Longitudinal Evaluation of Structural Changes in Frontotemporal Dementia Using Artificial Neural Networks

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dc.contributor.authorKumari, RS
dc.contributor.authorVarghese, T
dc.contributor.authorKesavadas, C
dc.contributor.authorSingh, NA
dc.contributor.authorMathuranath, PS
dc.date.accessioned2017-03-10T03:27:29Z
dc.date.available2017-03-10T03:27:29Z
dc.date.issued2014
dc.description.abstractAutomatic Segmentation of Magnetic Resonance (MR) Images plays an important role in medical image processing. Segmentation is the process of extracting the brain tissue components such as grey matter (GM), white matter (WM) and cerebrospinal fluids (CSF). The volumetric analysis of the segmented tissues helps in determining the amount of GM loss in specific disease pathology. Among the various segmentation techniques, fuzzy c means (FCM) is the most widely used one. The performance of traditional FCM is considerably reduces in noisy MR images. However, in the clinical analysis accurate segmentation of MR image is very important and crucial for the early diagnosis and prognosis. This paper put forward an Artificial Neural Network based segmentation to map the longitudinal structural changes overtime in Frontotemporal dementia (FTD) subjects that could be a better cue to impending behavioural changes. Our proposed approach has achieved an average classification accuracy of 96.7%, 96.4% and 97.96% for GM, WM and CSF respectively
dc.identifier.citation247 ,;165-172en_US
dc.identifier.uri10.1007/978-3-319-02931-3_20
dc.identifier.urihttps://dspace.sctimst.ac.in/handle/123456789/10011
dc.publisherPROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON FRONTIERS OF INTELLIGENT COMPUTING: THEORY AND APPLICATIONS (FICTA) 2013
dc.subjectComputer Science
dc.titleLongitudinal Evaluation of Structural Changes in Frontotemporal Dementia Using Artificial Neural Networks
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