Title :
Brain lesion detection in MRI with fuzzy and geostatistical models
Author_Institution :
Sch. of Eng. & Inf. Technol., Univ. of New South Wales, Canberra, ACT, Australia
fDate :
Aug. 31 2010-Sept. 4 2010
Abstract :
Automated image detection of white matter changes of the brain is essentially helpful in providing a quantitative measure for studying the association of white matter lesions with other types of biomedical data. Such study allows the possibility of several medical hypothesis validations which lead to therapeutic treatment and prevention. This paper presents a new clustering-based segmentation approach for detecting white matter changes in magnetic resonance imaging with particular reference to cognitive decline in the elderly. The proposed method is formulated using the principles of fuzzy c-means algorithm and geostatistics.
Keywords :
biomedical MRI; brain; cognition; fuzzy set theory; geriatrics; image segmentation; medical image processing; statistical analysis; MRI; brain lesion detection; clustering-based segmentation; cognition; elderly; fuzzy c-means algorithm; geostatistical models; magnetic resonance imaging; white matter lesions; Biomedical imaging; Computed tomography; Image segmentation; Lesions; Magnetic resonance imaging; Manuals; Senior citizens; Algorithms; Artificial Intelligence; Brain; Brain Neoplasms; Computer Simulation; Data Interpretation, Statistical; Female; Fuzzy Logic; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Magnetic Resonance Imaging; Models, Biological; Models, Statistical; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity;
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE
Conference_Location :
Buenos Aires
Print_ISBN :
978-1-4244-4123-5
DOI :
10.1109/IEMBS.2010.5627188
