Title :
Neuroimaging biomarker based prediction of Alzheimer´S disease severity with optimized graph construction
Author :
Sidong Liu ; Weidong Cai ; Lingfeng Wen ; Dagan Feng
Author_Institution :
Biomed. & Multimedia Inf. Technol. (BMIT) Res. Group, Univ. of Sydney, Sydney, NSW, Australia
Abstract :
The prediction of Alzheimer´s disease (AD) severity is very important in AD diagnosis and patient care, especially for patients at early stage when clinical intervention is most effective and no irreversible damages have been formed to brains. To achieve accurate diagnosis of AD and identify the subjects who have higher risk to convert to AD, we proposed an AD severity prediction method based on the neuroimaging predictors evaluated by the region-wise atrophy patterns. The proposed method introduced a global cost function that encodes the empirical conversion rates for subjects at different progression stages from normal aging through mild cognitive impairment (MCI) to AD, based on the classic graph cut algorithm. Experimental results on ADNI baseline dataset of 758 subjects validated the efficacy of the proposed method.
Keywords :
diseases; graph theory; image coding; medical image processing; neurophysiology; patient care; pattern recognition; AD diagnosis; AD severity prediction method; ADNI baseline dataset; Alzheimer´s disease severity prediction; classic graph cut algorithm; clinical intervention; empirical conversion rate encoding; global cost function; mild cognitive impairment; neuroimaging biomarker based prediction; normal aging; optimized graph construction; patient care; progression stages; region-wise atrophy pattern; Alzheimer´s disease; Magnetic resonance imaging; Neuroimaging; Performance gain; Prediction algorithms; Support vector machines; Alzheimer´s disease; neuroimaging; prediction;
Conference_Titel :
Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on
Conference_Location :
San Francisco, CA
Print_ISBN :
978-1-4673-6456-0
DOI :
10.1109/ISBI.2013.6556779