3D medical volume segmentation using hybrid multiresolution statistical approaches

This article is available through the Brunel Open Access Publishing Fund. Copyright © 2010 S AlZu’bi and A Amira.3D volume segmentation is the process of partitioning voxels into 3D regions (subvolumes) that represent meaningful physical entities which are more meaningful and easier to analyze and usable in future applications. Multiresolution Analysis (MRA) enables the preservation of an image according to certain levels of resolution or blurring. Because of multiresolution quality, wavelets have been deployed in image compression, denoising, and classification. This paper focuses on the implementation of efficient medical volume segmentation techniques. Multiresolution analysis including 3D wavelet and ridgelet has been used for feature extraction which can be modeled using Hidden Markov Models (HMMs) to segment the volume slices. A comparison study has been carried out to evaluate 2D and 3D techniques which reveals that 3D methodologies can accurately detect the Region Of Interest (ROI). Automatic segmentation has been achieved using HMMs where the ROI is detected accurately but suffers a long computation time for its calculations

A practical multirobot localization system

We present a fast and precise vision-based software intended for multiple robot localization. The core component of the software is a novel and efficient algorithm for black and white pattern detection. The method is robust to variable lighting conditions, achieves sub-pixel precision and its computational complexity is independent of the processed image size. With off-the-shelf computational equipment and low-cost cameras, the core algorithm is able to process hundreds of images per second while tracking hundreds of objects with a millimeter precision. In addition, we present the method's mathematical model, which allows to estimate the expected localization precision, area of coverage, and processing speed from the camera's intrinsic parameters and hardware's processing capacity. The correctness of the presented model and performance of the algorithm in real-world conditions is verified in several experiments. Apart from the method description, we also make its source code public at \emph{http://purl.org/robotics/whycon}; so, it can be used as an enabling technology for various mobile robotic problems

Identification of coronal heating events in 3D simulations

The solar coronal heating problem is an open question since 1939. One proposed model for the transport and release of mechanical energy generated in the sub-phorospheric layers and photosphere is the nanoflare model that incorporates Ohmic heating which releases a part of the energy stored in the magnetic field via magnetic reconnection. The problem with the verification of this model is that we cannot resolve observationally small scale events. Histograms of observable characteristics of flares, show powerlaw behavior, for both energy release rate, size and total energy. Depending on the powerlaw index of the energy release, nanoflares might be an important candidate for coronal heating; we seek to find that index. In this paper, we employ a numerical 3D-MHD simulation produced by the numerical code Bifrost, and a new technique to identify the 3D heating events at a specific instant. The quantity we explore is the Joule heating, which is explicitly correlated with the magnetic reconnection because depends on the curl of the magnetic field. We are able to identify 4136 events in a volume $24 \times 24 \times 9.5 \ \textrm{Mm}^3$(i.e.$768 \times 786 \times 331$ grid cells) of a specific snapshot. We find a powerlaw slope of the released energy per second, and two powerlaw slopes of the identified volume. The identified energy events do not represent all the released energy, but of the identified events, the total energy of the largest events dominate the energy release. Most of the energy release happens in the lower corona, while heating drops with height. We find that with a specific identification method that large events can be resolved into smaller ones, but at the expense of the total identified energy releases. The energy release which cannot be identified as an event favours a low energy release mechanism.Comment: 10 pages, 7 figure

Estimation of Pulmonary Arterial Volume Changes in the Normal and Hypertensive Fawn-Hooded Rat from 3D Micro-CT data

In the study of pulmonary vascular remodeling, much can be learned from observing the morphological changes undergone in the pulmonary arteries of the rat lung when exposed to chronic hypoxia or other challenges which elicit a remodeling response. Remodeling effects include thickening of vessel walls, and loss of wall compliance. Morphometric data can be used to localize the hemodynamic and functional consequences. We developed a CT imaging method for measuring the pulmonary arterial tree over a range of pressures in rat lungs. X-ray micro-focal isotropic volumetric imaging of the arterial tree in the intact rat lung provides detailed information on the size, shape and mechanical properties of the arterial network. In this study, we investigate the changes in arterial volume with step changes in pressure for both normoxic and hypoxic Fawn-Hooded (FH) rats. We show that FH rats exposed to hypoxia tend to have reduced arterial volume changes for the same preload when compared to FH controls. A secondary objective of this work is to quantify various phenotypes to better understand the genetic contribution of vascular remodeling in the lungs. This volume estimation method shows promise in high throughput phenotyping, distinguishing differences in the pulmonary hypertensive rat model

A Multiscale Approach for Statistical Characterization of Functional Images

Increasingly, scientific studies yield functional image data, in which the observed data consist of sets of curves recorded on the pixels of the image. Examples include temporal brain response intensities measured by fMRI and NMR frequency spectra measured at each pixel. This article presents a new methodology for improving the characterization of pixels in functional imaging, formulated as a spatial curve clustering problem. Our method operates on curves as a unit. It is nonparametric and involves multiple stages: (i) wavelet thresholding, aggregation, and Neyman truncation to effectively reduce dimensionality; (ii) clustering based on an extended EM algorithm; and (iii) multiscale penalized dyadic partitioning to create a spatial segmentation. We motivate the different stages with theoretical considerations and arguments, and illustrate the overall procedure on simulated and real datasets. Our method appears to offer substantial improvements over monoscale pixel-wise methods. An Appendix which gives some theoretical justifications of the methodology, computer code, documentation and dataset are available in the online supplements

Video-rate or high-precision: A flexible range imaging camera

A range imaging camera produces an output similar to a digital photograph, but every pixel in the image contains distance information as well as intensity. This is useful for measuring the shape, size and location of objects in a scene, hence is well suited to certain machine vision applications. Previously we demonstrated a heterodyne range imaging system operating in a relatively high resolution (512-by-512) pixels and high precision (0.4 mm best case) configuration, but with a slow measurement rate (one every 10 s). Although this high precision range imaging is useful for some applications, the low acquisition speed is limiting in many situations. The system’s frame rate and length of acquisition is fully configurable in software, which means the measurement rate can be increased by compromising precision and image resolution. In this paper we demonstrate the flexibility of our range imaging system by showing examples of high precision ranging at slow acquisition speeds and video-rate ranging with reduced ranging precision and image resolution. We also show that the heterodyne approach and the use of more than four samples per beat cycle provides better linearity than the traditional homodyne quadrature detection approach. Finally, we comment on practical issues of frame rate and beat signal frequency selection

Cortical spatio-temporal dimensionality reduction for visual grouping

The visual systems of many mammals, including humans, is able to integrate the geometric information of visual stimuli and to perform cognitive tasks already at the first stages of the cortical processing. This is thought to be the result of a combination of mechanisms, which include feature extraction at single cell level and geometric processing by means of cells connectivity. We present a geometric model of such connectivities in the space of detected features associated to spatio-temporal visual stimuli, and show how they can be used to obtain low-level object segmentation. The main idea is that of defining a spectral clustering procedure with anisotropic affinities over datasets consisting of embeddings of the visual stimuli into higher dimensional spaces. Neural plausibility of the proposed arguments will be discussed