Evaluation of 3D gradient filters for estimation of the surface orientation in CTC

The extraction of the gradient information from 3D surfaces plays an important role for many applications including 3D graphics and medical imaging. The extraction of the 3D gradient information is performed by filtering the input data with high pass filters that are typically implemented using 3×3×3 masks. Since these filters extract the gradient information in small neighborhood, the estimated gradient information will be very sensitive to image noise. The development of a 3D gradient operator that is robust to image noise is particularly important since the medical datasets are characterized by a relatively low signal to noise ratio. The aim of this paper is to detail the implementation of an optimized 3D gradient operator that is applied to sample the local curvature of the colon wall in CT data and its influence on the overall performance of our CAD-CTC method. The developed 3D gradient operator has been applied to extract the local curvature of the colon wall in a large number CT datasets captured with different radiation doses and the experimental results are presented and discussed

Force-detected nuclear magnetic resonance: Recent advances and future challenges

We review recent efforts to detect small numbers of nuclear spins using magnetic resonance force microscopy. Magnetic resonance force microscopy (MRFM) is a scanning probe technique that relies on the mechanical measurement of the weak magnetic force between a microscopic magnet and the magnetic moments in a sample. Spurred by the recent progress in fabricating ultrasensitive force detectors, MRFM has rapidly improved its capability over the last decade. Today it boasts a spin sensitivity that surpasses conventional, inductive nuclear magnetic resonance detectors by about eight orders of magnitude. In this review we touch on the origins of this technique and focus on its recent application to nanoscale nuclear spin ensembles, in particular on the imaging of nanoscale objects with a three-dimensional (3D) spatial resolution better than 10 nm. We consider the experimental advances driving this work and highlight the underlying physical principles and limitations of the method. Finally, we discuss the challenges that must be met in order to advance the technique towards single nuclear spin sensitivity -- and perhaps -- to 3D microscopy of molecules with atomic resolution.Comment: 15 pages & 11 figure

Fuzzy-based Propagation of Prior Knowledge to Improve Large-Scale Image Analysis Pipelines

Many automatically analyzable scientific questions are well-posed and offer a variety of information about the expected outcome a priori. Although often being neglected, this prior knowledge can be systematically exploited to make automated analysis operations sensitive to a desired phenomenon or to evaluate extracted content with respect to this prior knowledge. For instance, the performance of processing operators can be greatly enhanced by a more focused detection strategy and the direct information about the ambiguity inherent in the extracted data. We present a new concept for the estimation and propagation of uncertainty involved in image analysis operators. This allows using simple processing operators that are suitable for analyzing large-scale 3D+t microscopy images without compromising the result quality. On the foundation of fuzzy set theory, we transform available prior knowledge into a mathematical representation and extensively use it enhance the result quality of various processing operators. All presented concepts are illustrated on a typical bioimage analysis pipeline comprised of seed point detection, segmentation, multiview fusion and tracking. Furthermore, the functionality of the proposed approach is validated on a comprehensive simulated 3D+t benchmark data set that mimics embryonic development and on large-scale light-sheet microscopy data of a zebrafish embryo. The general concept introduced in this contribution represents a new approach to efficiently exploit prior knowledge to improve the result quality of image analysis pipelines. Especially, the automated analysis of terabyte-scale microscopy data will benefit from sophisticated and efficient algorithms that enable a quantitative and fast readout. The generality of the concept, however, makes it also applicable to practically any other field with processing strategies that are arranged as linear pipelines.Comment: 39 pages, 12 figure

A Comparison of Weak Lensing Measurements From Ground- and Space-Based Facilities

We assess the relative merits of weak lensing surveys, using overlapping imaging data from the ground-based Subaru telescope and the Hubble Space Telescope (HST). Our tests complement similar studies undertaken with simulated data. From observations of 230,000 matched objects in the 2 square degree COSMOS field, we identify the limit at which faint galaxy shapes can be reliably measured from the ground. Our ground-based shear catalog achieves sub-percent calibration bias compared to high resolution space-based data, for galaxies brighter than i'~24.5 and with half-light radii larger than 1.8". This selection corresponds to a surface density of ~15 galaxies per sq arcmin compared to ~71 per sq arcmin from space. On the other hand the survey speed of current ground-based facilities is much faster than that of HST, although this gain is mitigated by the increased depth of space-based imaging desirable for tomographic (3D) analyses. As an independent experiment, we also reconstruct the projected mass distribution in the COSMOS field using both data sets, and compare the derived cluster catalogs with those from X-ray observations. The ground-based catalog achieves a reasonable degree of completeness, with minimal contamination and no detected bias, for massive clusters at redshifts 0.2<z<0.5. The space-based data provide improved precision and a greater sensitivity to clusters of lower mass or at higher redshift.Comment: 12 pages, 8 figures, submitted to ApJ, Higher resolution figures available at http://www.astro.caltech.edu/~mansi/GroundvsSpace.pd

Surface reconstruction of wear in carpets by using a wavelet edge detector

Carpet manufacturers have wear labels assigned to their products by human experts who evaluate carpet samples subjected to accelerated wear in a test device. There is considerable industrial and academic interest in going from human to automated evaluation, which should be less cumbersome and more objective. In this paper, we present image analysis research on videos of carpet surfaces scanned with a 3D laser. The purpose is obtaining good depth Images for an automated system that should have a high percentage of correct assessments for a wide variety of carpets. The innovation is the use of a wavelet edge detector to obtain a more continuously defined surface shape. The evaluation is based on how well the algorithms allow a good linear ranking and a good discriminance of consecutive wear labels. The results show an improved linear ranking for most carpet types, for two carpet types the results are quite significant