DocumentCode
3107969
Title
Discovering Regional Pathological Patterns in Brain MRI
Author
Pulido, Andrea ; Rueda, Andrea ; Romero, Eduardo ; Malpica, Norberto
Author_Institution
Comput. Imaging & Med. Applic. Lab., Univ. Nac. de Colombia Bogota, Bogota, Colombia
fYear
2013
fDate
22-24 June 2013
Firstpage
152
Lastpage
156
Abstract
Complex pathological brain patterns generally are found in neurodegenerative diseases which can be correlated with different clinical onsets of a particular pathology. Currently, an objective method that aids to determine such signs, in terms of global and local changes, is not available in clinical practice and the whole interpretation is dependent on the radiologist´s skills. In this paper, we propose a fully automatic method that analyzes the brain structure under a multidimensional frame and highlights relevant brain patterns. An association of such patterns with the disease is herein evaluated in three classification tasks, involving probable Alzheimer´s disease (AD) patients, Mild Cognitive Impairment (MCI) patients and normal subjects (NC). A set of 75 brain MR images from NC subjects (25), MCI (25) and probable AD (25) patients, split into training (15 subjects) and testing (60 subjects) sets, was used to evaluate the performance of the proposed approach. Preliminary results show that the proposed method reaches a maximum classification accuracy of 80% when discriminating AD patients from NC, of 75% for classification of MCI patients from NC.
Keywords
biomedical MRI; brain; diseases; image classification; medical disorders; medical image processing; neurophysiology; probability; AD patients; Alzheimer´s disease patients; MCI patients; NC; brain MR images; brain MRI; brain structure analysis; diseases; maximum classification accuracy; mild-cognitive impairment patients; multidimensional frame; neurodegenerative diseases; normal subjects; pathology; regional pathological brain pattern discovery; testing sets; training sets; Diseases; Feature extraction; Kernel; Pathology; Probabilistic logic; Support vector machines; Visualization; Alzheimer´s disease; MRI; Visual Attention Models; probabilistic Latent Semantic Analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition in Neuroimaging (PRNI), 2013 International Workshop on
Conference_Location
Philadelphia, PA
Type
conf
DOI
10.1109/PRNI.2013.47
Filename
6603579
Link To Document