Multiresolution analysis using wavelet, ridgelet, and curvelet transforms for medical image segmentation

Copyright @ 2011 Shadi AlZubi et al. This article has been made available through the Brunel Open Access Publishing Fund.The experimental study presented in this paper is aimed at the development of an automatic image segmentation system for classifying region of interest (ROI) in medical images which are obtained from different medical scanners such as PET, CT, or MRI. Multiresolution analysis (MRA) using wavelet, ridgelet, and curvelet transforms has been used in the proposed segmentation system. It is particularly a challenging task to classify cancers in human organs in scanners output using shape or gray-level information; organs shape changes throw different slices in medical stack and the gray-level intensity overlap in soft tissues. Curvelet transform is a new extension of wavelet and ridgelet transforms which aims to deal with interesting phenomena occurring along curves. Curvelet transforms has been tested on medical data sets, and results are compared with those obtained from the other transforms. Tests indicate that using curvelet significantly improves the classification of abnormal tissues in the scans and reduce the surrounding noise

The Burst and Transient Source Experiment Earth Occultation Technique

An Earth orbiting detector sensitive to gamma ray photons will see step-like occultation features in its counting rate when a gamma ray point source crosses the Earth's limb. This is due to the change in atmospheric attenuation of the gamma rays along the line of sight. In an uncollimated detector, these occultation features can be used to locate and monitor astrophysical sources provided their signals can be individually separated from the detector background. We show that the Earth occultation technique applied to the Burst and Transient Source Experiment (BATSE) on the Compton Gamma Ray Observatory (CGRO) is a viable and flexible all-sky monitor in the low energy gamma ray and hard X-ray energy range (20 keV - 1 MeV). The method is an alternative to more sophisticated photon imaging devices for astronomy, and can serve well as a cost-effective science capability for monitoring the high energy sky. Here we describe the Earth occultation technique for locating new sources and for measuring source intensity and spectra without the use of complex background models. Examples of transform imaging, step searches, spectra, and light curves are presented. Systematic uncertainties due to source confusion, detector response, and contamination from rapid background fluctuations are discussed and analyzed for their effect on intensity measurements. A sky location-dependent average systematic error is derived as a function of galactic coordinates. The sensitivity of the technique is derived as a function of incident photon energy and also as a function of angle between the source and the normal to the detector entrance window. Occultations of the Crab Nebula by the Moon are used to calibrate Earth occultation flux measurements independent of possible atmospheric scattering effects.Comment: 39 pages, 24 figures. Accepted for publication in the Astrophysical Journal Supplement

Computer Vision Inspection And Classification On Printed Circuit Boards For Flux Defects

The manual inspection of Printed Circuit Boards (PCB) is labor intensive and slow down the production line. During the assembly process, the defective PCBs with flux defects if not detected and remove, it can create corrosion and cause harmful effects on the board itself. As such, an automated inspection system is very much needed to overcome the aforementioned problems in PCB production line. The main objective of this work is to develop a real-time machine vision system for quality assessment of PCBs by detecting defectives PCBs. The proposed system should be able to detect flux defect on PCB board during the re-flow process and achieve good accuracy of the PCB quality checking. The proposed system is named as An Automatic Inspection System for Printed Circuit Boards (AIS-PCB), involves design and fabrication of a total automation control system involving the use of mechanical PCB loader/un-loader, robotic pneumatic system handler with vacuum cap and a vision inspection station that makes a decision either to accept or reject. The decision making part involves classifier training of PCB images. Prior to ANN training, the images need to be processed by the image processing and feature extraction. The image processing system is based on pattern matching and color image analysis techniques. The shape of the PCB pins is analyzed by using pattern matching technique to detect the PCB flux defect area. After that, the color analysis of the flux defect on a PCB boards are processed based on their red color pixel percentage in Red, Green and Blue (RGB) model. The red color filter band mean value of histogram is measured and compared to the value threshold to determine the occurrence of flux defect on the PCBs. The texture of the PCB flux defect can also be extracted based on line detection of the gradient field PCB images and feature indexing by using Radon transform-based approach. The feed-forward back-propagation (FFBP) model is used as classifier to classify the product quality of the PCBs via a learning concept. A number of trainings using the FFBP are performed for the classifier to learn and match the targets. The learned classifier, when tested on the PCBs from a factory’s production line, achieves a grading accuracy of coefficient of efficiency (COE) greater than 95%. As such, it can be concluded that the developed AIS-PCB system has shown promising results by successfully classifying flux defects in PCBs through visual information and facilitates automatic inspection, thereby aiding humans in conducting rapid inspections

DTW-Radon-based Shape Descriptor for Pattern Recognition

International audienceIn this paper, we present a pattern recognition method that uses dynamic programming (DP) for the alignment of Radon features. The key characteristic of the method is to use dynamic time warping (DTW) to match corresponding pairs of the Radon features for all possible projections. Thanks to DTW, we avoid compressing the feature matrix into a single vector which would otherwise miss information. To reduce the possible number of matchings, we rely on a initial normalisation based on the pattern orientation. A comprehensive study is made using major state-of-the-art shape descriptors over several public datasets of shapes such as graphical symbols (both printed and hand-drawn), handwritten characters and footwear prints. In all tests, the method proves its generic behaviour by providing better recognition performance. Overall, we validate that our method is robust to deformed shape due to distortion, degradation and occlusion

Full tomographic reconstruction of 2D vector fields using discrete integral data

Vector field tomography is a field that has received considerable attention in recent decades. It deals with the problem of the determination of a vector field from non-invasive integral data. These data are modelled by the vectorial Radon transform. Previous attempts at solving this reconstruction problem showed that tomographic data alone are insufficient for determining a 2D band-limited vector field completely and uniquely. This paper describes a method that allows one to recover both components of a 2D vector field based only on integral data, by solving a system of linear equations. We carry out the analysis in the digital domain and we take advantage of the redundancy in the projection data, since these may be viewed as weighted sums of the local vector field's Cartesian components. The potential of the introduced method is demonstrated by presenting examples of vector field reconstruction

A parallel windowing approach to the Hough transform for line segment detection

In the wide range of image processing and computer vision problems, line segment detection has always been among the most critical headlines. Detection of primitives such as linear features and straight edges has diverse applications in many image understanding and perception tasks. The research presented in this dissertation is a contribution to the detection of straight-line segments by identifying the location of their endpoints within a two-dimensional digital image. The proposed method is based on a unique domain-crossing approach that takes both image and parameter domain information into consideration. First, the straight-line parameters, i.e. location and orientation, have been identified using an advanced Fourier-based Hough transform. As well as producing more accurate and robust detection of straight-lines, this method has been proven to have better efficiency in terms of computational time in comparison with the standard Hough transform. Second, for each straight-line a window-of-interest is designed in the image domain and the disturbance caused by the other neighbouring segments is removed to capture the Hough transform buttery of the target segment. In this way, for each straight-line a separate buttery is constructed. The boundary of the buttery wings are further smoothed and approximated by a curve fitting approach. Finally, segments endpoints were identified using buttery boundary points and the Hough transform peak. Experimental results on synthetic and real images have shown that the proposed method enjoys a superior performance compared with the existing similar representative works