Spatio-temporal analysis of brain MRI images using hidden Markov models.
A rapidly increasing number of medical imaging studies is longitudinal, i.e. involves series of repeated examinations of the same individuals. This paper presents a methodology for analysis of such 4D images, with brain aging as the primary application. An adaptive regional clustering method is first adopted to construct a spatial pattern, in which a measure of correlation between morphological measurements and a continuous patient's variable (age in our case) is used to group brain voxels into regions; Secondly, a dynamic probabilistic Hidden Markov Model (HMM) is created to statistically analyze the relationship between spatial brain patterns and hidden states; Thirdly, parametric HMM models under a bagging framework are used to capture the changes occurring with time by decoding the hidden states longitudinally. We apply this method to datasets from elderly individuals, and test the effectiveness of this spatio-temporal model in analyzing the temporal dynamics of spatial aging patterns on an individual basis. Experimental results show this method could facilitate the early detection of pathological brain change.
Wang
Ying
Y
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, USA.
Resnick
Susan M
SM
Davatzikos
Christos
C
D000328:Adult; D000465:Algorithms; D001921:Brain / Q000473:pathology*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008297:Male; D008390:Markov Chains; D008959:Models, Neurological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012559:Schizophrenia / Q000175:diagnosis*; D012680:Sensitivity and Specificity