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Authors
Pinti, Antonio

Identifier
10.3182/20090812-3-DK-2006.00046

Index Terms
Biomedical imaging systems

Abstract
In the diagnosis using MRI images, image segmentation techniques play a key role. Existing segmentation methods are generally based on the features such as grey level and texture. However, these methods canÂ’t identify the physical significance of segmented objects from image because the general features such as grey level can not take into consideration the 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 incorporating the specific medical knowledge. All of these systems focus on specific applications and are not normalized and structured. So they lack of certainty and precision when being applied in other contexts. 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 which permits to formalize non structured and non normalized medical knowledge from various medical images. A user friendly interface has been constructed so that medical knowledge can be integrated into this data structure in an interactive way. This system has been successfully applied to the classification of human thigh, crus, arm, forearm, and brain in MRI images

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