Synthetic 3D Pap smear nucleus generation

Gómez Aguilar, S. (2010). Synthetic 3D Pap smear nucleus generation. http://hdl.handle.net/10251/10215.Archivo delegad

From 3D Point Clouds to Pose-Normalised Depth Maps

We consider the problem of generating either pairwise-aligned or pose-normalised depth maps from noisy 3D point clouds in a relatively unrestricted poses. Our system is deployed in a 3D face alignment application and consists of the following four stages: (i) data filtering, (ii) nose tip identification and sub-vertex localisation, (iii) computation of the (relative) face orientation, (iv) generation of either a pose aligned or a pose normalised depth map. We generate an implicit radial basis function (RBF) model of the facial surface and this is employed within all four stages of the process. For example, in stage (ii), construction of novel invariant features is based on sampling this RBF over a set of concentric spheres to give a spherically-sampled RBF (SSR) shape histogram. In stage (iii), a second novel descriptor, called an isoradius contour curvature signal, is defined, which allows rotational alignment to be determined using a simple process of 1D correlation. We test our system on both the University of York (UoY) 3D face dataset and the Face Recognition Grand Challenge (FRGC) 3D data. For the more challenging UoY data, our SSR descriptors significantly outperform three variants of spin images, successfully identifying nose vertices at a rate of 99.6%. Nose localisation performance on the higher quality FRGC data, which has only small pose variations, is 99.9%. Our best system successfully normalises the pose of 3D faces at rates of 99.1% (UoY data) and 99.6% (FRGC data)

A Comparative Study of Automatic Localization Algorithms for Spherical Markers within 3D MRI Data

Localization of features and structures in images is an important task in medical image-processing. Characteristic structures and features are used in diagnostics and surgery planning for spatial adjustments of the volumetric data, including image registration or localization of bone-anchors and fiducials. Since this task is highly recurrent, a fast, reliable and automated approach without human interaction and parameter adjustment is of high interest. In this paper we propose and compare four image processing pipelines, including algorithms for automatic detection and localization of spherical features within 3D MRI data. We developed a convolution based method as well as algorithms based on connected-components labeling and analysis and the circular Hough-transform. A blob detection related approach, analyzing the Hessian determinant, was examined. Furthermore, we introduce a novel spherical MRI-marker design. In combination with the proposed algorithms and pipelines, this allows the detection and spatial localization, including the direction, of fiducials and bone-anchors

Shells and Spheres: A Novel Framework for Variable Scale Statistical Image Analysis

A framework for analyzing images, called emph{Shells and Spheres},has been developed based on a set of spheres with adjustable radii,with exactly one sphere centered at each image pixel. This set ofspheres, known as a emph{sphere map}, is considered optimized wheneach sphere reaches, but does not cross, the nearest boundary.Calculations denoted as emph{Variable-Scale Statistics} (VSS) areperformed on populations of pixels within spheres, as well aspopulations of adjacent and overlapping spheres, in order to deducethe proper radius of each sphere. Spheres grow or shrink by addingor deleting an outer shell one pixel thick . Unlike conventionalfixed-scale kernels, our spherical operators consider as many pixelsas possible to differentiate between objects and accuratelydelineate boundaries. The term ``sphere" is used for brevity, thoughthe approach is not limited to 3D and is valid in $n$-dimensions.The approach is illustrated using both real images and noiselesssynthetic images containing objects with uniform intensity, and moreclosely examined and validated using various synthetic images withadded white noise and multiple contrast enhanced CT scans of theaortic arch. A particular algorithm using Shells and Spheres isdescribed and demonstrated on segmentation of the aortic arch in acontrast-enhanced CT scan, both in 2D and 3D

Image reconstruction and correction methods in neutron and X-ray tomography

Neutron and X-ray tomography are imaging techniques for getting information about the interior of objects in a non-destructive way. They reconstruct cross-sections from projection images of the object being investigated. Due to the properties of the image acquisition system, the projection images are distorted by several artifacts, and these reduce the quality of the reconstruction. In order to eliminate these harmful effects the projection images should be corrected before reconstruction. Taking projections is usually an expensive and time consuming procedure. One of our main goals has been to try to minimize the number of projections - for example, by exploiting more a priori information. A possible way of reducing the number of projections is by the application of discrete tomographic methods. In this case a special class of objects can be reconstructed, consisting of only a few homogenous materials that can be characterized by known discrete absorption values. To this end we have implemented two reconstruction methods. One is able to reconstruct objects consisting of cylinders and spheres made of homogeneous materials only. The other method is a general one in the sense that it can be used for reconstructing any shape. Simulations on phantoms and physical measurements were carried out and the results are presented here

Quantification of diesel injector dribble using 3D reconstruction from x-ray and DBI imaging

