RATIONALE AND OBJECTIVES: An automated method for identification of patients with cerebral atrophy due to Alzheimer's disease (AD) was developed based on three-dimensional (3D) T1-weighted magnetic resonance (MR) images. MATERIALS AND METHODS: Our proposed method consisted of determination of atrophic image features and identification of AD patients. The atrophic image features included white matter and gray matter volumes, cerebrospinal fluid (CSF) volume, and cerebral cortical thickness determined based on a level set method. The cortical thickness was measured with normal vectors on a voxel-by-voxel basis, which were determined by differentiating a level set function. The CSF spaces within cerebral sulci and lateral ventricles (LVs) were extracted by wrapping the brain tightly in a propagating surface determined with a level set method. Identification of AD cases was performed using a support vector machine (SVM) classifier, which was trained by the atrophic image features of AD and non-AD cases, and then an unknown case was classified into either AD or non-AD group based on an SVM model. We applied our proposed method to MR images of the whole brains obtained from 54 cases, including 29 clinically diagnosed AD cases (age range, 52-82 years; mean age, 70 years) and 25 non-AD cases (age range, 49-78 years; mean age, 62 years). RESULTS: As a result, the area under a receiver operating characteristic (ROC) curve (Az value) obtained by our computerized method was 0.909 based on a leave-one-out test in identification of AD cases among 54 cases. CONCLUSION: This preliminary result showed that our method may be promising for detecting AD patients.
RATIONALE AND OBJECTIVES: An automated method for identification of patients with cerebral atrophy due to Alzheimer's disease (AD) was developed based on three-dimensional (3D) T1-weighted magnetic resonance (MR) images. MATERIALS AND METHODS: Our proposed method consisted of determination of atrophic image features and identification of ADpatients. The atrophic image features included white matter and gray matter volumes, cerebrospinal fluid (CSF) volume, and cerebral cortical thickness determined based on a level set method. The cortical thickness was measured with normal vectors on a voxel-by-voxel basis, which were determined by differentiating a level set function. The CSF spaces within cerebral sulci and lateral ventricles (LVs) were extracted by wrapping the brain tightly in a propagating surface determined with a level set method. Identification of AD cases was performed using a support vector machine (SVM) classifier, which was trained by the atrophic image features of AD and non-AD cases, and then an unknown case was classified into either AD or non-AD group based on an SVM model. We applied our proposed method to MR images of the whole brains obtained from 54 cases, including 29 clinically diagnosed AD cases (age range, 52-82 years; mean age, 70 years) and 25 non-AD cases (age range, 49-78 years; mean age, 62 years). RESULTS: As a result, the area under a receiver operating characteristic (ROC) curve (Az value) obtained by our computerized method was 0.909 based on a leave-one-out test in identification of AD cases among 54 cases. CONCLUSION: This preliminary result showed that our method may be promising for detecting ADpatients.