Title :
Subject-centered multi-view feature fusion for neuroimaging retrieval and classification
Author :
Sidong Liu;Weidong Cai;Siqi Liu;Sonia Pujol;Ron Kikinis;Dagan Feng
Author_Institution :
BMIT Research Group, School of IT, University of Sydney, Sydney, Australia
Abstract :
Multi-View neuroimaging retrieval and classification play an important role in computer-aided-diagnosis of brain disorders, as multi-view features could provide more insights of the disease pathology and potentially lead to more accurate diagnosis than single-view features. The large inter-feature and inter-subject variations make the multi-view neuroimaging analysis a challenging task. Many multi-view or multi-modal feature fusion methods have been proposed to reduce the impact of inter-feature variations in neuroimaging data. However, there is not much in-depth work focusing on the inter-subject variations. In this study, we propose a subject-centered multi-view feature fusion method for neuroimaging retrieval and classification based on the propagation graph fusion (PGF) algorithm. Two main advantages of the proposed method are: 1) it evaluates the query online and adaptively reshapes the connections between subjects according to the query; 2) it measures the affinity of the query to the subjects using the subject-centered affinity matrices, which can be easily combined and efficiently solved. Evaluated using a public accessible neuroimaging database, our algorithm outperforms the state-of-the-art methods in retrieval and achieves comparable performance in classification.
Keywords :
"Neuroimaging","Feature extraction","Support vector machines","Indexes","Alzheimer´s disease"
Conference_Titel :
Image Processing (ICIP), 2015 IEEE International Conference on
DOI :
10.1109/ICIP.2015.7351253