Segmentation of brain structures in magnetic resonance images is an important task in neuro image analysis. Several
papers on this topic have shown the benefit of supervised classification based on local appearance features, often combined with atlas-based approaches. These methods require a representative annotated training set and therefore often do not perform well if the target image is acquired on a different scanner or with a different acquisition protocol than the training images. Assuming that the appearance of the brain is determined by the underlying brain tissue distribution and that brain tissue classification can be performed robustly for images obtained with different protocols, we propose to derive appearance features from brain-tissue density maps instead of directly from the MR images. We evaluated this approach on hippocampus segmentation in two sets of images acquired with substantially different imaging protocols and on different scanners. While a combination of conventional appearance features trained on data from a different scanner with multi-atlas segmentation performed poorly with an average Dice overlap of 0.698, the local appearance model based on the new acquisition-independent features significantly improved (0.783) over atlas-based segmentation alone (0.728).
It is still unclear whether periventricular and subcortical white matter lesions (WMLs) differ in etiology or clinical
consequences. Studies addressing this issue would benefit from automated segmentation and localization
of WMLs. Several papers have been published on WML segmentation in MR images. Automated localization
however, has not been investigated as much. This work presents and evaluates a novel method to label segmented
WMLs as periventricular and subcortical.
The proposed technique combines tissue classification and registration-based segmentation to outline the ventricles
in MRI brain data. The segmented lesions can then be labeled into periventricular WMLs and subcortical
WMLs by applying region growing and morphological operations.
The technique was tested on scans of 20 elderly subjects in which neuro-anatomy experts manually segmented
WMLs. Localization accuracy was evaluated by comparing the results of the automated method with a manual
localization. Similarity indices and volumetric intraclass correlations between the automated and the manual
localization were 0.89 and 0.95 for periventricular WMLs and 0.64 and 0.89 for subcortical WMLs, respectively.
We conclude that this automated method for WML localization performs well to excellent in comparison to the
Proc. SPIE. 5089, Detection and Remediation Technologies for Mines and Minelike Targets VIII
KEYWORDS: Radar, Finite-difference time-domain method, Detection and tracking algorithms, Metals, Dielectrics, Deconvolution, Algorithm development, Ground penetrating radar, Land mines, General packet radio service
This paper presents a novel deconvolution algorithm designed to estimate the impulse response of buried objects based on ground penetrating radar (GPR) signals. The impulse response is a rich source of information about the buried object and therefore very useful for intelligent signal processing of GPR data. For example, it can be used in a target classification scheme to reduce the false alarm rate in demining operations. Estimating the target impulse response from the incident and scattered radar signals is a basic deconvolution problem. However, noise sensitivity and ground dispersion prevent the use of simple deconvolution methods like linear least squares deconvolution. Instead, a new deconvolution algorithm has been developed that computes estimates adhering to a physical impulse response model and that can be characterized by a limited number of parameters. It is shown that the new algorithm is robust with respect to noise and that it can deal with ground dispersion. The general performance of the algorithm has been tested on data generated by finite-difference time-domain (FDTD) simulations. The results demonstrate that the algorithm can distinguish between different dielectric and metal targets, making it very suitable for use in a classification scheme. Moreover, since the estimated impulse responses have physical meaning they can be related to target characteristics such as size and material properties. A direct application of this is the estimation of the permittivity of a dielectric target from its impulse response and that of a calibration target.