An Efficient Classification Method for Knee MR Image Segmentation

Abstract

Aiming at application to automated recognition of knee bone magnetic resonance (MR) images, an evolutional classification method called CBGA-LDIC is proposed. CBGA-LDIC finds an appropriate cell set towards efficient image segmentation. This method uses location-dependent image classification (LDIC), which is integrated by genetic algorithm (GA) combined with case based reasoning (CB). LDIC introduces a new but local heuristics for image segmentation, and defines multiple classifiers dependent on location. Each classifier is trained by Gaussian mixture model. CBGA-LDIC decomposes the whole image into some cells, makes a set of cells, and then trains classifiers. Since the knee bones and/or their formations are similar in their location, good combinations of cells seem useful for other clients and are stored in case bases. Thus this method is expected to produce the better results when good combinations of cells are selected from cases as initial individuals of GA, especially through its repetition on restarting GA. This is verified by some experimentations shown in this paper.