MR tissue characterization of intracranial tumors by means of texture analysis

Tissue characterization of the human brain has been performed by texture analysis of proton relaxation time images using a standard MR whole body imager operating at 1.5 T. A combined CP/CPMG multi-echo, multislice sequence was used to measure T1 and T2 in each pixel with an uncertainty not exceeding 10%. In a prospective clinical study, 12 patients with histologically confirmed brain tumors were investigated. For each ROI in the calculated T1 and T2 parameter images, texture parameters originating from the grey level distribution, the gradient distribution, the grey level co-occurrence matrix, and the grey level runlength histogram were used for classification and discrimination between tissues. All regions corresponding to the normal brain tissue (white matter, grey matter, cerebrospinal fluid) were successfully discriminated from each other as well as from the pathological tissue parts (edema and tumor). The classification of 10 edematous and 8 tumorous tissue regions yielded only one misclassification. Together with additional rules, these discrimination rules formed the knowledge base of an expert system for segmentation of the brain images. In cases of tumors without Gd-DTPA contrast medium uptake or in cases of Gd-DTPA contraindication, segmentated images can help solve nontrivial diagnostical problems such as delineating the target volume in radiation therapy.