An intelligent generalized system for tissue classification on MR images by integrating qualitative medical knowledge
In the diagnosis using MRI images, image segmentation techniques play a key role. Existing segmentation methods are generally based on basic image features such as grey level and texture. However, these methods cannot effectively identify physical significance of segmented objects from image because these basic image features such as grey level cannot take into consideration specialized medical knowledge, which is important when doctors study them manually using their own vision and experience. To deal with this problem, many tissue classification systems have been developed by integrating specific medical knowledge. All of these systems focus on specific applications and cannot be normalized and structured. Therefore, adaption of such systems to other contexts is rather difficult. In this paper, we propose an intelligent generalized tissue classification system which combines both the Fuzzy C-Means algorithm and the qualitative medical knowledge on geometric properties of different tissues. In this system, a general geometric model is proposed for formalizing non-structured and non-normalized medical knowledge from various medical images. This system has been successfully applied to the classification of human thigh, crus, arm, forearm, and brain in MRI images.
tissue classification medical knowledge mri image segmentation fuzzy c-means