Proceedings Article | 27 February 2018
Proc. SPIE. 10575, Medical Imaging 2018: Computer-Aided Diagnosis
KEYWORDS: Magnetic resonance imaging, Image segmentation, Receivers, Control systems, Computed tomography, Neuroimaging, Alzheimer's disease, Brain mapping, Dementia, Brain
The rapid increase in the incidence of Alzheimer’s disease (AD) has become a critical issue in low and middle income
countries. In general, MR imaging has become sufficiently suitable in clinical situations, while CT scan might be
uncommonly used in the diagnosis of AD due to its low contrast between brain tissues. However, in those countries, CT
scan, which is less costly and readily available, will be desired to become useful for the diagnosis of AD. For CT scan,
the enlargement of the temporal horn of the lateral ventricle (THLV) is one of few findings for the diagnosis of AD. In
this paper, we present an automated volumetry of THLV with segmentation based on Bayes’ rule on CT images. In our
method, first, all CT data sets are normalized into an atlas by using linear affine transformation and non-linear wrapping
techniques. Next, a probability map of THLV is constructed in the normalized data. Then, THLV regions are extracted
based on Bayes’ rule. Finally, the volume of the THLV is evaluated. This scheme was applied to CT scans from 20 AD
patients and 20 controls to evaluate the performance of the method for detecting AD. The estimated THLV volume was
markedly increased in the AD group compared with the controls (P < .0001), and the area under the receiver operating
characteristic curve (AUC) was 0.921. Therefore, this computerized method may have the potential to accurately detect
AD on CT images.
