モルフォロジー処理を利用した頭部MR画像における小脳および脳幹部の自動抽出法

抄録

MR imaging is an important method for the diagnosis of diseases caused by various cerebral pathologies. Assessment of the volume reduction such as cerebral atrophy, SDAT (Senile Dementia of Alzheimer Type) and OPCA (Olivopontocerebellar atrophy) is very important in clinical practice. However, the assessment of the atrophy used to be performed by manual measurement or visual evaluation. Therefore, in order to diagnose by quantitative assessment, it is desirable to measure the regional volume automatically. In this study, we investigated an automated segmentation method of cerebellum and brainstem on MR images using morphological information. An automated method was consisted of the following three steps: (1) segmentation of the brain on MR images (2) segmentation of the cerebellum and brainstem on the brain images using mathematical morphology (3) correction of errors on the segmented images using 3-D information. The results indicated that the regions obtained by automated segmentation method were visually similar to those by manual method. An average of the rate of correctly recognized regions is over 70%. However, an average of the rate of unrecognized regions is over 10%. If segmentation accuracy is improved moreover, our method may provide the quantitative diagnostic information.