Abstract: When detecting and classifying hypo-metabolic regions in the brain to facilitate dementia diagnosis, a patient's brain scan image, generated using an FDG-PET scan, is compared to a plurality of hypo-metabolic region patterns in brain scan images associated with a plurality of types of dementia. In a fully automated mode, the patient's scan is compared to all scans stored in a knowledge base, and a type of dementia associated with a most likely match is output to a user along with a highlighted image of the patient' s brain. In a semi-automated mode, a user specifies two or more types of dementia, and the patient's scan is compared to scans typical of the specified types. Diagnosis information including respective likelihoods for each type is then output to the user. Additionally, the user can adjust a threshold significance level to increase or decrease a number of voxels that are included in hypo-metabolic regions highlighted in the patient' brain scan image.

Abstract: When detecting and classifying hypo-metabolic regions in the brain to facilitate dementia diagnosis, a patient's brain scan image, generated using an FDG-PET scan, is compared to a plurality of hypo-metabolic region patterns in brain scan images associated with a plurality of types of dementia. In a fully automated mode, the patient's scan is compared to all scans stored in a knowledge base, and a type of dementia associated with a most likely match is output to a user along with a highlighted image of the patient' s brain. In a semi-automated mode, a user specifies two or more types of dementia, and the patient's scan is compared to scans typical of the specified types. Diagnosis information including respective likelihoods for each type is then output to the user. Additionally, the user can adjust a threshold significance level to increase or decrease a number of voxels that are included in hypo-metabolic regions highlighted in the patient' brain scan image.