FEATURE SPACE ANALYSIS OF MRI

This paper presents an MRI feature-space image-analysis method and its application to brain tumor studies. The proposed method generates a t ransformed feature space in which the normal tissues (white matter, gr ay matter, and CSF) become orthonormal. As such, the method is expecte d to have site-to-site and patient-to-patient consistency, and is usef ul for identification of tissue types, segmentation of tissues, and qu antitative measurements on tissues, The steps of the work accomplished are as follows: (1) Four T-2-weighted and two T-1-weighted images (be fore and after injection of gadolinium) were acquired for 10 tumor pat ients. (2) Images were analyzed by an image analyst according to the p roposed algorithm, (3) Biopsy samples were extracted from each patient and were subsequently analyzed by the pathology laboratory. (4) Image -analysis results were compared with the biopsy results. Pre- and post surgery feature spaces were also compared, The proposed method made it possible to visualize the MRI feature space and to segment the image. In all cases, the operators were able to find clusters for normal and abnormal tissues. Also, clusters for different zones of the tumor wer e found. The method successfully segmented the image into normal tissu es (white matter, gray matter, and CSF) and different zones of the les ion (tumor, cyst, edema, radiation necrosis, necrotic core, and infilt rated tumor). The results agreed with those obtained from the biopsy s amples. Comparison of pre- with postsurgery and radiation feature spac es illustrated that the original solid tumor was not present in the se cond study, but a new tissue component appeared in a different locatio n of the feature space. This tissue could be radiation necrosis genera ted as a result of radiation.