Expert systems for image processing

The successful application of image processing requires an understanding of the image-processing operations, of the class of images to be processed and of how the imaging process works. Nazif and Levine [Nazifl984] were the first to propose that developers attempt to represent this knowledge explicitly in the software. There are now several kinds of system which may be considered as expert systems for image processing and analysis (ESIPAs). Matsuyama [Matsuyama1993], reviewing this work proposes a general architecture for ESIPAs comprising:

• a user interface

• a library of image-processing operators

• knowledge about image-processing techniques • database of characteristics of image data • image processing and analysis executor • reasoning engine

• a consultation system for image processing • knowledge-based program composition system • rule-based system for image segmentation

• goal-directed top-down image segmentation system

The last two categories are dealt with in later sections of this chapter. There is at least one example of a consultation system for medical image analysis [Rosis 1992], which provides an intelligent interface to image-processing operators. A more developed example is of a knowledge-based program composition system, as described by [Kulikowskil995].

3.3.2.1 MICRORAY-1: a consultation system for medical image analysis

De Rosis et al. [Rosis 1992] describe a system aimed at guiding a doctor using image analysis techniques in the interpretation of a medical image.

Images and image processing

The system is designed to be independent of any particular imaging modality or set of image-processing algorithms. The published paper includes examples of image- processing routines such as linear contrast enhancement used in the analysis o f CT images of cancer in the eyelid.

Symbolic knowledge

The system contains application-independent knowledge about various goals of image analysis, the methods for achieving them and about the image analysis software functions supported by the system at any one time. This information is represented in a network of frames. Information acquired during the consultation about the particular problem the system is being used to address is represented in frames which describe

both the image and the anatomy it depicts. The system also contains a simple set of rules which implement an image analysis strategy, in essence that goals are achieved by applying appropriate operators. The strategy involves testing that the application condi­ tions for an operator have been met and, once it has been applied, checking that the goal has been achieved.

Operation

The system is used by a clinician who is not expert in the use of image analysis tools but who wishes to use them in interpreting an image. The user views the image and then enters into the system an image-analysis goal, such as ‘enhance contrast’. The system elicits information from the user about the image, applies an appropriate image- processing operator to the image then requests an assessment of its success in achieving the goal. The design provides explanations of the system’s behaviour and stored chunks of text are included to describe the various supported operations.

Combining symbols and signal data

The connection between the signal data and the symbolic reasoning is mediated entirely by the user, who supplies the information about the appropriate image- processing goals and the effects of the image processing in achieving those goals.

3.3.2.2 VISIPLAN: a knowledge-based program composition system

The system [Kulikowskil995] is a knowledge-based system for automatically composing image analysis programs. The aim is allow the automated segmentation of M RI scans to be performed in response to queries couched in the terms typically used in patient records.

Images and image processing

This system is designed to work with MRI images. It employs four segmen­ tation methods: local binary thresholding, global thresholding using a single image, global thresholding using multiple images and line detection using primary edge segments. Two schemes are used to identify segmented regions: a region classifier and a line-fitting operator.

Symbolic knowledge

The system uses knowledge about structural relations between objects and their components, spatial relations between objects and their components, morphological characteristics of objects and their components and knowledge about the task specifi­ cation. The first three of these together represent a model of the expected objects and are represented in semantic networks. The fourth consists of knowledge about how decisions are made in the selection of reference objects, MRI modalities, foci, focusing operators, segmentation methods and recognition schemes. This knowledge is repre­ sented using production rules.

Operation

The user specifies the analysis in terms of a goal, available image modalities and a reference object. For a given task, an abstract plan is formulated in terms of a set of reference objects which must be found. For each reference object the system selects a focus, focusing operator, segmentation method and recognition scheme.

Combining symbols and signal data

The reference objects are defined in the knowledge base in terms of their spatial characteristics, their spatial relationships to other objects and their spatial separability from other objects and it is on the basis of these definitions that the segmentation operator is chosen. The choice of recognition scheme (region-based or line-based) is determined by the morphology of the object. So the symbolic information which

describes objects is used to identify a set of image features which image processing can be used to detect. Once these features are detected, no further use is made of the signal information in the region which is taken to correspond to the object.