In this paper, we consider the problem of detecting the presence of footsteps using signal measurements from
a network of seismic sensors. Since the sensors are closely spaced, they result in correlated measurements. A
novel method for detection that exploits the spatial dependence of sensor measurements using copula functions is
proposed. An approach for selecting the copula function that is most suited for modeling the spatial dependence
of sensor observations is also provided. The performance of the proposed approach is illustrated using real
footstep signals collected using an experimental test-bed consisting of seismic sensors.
KEYWORDS: Sensors, Signal processing, Signal detection, Surveillance, Data analysis, Analytical research, Seismic sensors, Signal analyzers, Wavelets, Transform theory
This paper describes experiments and analysis of seismic signals in addressing the problem of personnel detection
for indoor surveillance. Data was collected using geophones to detect footsteps from walking and running in
indoor environments such as hallways. Our analysis of the data shows the significant presence of nonlinearity,
when tested using the surrogate data method. This necessitates the need for novel detector designs that are not
based on linearity assumptions. We present one such method based on empirical mode decomposition (EMD)
and functional data analysis (FDA) and evaluate its applicability on our collected dataset.
Abnormalities of the number and location of cells are hallmarks of both developmental and degenerative neurological diseases. However, standard stereological methods are impractical for assigning each cell's nucleus position within a large volume of brain tissue. We propose an automated approach for segmentation and localization of the brain cell nuclei in laser scanning microscopy (LSM) embryonic mouse brain images. The nuclei in these images are first segmented by using the level set (LS) and watershed methods in each optical plane. The segmentation results are further refined by application of information from adjacent optical planes and prior knowledge of nuclear shape. Segmentation is then followed with an algorithm for 3D localization of the centroid of nucleus (CN). Each volume of tissue is thus represented by a collection of centroids leading to an approximate 10,000-fold reduction in the data set size, as compared to the original image series. Our method has been tested on LSM images obtained from an embryonic mouse brain, and compared to the segmentation and CN localization performed by an expert. The average Euclidian distance between locations of CNs obtained using our method and those obtained by an expert is 1.58±1.24 µm, a value well within the ~5 µm average radius of each nucleus. We conclude that our approach accurately segments and localizes CNs within cell dense embryonic tissue.
In biomedical research, there is an increased need for reconstruction of large soft tissue volumes (e.g. whole organs) at
the microscopic scale from images obtained using laser scanning microscopy (LSM) with fluorescent dyes targeting
selected cellular features. However, LSM allows reconstruction of volumes not exceeding a few hundred ìm in size and
most LSM procedures require physical sectioning of soft tissue resulting in tissue deformation. Micro-CT (&mgr;CT) can
provide deformation free tomographic image of the whole tissue volume before sectioning. Even though, the spatial
resolution of &mgr;CT is around 5 &mgr;m and its contrast resolution is poor, it could provide information on external and
internal interfaces of the investigated volume and therefore could be used as a template in the volume reconstruction
from a very large number of LSM images. Here we present a method for accurate 3D reconstruction of the murine heart
from large number of images obtained using confocal LSM. The volume is reconstructed in the following steps: (i)
Montage synthesis of individual LSM images to form a set of aligned optical planes within given physical section; (ii)
Image enhancement and segmentation to correct for non-uniform illumination and noise; (iii) Volume matching of a
synthesized physical section to a corresponding sub-volume of &mgr;CT; (iv) Affine registration of the physical section to
the selected &mgr;CT sub-volume. We observe correct gross alignment of the physical sections. However, many sections
still exhibit local misalignment that could be only corrected via local nonrigid registration to &mgr;CT template and we plan
to do it in the future.
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