[EN] Post-injection dribble is known to lead to incomplete atomisation and combustion due to the release of slow moving, and often surface-bound, liquid fuel after the end of the injection event. This can have a negative effect on engine emissions, performance, and injector durability. To better quantify this phenomenon we present a new image processing approach to quantify the volume and surface area of ligaments produced during the end of injection, for an ECN ‘Spray B’ 3-hole injector. Circular approximation for cross-sections was used to estimate three-dimensional parameters of droplets and ligaments. The image processing consisted in three stages: edge detection, morphological reconstruction, and 3D reconstruction. For the last stage of 3D reconstruction, smooth surfaces were obtained by computation of the alpha shape which represents a bounding volume enveloping a set of 3D points. The object model was verified by calculation of surface area and volume from 2D images of figures with well-known shapes. We show that the object model fits non-spherical droplets and pseudo-cylindrical ligaments reasonably well. We applied our processing approach to datasets generated by different research groups to decouple the effect of gas temperature and pressure on the fuel dribble process. High-speed X-ray phase-contrast images obtained at room temperature conditions (297 K) at the 7-ID beamline of the Advanced Photon Source at Argonne National Laboratory, together with diffused back-illumination (DBI) images captured at a wide range of temperature conditions (293-900 K) by CMT Motores Térmicos, were analysed and compared quantitatively.This work was supported by the UK’s Engineering and Physical Science Research Council [grants EP/K020528/1 and EP/M009424/1], and BP Formulated Products Technology. The authors acknowledge the support of this work from CMT Motores Térmicos (Universitat Politècnica de València, Spain). Parts of this research were performed at the 7-ID beam line of the Advanced Photon Source at Argonne National Laboratory. Use of the APS is supported by the U.S. Department of Energy (DOE) under Contract No. DEAC02-06CH11357. This research was partially funded by DOE's Vehicle Technologies Program, Office of Energy Efficiency and Renewable Energy. The authors would like to thank Team Leaders Gurpreet Singh and Leo Breton for their support of this workSechenyh, V.; Turner, J.; Sykes, D.; Duke, D.; Swantek, A.; Matusik, K.; Kastengren, A.... (2017). Quantification of diesel injector dribble using 3D reconstruction from x-ray and DBI imaging. En Ilass Europe. 28th european conference on Liquid Atomization and Spray Systems. Editorial Universitat Politècnica de València. 216-223. https://doi.org/10.4995/ILASS2017.2017.4742OCS21622

Effect of fluid distribution on compressional wave propagation in partially saturated rocks

Partial saturation of porous rock by two fluids substantially affects compressional wave propagation. In particular, partial saturation causes significant attenuation and dispersion due to wave-induced fluid flow. Such flow arises when a passing wave induces different fluid pressures in regions of rock saturated by different fluids. When partial saturation is mesoscopic, i.e. existing on a length scale much greater than pore scale but less than wavelength scale, significant attenuation can arise for frequencies 10-1000 Hz. Models for attenuation and dispersion due to mesoscale heterogeneities mostly assume fluids are distributed in a regular way. Recent experiments indicate mesoscopic heterogeneities have less idealised distributions and distribution affects attenuation/dispersion. Thus, theoretical models are required to simulate effects due to realistic fluid distributions.The thesis focus is to model attenuation and dispersion due to realistic mesoscopic fluid distributions and fluid contrasts. First X-ray tomographic images of partially saturated rock are analysed statistically to identify spatial measures useful for describing fluid distribution patterns. The correlation function and associated correlation length for a specific fluid type are shown to be of greatest utility. Next a new model, called 3DCRM (CRM stands for continuous random media) is derived, utilizing a correlation function to describe the fluid distribution pattern. It is a random media model, is accurate for small fluid contrast and approximate for large fluid contrast. Using 3DCRM attenuation and dispersion are shown to depend on fluid distribution.Next a general framework for partial saturation called APS (acoustics of partial saturation) is extended enabling estimation of attenuation and dispersion due to arbitrary 1D/3D fluid distributions. The intent is to construct a versatile model enabling attenuation and dispersion to be estimated for arbitrary fluid distributions, contrasts and saturations. Two crucial parameters within APS called shape and frequency scaling parameters are modified via asymptotic analysis using several random media models (which are accurate for only certain contrasts in fluid bulk moduli and percent saturation). For valid fluid contrasts and saturations, which satisfy certain random media conditions there is good correspondence between modified APS and the random media models, hence showing that APS can be utilized to model attenuation and dispersion due to more realistic fluid distributions.Finally I devise a numerical method to test the accuracy of the analytical shape parameters for a range of fluid distributions, saturations and contrasts. In particular, the analytical shape parameter for randomly distributed spheres was shown to be accurate for a large range of saturations and fluid contrasts