3] and the ing binary image according to a series ofmeasurements. specification of the Object Database Standard by OMG 14] as We introduced a domain model containing relative measure­ well as the CORBA [5] specification. ments and anatomical landmarks for the elimination of the In our model we introduced the notion ofthe algorithm as a spe­ interpersonal variation for various regions ofthe body and con­ cialization ofa software building block. We described the algo­ tinuouslyextend the model to include new regions. These meas­ rithm in terms ofits signature (the parameters and return value) urements include: and its context. By context we mean both the kind of and the size ofthe segmented object relative to an easy to detect restrictions on the data to be processed (such as the format or organ (e.g. liver to thorax in CT), the datatype of a digital image) as well as the execution envi­ center ofinertia ofthe
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