<p>In the last several years, there has been an increased focus on connecting image processing and artificial intelligence. Especially in the field of medical image diagnostics the benefits for such integration is apparent. In this paper we present use of the Common Object Request Broker Architecture (CORBA), as the mean for connecting existing systems for image processing and artificial intelligence. To visualize this, we will use CORBA for connecting Dynamic Imager and JavaCreek. Dynamic Imager is an image processing software, that is especially suitable for setting up and test customized sequences of image processing operations. JavaCreek is an artificial intelligence software based on the Case-Based Reasoning (CBR) theory.</p><p>After connecting the two systems with CORBA, we proceed develop the specific image processing methods for data gathering, and a knowledge base for diagnosis in the artificial intelligence system. The image processing methods and the knowledge base are produced for one special knowledge domain, for visualizing how the proposed system can help in medical image diagnostics.</p><p>The task we use to visualize our approach, is detecting malignancy in breast tumors, from magnetic resonance (MR) images taken over time as contrast agents is injected. This is from a reasonably new method for deciding if a tumor is malignant or benign. All image processing methods and the knowledge base is produced to let the two systems cooperate to find and diagnose tumors.</p><p>The image processing methods, the knowledge model, and the selected software with CORBA connection, was the basis for our system implementation. The implementation was tested with data gathered during the development of the clinical method for determining if a tumor is malignant, from the MR images. In all 127 patient cases was available, where 77 has malignant tumors in the gathered images. The results was then compared with diagnosis methods based on manual detection, and on other image processing methods. Although the found results were promising, there was also found several areas for future work.</p>

In the last several years, there has been an increased focus on connecting image processing and artificial intelligence. Especially in the field of medical image diagnostics the benefits for such integration is apparent. In this paper we present use of the Common Object Request Broker Architecture (CORBA), as the mean for connecting existing systems for image processing and artificial intelligence. To visualize this, we will use CORBA for connecting Dynamic Imager and JavaCreek. Dynamic Imager is an image processing software, that is especially suitable for setting up and test customized sequences of image processing operations. JavaCreek is an artificial intelligence software based on the Case-Based Reasoning (CBR) theory. After connecting the two systems with CORBA, we proceed develop the specific image processing methods for data gathering, and a knowledge base for diagnosis in the artificial intelligence system. The image processing methods and the knowledge base are produced for one special knowledge domain, for visualizing how the proposed system can help in medical image diagnostics. The task we use to visualize our approach, is detecting malignancy in breast tumors, from magnetic resonance (MR) images taken over time as contrast agents is injected. This is from a reasonably new method for deciding if a tumor is malignant or benign. All image processing methods and the knowledge base is produced to let the two systems cooperate to find and diagnose tumors. The image processing methods, the knowledge model, and the selected software with CORBA connection, was the basis for our system implementation. The implementation was tested with data gathered during the development of the clinical method for determining if a tumor is malignant, from the MR images. In all 127 patient cases was available, where 77 has malignant tumors in the gathered images. The results was then compared with diagnosis methods based on manual detection, and on other image processing methods. Although the found results were promising, there was also found several areas for future work.