An Extenics Approach to MRI Classification

Magnetic resonance imaging (MRI) has become a useful modality since it provides unparallel capability of revealing soft tissue contrast as well as 3D visualization. One potential application of MRI in clinical practice is the parenchyma classification and segmentation of normal and pathological tissue. It is the first step to address a wide range of clinical problems. This paper presents a new spectral signature detection approach to magnetic resonance (MR) image classification. It is called the extension (extenics, extension theory), which can separate the blocks efficiently so as to reduce the noise effect upon tissues. This paper has demonstrated satisfactory noise-proof features of extension. A series of experiments is conducted and compared with the commonly used c-means method for performance evaluation. The results show that the Extensions method is a promising and effective technique for MR image classification.