Standard clinical radiological techniques for determining lesion volume changes in interval exams are, as far as we
know, quantitatively non-descriptive or approximate at best. We investigate two new registration based methods
that help sketch an improved quantitative picture of lesion volume changes in hepatic interval CT exams. The
first method, Jacobian Integration, employs a constrained Thin Plate Spline warp to compute the deformation
of the lesion of interest over the intervals. The resulting jacobian map of the deformation is integrated to yield
the net lesion volume change. The technique is fast, accurate and requires no segmentation, but is sensitive
to misregistration. The second scheme uses a Weighted Gray Value Difference image of two registered interval
exams to estimate the change in lesion volume. A linear weighting and trimming curve is used to accurately
account for the contribution of partial voxels. This technique is insensitive to slight misregistration and useful
in analyzing simple lesions with uniform contrast or lesions with insufficient mutual information to allow the
computation of an accurate warp. The methods are tested on both synthetic and in vivo liver lesions and results
are evaluated against estimates obtained through careful manual segmentation of the lesions. Our findings so far
have given us reason to believe that the estimators are reliable. Further experiments on numerous in vivo lesions
will probably establish the improved efficacy of these methods in supporting earlier detection of new disease or
conversion from stable to progressive disease in comparison to existing clinical estimation techniques.
Sub-pixel compounding is a technique that synthesizes the information of an image sequence to form a betterresolved
and speckle reduced image. To avoid extra data acquisition time and patient exposure, reuse of the existing data
is highly desired. In elasticity imaging, a set of images with slight changes due to deformation is produced, which
provides an ideal input for the sub-pixel compounding process. In this paper, a brief review of the resolution
enhancement techniques in ultrasound imaging will be provided, and then, a diffusion-regularized, least square approach
is presented for sub-pixel compounding image reconstruction. Based on the results, we suggest that (1) B-mode images
from elastic imaging are suitable data for sub-pixel compounding and a speckle noise reduced higher-resolution image is
a co-product of elasticity imaging; (2) for breast diagnosis, resolution improvement is of strong interest since better
depiction of the interior and exterior structures of a tumor provides important detection and diagnostic information; (3) a
similar approach could be extended to elasticity imaging with other modalities.
Image registration is now a well understood problem and several techniques using a combination of cost functions,
transformation models and optimizers have been reported in medical imaging literature. Parametric methods
often rely on the efficient placement of control points in the images, that is, depending on the location and scale
at which images are mismatched. Poor choice of parameterization results in deformations not being modeled
accurately or over parameterization, where control points may lie in homogeneous regions with low sensitivity to
cost. This lowers computational efficiency due to the high complexity of the search space and might also provide
transformations that are not physically meaningful, and possibly folded.
Adaptive methods that parameterize based on mismatch in images have been proposed. In such methods, the
cost measure must be normalized, heuristics such as how many points to pick, resolution of the grids, choosing
gradient thresholds and when to refine scale would have to be ascertained in addition to the limitation of working
only at a few discrete scales.
In this paper we identify mismatch by searching the entire image and a wide range of smooth spatial scales.
The mismatch vector, containing location and scale of mismatch is computed from peaks in the local joint
entropy. Results show that this method can be used to quickly and effectively locate mismatched regions in
images where control points can be placed in preference to other regions speeding up registration.
Registration of medical images (intra- or multi-modality) is the first step before any analysis is performed.
The analysis includes treatment monitoring, diagnosis, volumetric measurements or classification to mention a
few. While pairwise registration, i.e., aligning a floating image to a fixed reference, is straightforward, it is not
immediately clear what cost measures could be exploited for the groupwise alignment of several images (possibly
multimodal) simultaneously. Recently however there has been increasing interest in this problem applied to atlas
construction, statistical shape modeling, or simply joint alignment of images to get a consistent correspondence
of voxels across all images based on a single cost measure.
The aim of this paper is twofold, a) propose a cost function - alpha mutual information computed using
entropic graphs that is a natural extension to Shannon mutual information for pairwise registration and b)
compare its performance with the pairwise registration of the image set. We show that this measure can be
reliably used to jointly align several images to a common reference. We also test its robustness by comparing
registration errors for the registration process repeated at varying noise levels.
In our experiments we used simulated data, applying different B-spline based geometric transformations to the
same image and adding independent filtered Gaussian noise to each image. Non-rigid registration was employed
with Thin Plate Splines(TPS) as the geometric interpolant.
Unmanned ground vehicle (UGV) technology can be used in a number of ways to assist in counter-terrorism activities. Robots can be employed for a host of terrorism deterrence and detection applications. As reported in last year's Aerosense conference, the U.S. Army Tank Automotive Research, Development and Engineering Center (TARDEC) and Utah State University (USU) have developed a
tele-operated robot called ODIS (Omnidirectional Inspection System) that is particularly effective in performing under-vehicle inspections at security checkpoints. ODIS' continuing development for this task is heavily influenced by feedback received from soldiers and civilian law enforcement personnel using ODIS-prototypes in an operational environment. Our goal is to convince civilian law enforcement and military police to replace the traditional "mirror on a stick" system of looking under cars for bombs and contraband with ODIS. This paper reports our efforts in the past one year in terms of optimizing ODIS for the visual inspection task. Of particular concern is the design of the vision system. This paper documents details on the various issues relating to ODIS' vision system - sensor, lighting, image processing, and display.