AN ALGORITHM FOR AUTOMATIC SEGMENTATION AND CLASSIFICATION OF MAGNETIC-RESONANCE BRAIN IMAGES

In this article, we describe the development and validation of an auto matic algorithm to segment brain from extracranial tissues, and to cla ssify intracranial tissues as cerebrospinal fluid (CSF), gray matter ( GM), white matter (WM) or pathology. T1 weighted spin echo, dual echo fast spin echo (T2 weighted and proton density (PD) weighted images) a nd fast Fluid Attenuated Inversion Recovery (FLAIR) magnetic resonance (MR) images were acquired in 100 normal patients and 9 multiple scler osis (MS) patients. One of the normal studies had synthesized MS-like lesions superimposed. This allowed precise measurement of the accuracy of the classification. The 9 MS patients were imaged twice in one wee k. The algorithm was applied to these data sets to measure reproducibi lity. The accuracy was measured based on the synthetic lesion images, where the true voxel class was known. Ninety-six percent of normal int radural tissue voxels (GM, WM, and CSF) were labeled correctly, and 94 % of pathological tissues were labeled correctly. A low coefficient of variation (COV) was found (mean, 4.1%) for measurement of brain tissu es and pathology when comparing MRI scans on the 9 patients. A totally automatic segmentation algorithm has been described which accurately and reproducibly segments and classifies intradural tissues based on b oth synthetic and actual images. Copyright (C) 1998 by W.B. Saunders C ompany.