A non-invasive technique for burn area measurement

The need for a reliable and accurate method for assessing the surface area of burn wounds currently exists in the branch of medicine involved with burn care and treatment. The percentage of the surface area is of critical importance in evaluating fluid replacement amounts and nutritional support during the 24 hours of postburn therapy. A noninvasive technique has been developed which facilitates the measurement of burn area. The method we shall describe is an inexpensive technique to measure the burn areas accurately. Our imaging system is based on a technique known as structured light. Most structured light computer imaging systems, including ours, use triangulation to determine the location of points in three dimensions as the intersection of two lines: a ray of light originating from the structured light projector and the line of sight determined by the location of the image point in the camera plane. The geometry used to determine 3D location by triangulation is identical to the geometry of other stereo-based vision system, including the human vision system. Our system projects a square grid pattern from 35mm slide onto the patient. The grid on the slide is composed of uniformly spaced orthogonal stripes which may be indexed by row and column. Each slide also has square markers placed in between time lines of the grid in both the horizontal and vertical directions in the center of the slide. Our system locates intersections of the projected grid stripes in the camera image and determines the 3D location of the corresponding points on the body by triangulation. Four steps are necessary in order to reconstruct 3D locations of points on the surface of the skin: camera and projector calibration; image processing to locate the grid intersections in the camera image; grid labeling to establish the correspondence between projected and imaged intersections; and triangulation to determine three-dimensional position. Three steps are required to segment burned portion in image: edge detection to get the strongest edges of the region; edge following to form a closed boundary; and region filling to identify the burn region. After combining the reconstructed 3D locations and segmented image, numerical analysis and geometric modeling techniques are used to calculate the burn area. We use cubic spline interpolation, bicubic surface patches and Gaussian quadrature double integration to calculate the burn wound area. The accuracy of this technique is demonstrated The benefits and advantages of this technique are, first, that we don’t have to make any assumptions about the shape of the human body and second, there is no need for either the Rule-of-Nines, or the weight and height of the patient. This technique can be used for human body shape, regardless of weight proportion, size, sex or skin pigmentation. The low cost, intuitive method, and demonstrated efficiency of this computer imaging technique makes it a desirable alternative to current methods and provides the burn care specialist with a sterile, safe, and effective diagnostic tool in assessing and investigating burn areas

Space-Varying Coefficient Models for Brain Imaging

The methodological development and the application in this paper originate from diffusion tensor imaging (DTI), a powerful nuclear magnetic resonance technique enabling diagnosis and monitoring of several diseases as well as reconstruction of neural pathways. We reformulate the current analysis framework of separate voxelwise regressions as a 3d space-varying coefficient model (VCM) for the entire set of DTI images recorded on a 3d grid of voxels. Hence by allowing to borrow strength from spatially adjacent voxels, to smooth noisy observations, and to estimate diffusion tensors at any location within the brain, the three-step cascade of standard data processing is overcome simultaneously. We conceptualize two VCM variants based on B-spline basis functions: a full tensor product approach and a sequential approximation, rendering the VCM numerically and computationally feasible even for the huge dimension of the joint model in a realistic setup. A simulation study shows that both approaches outperform the standard method of voxelwise regressions with subsequent regularization. Due to major efficacy, we apply the sequential method to a clinical DTI data set and demonstrate the inherent ability of increasing the rigid grid resolution by evaluating the incorporated basis functions at intermediate points. In conclusion, the suggested fitting methods clearly improve the current state-of-the-art, but ameloriation of local adaptivity remains desirable

Extracting 3D parametric curves from 2D images of Helical objects

Helical objects occur in medicine, biology, cosmetics, nanotechnology, and engineering. Extracting a 3D parametric curve from a 2D image of a helical object has many practical applications, in particular being able to extract metrics such as tortuosity, frequency, and pitch. We present a method that is able to straighten the image object and derive a robust 3D helical curve from peaks in the object boundary. The algorithm has a small number of stable parameters that require little tuning, and the curve is validated against both synthetic and real-world data. The results show that the extracted 3D curve comes within close Hausdorff distance to the ground truth, and has near identical tortuosity for helical objects with a circular profile. Parameter insensitivity and robustness against high levels of image noise are demonstrated thoroughly and quantitatively

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

The Panchromatic High-Resolution Spectroscopic Survey of Local Group Star Clusters - I. General Data Reduction Procedures for the VLT/X-shooter UVB and VIS arm

Our dataset contains spectroscopic observations of 29 globular clusters in the Magellanic Clouds and the Milky Way performed with VLT/X-shooter. Here we present detailed data reduction procedures for the VLT/X-shooter UVB and VIS arm. These are not restricted to our particular dataset, but are generally applicable to different kinds of X-shooter data without major limitation on the astronomical object of interest. ESO's X-shooter pipeline (v1.5.0) performs well and reliably for the wavelength calibration and the associated rectification procedure, yet we find several weaknesses in the reduction cascade that are addressed with additional calibration steps, such as bad pixel interpolation, flat fielding, and slit illumination corrections. Furthermore, the instrumental PSF is analytically modeled and used to reconstruct flux losses at slit transit and for optimally extracting point sources. Regular observations of spectrophotometric standard stars allow us to detect instrumental variability, which needs to be understood if a reliable absolute flux calibration is desired. A cascade of additional custom calibration steps is presented that allows for an absolute flux calibration uncertainty of less than ten percent under virtually every observational setup provided that the signal-to-noise ratio is sufficiently high. The optimal extraction increases the signal-to-noise ratio typically by a factor of 1.5, while simultaneously correcting for resulting flux losses. The wavelength calibration is found to be accurate to an uncertainty level of approximately 0.02 Angstrom. We find that most of the X-shooter systematics can be reliably modeled and corrected for. This offers the possibility of comparing observations on different nights and with different telescope pointings and instrumental setups, thereby facilitating a robust statistical analysis of large datasets.Comment: 22 pages, 18 figures, Accepted for publication in Astronomy & Astrophysics; V2 contains a minor change in the abstract. We note that we did not test X-shooter pipeline versions 2.0 or later. V3 contains an updated referenc

Image segmentation and reconstruction of 3D surfaces from carotid ultrasound images

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Object recognition in infrared imagery using appearance-based methods

Abstract unavailable please refer to PD