The Hough Transform and the Impact of Chronic Leukemia on the Compact Bone Tissue from CT-Images Analysis

Computational analysis of X-ray Computed Tomography (CT) images allows the assessment of alteration of bone structure in adult patients with Advanced Chronic Lymphocytic Leukemia (ACLL), and may even offer a powerful tool to assess the development of the disease (prognostic potential). The crucial requirement for this kind of analysis is the application of a pattern recognition method able to accurately segment the intra-bone space in clinical CT images of the human skeleton. Our purpose is to show how this task can be accomplished by a procedure based on the use of the Hough transform technique for special families of algebraic curves. The dataset used for this study is composed of sixteen subjects including eight control subjects, one ACLL survivor, and seven ACLL victims. We apply the Hough transform approach to the set of CT images of appendicular bones for detecting the compact and trabecular bone contours by using ellipses, and we use the computed semi-axes values to infer information on bone alterations in the population affected by ACLL. The effectiveness of this method is proved against ground truth comparison. We show that features depending on the semi-axes values detect a statistically significant difference between the class of control subjects plus the ACLL survivor and the class of ACLL victims

Semi-automatic spline fitting of planar curvilinear profiles in digital images using the Hough transform

We develop a novel method for the recognition of curvilinear profiles in digital images. The proposed method, semi-automatic for both closed and open planar profiles, essentially consists of a preprocessing step exploiting an edge detection algorithm, and a main step involving the Hough transform technique. In the preprocessing step, a Canny edge detection algorithm is applied in order to obtain a reduced point set describing the profile curve to be reconstructed. Also, to identify in the profile possible sharp points like cusps, we additionally use an algorithm to find the approximated tangent vector of every edge point. In the subsequent main step, we then use a piecewisely defined Hough transform to locally recognize from the point set a low-degree piecewise polynomial curve. The final outcome of the algorithm is thus a spline curve approximating the underlined profile image. The output curve consists of polynomial pieces connected G^1 continuously, except in correspondence of the identified cusps, where the order of continu- ity is only C^0 , as expected. To illustrate effectiveness and efficiency of the new profile detection technique we present several numerical results dealing with detection of open and closed profiles in images of dif- ferent type, i.e., medical and photographic image

AutoGraff: towards a computational understanding of graffiti writing and related art forms.

The aim of this thesis is to develop a system that generates letters and pictures with a style that is immediately recognizable as graffiti art or calligraphy. The proposed system can be used similarly to, and in tight integration with, conventional computer-aided geometric design tools and can be used to generate synthetic graffiti content for urban environments in games and in movies, and to guide robotic or fabrication systems that can materialise the output of the system with physical drawing media. The thesis is divided into two main parts. The first part describes a set of stroke primitives, building blocks that can be combined to generate different designs that resemble graffiti or calligraphy. These primitives mimic the process typically used to design graffiti letters and exploit well known principles of motor control to model the way in which an artist moves when incrementally tracing stylised letter forms. The second part demonstrates how these stroke primitives can be automatically recovered from input geometry defined in vector form, such as the digitised traces of writing made by a user, or the glyph outlines in a font. This procedure converts the input geometry into a seed that can be transformed into a variety of calligraphic and graffiti stylisations, which depend on parametric variations of the strokes

Image-Based Fracture Mechanics with Digital Image Correlation and Digital Volume Correlation

Analysis that requires human judgement can add bias which may, as a result, increase uncertainty. Accurate detection of a crack and segmentation of the crack geometry is beneficial to any fracture experiment. Studies of crack behaviour, such as the effect of closure, residual stress in fatigue or elastic-plastic fracture mechanics, require data on crack opening displacement. Furthermore, the crack path can give critical information of how the crack interacts with the microstructure and stress fields. Digital Image Correlation (DIC) and Digital Volume Correlation (DVC) have been widely accepted and routinely used to measure full-field displacements in many areas of solid mechanics, including fracture mechanics. However, current practise for the extraction of crack parameters from displacement fields usually requires manual methods and are quite onerous, particularly for large amounts of data. This thesis introduces the novel application of Phase Congruency-based Crack Detection (PC-CD) to automatically detect and characterise cracks from displacement fields. Phase congruency is a powerful mathematical tool that highlights a discontinuity more efficiently than gradient based methods. Phase congruency’s invariance to the magnitude of the discontinuity and its state-of-the-art de-noising method, make it ideal for the application to crack tip displacement fields. PC-CD’s accuracy is quantified and benchmarked using both theoretical and virtual displacement fields. The accuracy of PC-CD is evaluated and compared with conventional, manual computation methods such as Heaviside function fitting and gradient based methods. It is demonstrated how PC-CD can be coupled with a new method that is based on the conjoint use of displacement fields and finite element analysis to extract the strain energy release rate of cracks automatically. The PC-CD method is extended to volume displacement fields (VPC-CD) and semi-autonomously extracts crack surface, crack front and opening displacement through the thickness. As a proof of concept, PC-CD and VPC-CD are applied to a range of fracture experiments varying in material and fracture behaviour: two ductile and one quasi-brittle for surface displacement measurements; and two quasi-brittle and one ductile for volume measurements. Using the novel PC-CD and VPC-CD analyses, the crack geometry is obtained fully automatically and without any user judgement or intervention. The geometrical parameters extracted by PC-CD and VPC-CD are validated experimentally through other tools such as: optical microscope measurements, high resolution fractography and visual inspection

Robust and flexible multi-scale medial axis computation

The principle of the multi-scale medial axis (MMA) is important in that any object is detected at a blurring scale proportional to the size of the object. Thus it provides a sound balance between noise removal and preserving detail. The robustness of the MMA has been reflected in many existing applications in object segmentation, recognition, description and registration. This thesis aims to improve the computational aspects of the MMA. The MMA is obtained by computing ridges in a “medialness” scale-space derived from an image. In computing the medialness scale-space, we propose an edge-free medialness algorithm, the Concordance-based Medial Axis Transform (CMAT). It not only depends on the symmetry of the positions of boundaries, but also is related to the symmetry of the intensity contrasts at boundaries. Therefore it excludes spurious MMA branches arising from isolated boundaries. In addition, the localisation accuracy for the position and width of an object, as well as the robustness under noisy conditions, is preserved in the CMAT. In computing ridges in the medialness space, we propose the sliding window algorithm for extracting locally optimal scale ridges. It is simple and efficient in that it can readily separate the scale dimension from the search space but avoids the difficult task of constructing surfaces of connected maxima. It can extract a complete set of MMA for interfering objects in scale-space, e.g. embedded or adjacent objects. These algorithms are evaluated using a quantitative study of their performance for 1-D signals and qualitative testing on 2-D images

Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p