Segmentation of Medical Images Using CBR

Abstract

This paper describes a case based reasoning system that is used to guide the parameters of a segmentation algorithm. Instead of using a fixed set of parameters that gives the best average result over all images, the parameteres are tuned to maximize the score for each image separately. The system's foundation is a set of 20 cases that each contains one 3D MRI image and the parameters needed for its optimal segmentation. When a new image is presented to the system a new case is generated and compared to the other cases based on image similarity. The parameters from the best matching case are then used to segment the new image. The key issue is the use of an iterative approach that lets the system adapt the parameters to suit the new image better, if necessary. Each iteration contains a segmentation and a revision of the result, and this is done until the system approves the result. The revision is based on metadata stored in each case to see if the result has the expected properties as defined by the case. The results show that combining case based reasoning and segmentation can be applied within image processing. This is valid for choosing a good set of starting parameters, and also for using case specific knowledge to guide their adaption. A set of challenges for future research is identified and discussed at length.