In this paper, we propose and validate a fully automated pipeline for simultaneous skull-stripping and lateral ventricle segmentation using T1-weighted images. The pipeline is built upon a segmentation algorithm entitled fast multi-atlas likelihood-fusion (MALF) which utilizes multiple T1 atlases that have been pre-segmented into six whole-brain labels – the gray matter, the white matter, the cerebrospinal fluid, the lateral ventricles, the skull, and the background of the entire image. This algorithm, MALF, was designed for estimating brain anatomical structures in the framework of coordinate changes via large diffeomorphisms. In the proposed pipeline, we use a variant of MALF to estimate those six whole-brain labels in the test T1-weighted image. The three tissue labels (gray matter, white matter, and cerebrospinal fluid) and the lateral ventricles are then grouped together to form a binary brain mask to which we apply morphological smoothing so as to create the final mask for brain extraction. For computational purposes, all input images to MALF are down-sampled by a factor of two. In addition, small deformations are used for the changes of coordinates. This substantially reduces the computational complexity, hence we use the term “fast MALF”. The skull-stripping performance is qualitatively evaluated on a total of 486 brain scans from a longitudinal study on Alzheimer dementia. Quantitative error analysis is carried out on 36 scans for evaluating the accuracy of the pipeline in segmenting the lateral ventricle. The volumes of the automated lateral ventricle segmentations, obtained from the proposed pipeline, are compared across three different clinical groups. The ventricle volumes from our pipeline are found to be sensitive to the diagnosis.
We create a series of detailed computerized phantoms to estimate patient organ and effective dose in pediatric CT and
investigate techniques for efficiently creating patient-specific phantoms based on imaging data. The initial anatomy of
each phantom was previously developed based on manual segmentation of pediatric CT data. Each phantom was
extended to include a more detailed anatomy based on morphing an existing adult phantom in our laboratory to match
the framework (based on segmentation) defined for the target pediatric model. By morphing a template anatomy to
match the patient data in the LDDMM framework, it was possible to create a patient specific phantom with many
anatomical structures, some not visible in the CT data. The adult models contain thousands of defined structures that
were transformed to define them in each pediatric anatomy. The accuracy of this method, under different conditions, was
tested using a known voxelized phantom as the target. Errors were measured in terms of a distance map between the
predicted organ surfaces and the known ones. We also compared calculated dose measurements to see the effect of
different magnitudes of errors in morphing. Despite some variations in organ geometry, dose measurements from
morphing predictions were found to agree with those calculated from the voxelized phantom thus demonstrating the
feasibility of our methods.
In this study, the asymptotic performance analysis for target
detection-identification through Bayesian hypothesis testing
in infrared images is presented. In the problem, probabilistic
representations in terms of Bayesian pattern-theoretic framework
is used. The infrared clutter is modelled as a second-order
random field. The targets are represented as rigid CAD models.
Their infinite variety of pose is modelled as transformations on
the templates. For the template matching in hypothesis testing,
a metric distance, based on empirical covariance, is used. The asymptotic performance of ATR algorithm under this metric and Euclidian metric is compared. The receiver operating characteristic (ROC) curves indicate that using the empirical covariance metric improves the performance significantly. These curves are also compared with the curves based on analytical expressions. The analytical results predict the experimental results quite well.