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

Corrigendum for "Almost vanishing polynomials and an application to the Hough transform"

In this note we correct a technical error occurred in [M. Torrente and M.C. Beltrametti, "Almost vanishing polynomials and an application to the Hough transform", J. Algebra Appl. 13(8), (2014)]. This affects the bounds given in that paper, even though the structure and the logic of all proofs remain fully unchanged.Comment: 30 page

Simulation Models for Straight Lines Images Detection Using Hough Transform

The Hough transform (HT) is a robust parameter estimator of multidimensional features in images. The HT is an established technique which evidences a shape by mapping image edge points into a parameter space. Recently, the formulation of the HT has been extended to extract analytic arbitrary shapes which change their appearance according to similarity transformations. It finds many applications in astronomical data analysis. It enables, in particular, to develop autoadaptive, fast algorithms for the detection of automated arc line identification. The HT is a technique which is used to isolate curves of a given shape in an image. The classical HT requires that the curve be specified in some parametric form and, hence is most commonly used in the detection of regular curves. The HT has been generalized so that it is capable of detecting arbitrary curved shapes

Interplay between spinal cord and cerebral cortex metabolism in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder, characterized by a degeneration of upper and lower motor neurons leading to a progressive muscular paralysis. Although median survival most often averages 3\u20134 years, the large variability of its course (Calvo et al., 2017) raises an urgent need to develop biomarkers able to characterize the mechanisms underlying disease progression and to improve the diagnostic yield of clinical and neuro-physiological evaluation. Most studies in this setting focused on cortical response to ALS. Among these approaches, brain PET studies with 18F-\ufb02uorodeoxyglucose (FDG) already reported a signi\ufb01cant reduction in glucose metabolism (Pagani et al., 2014) in motor and premotor cortex (Kiernan et al., 1994; Abrahams et al., 1996, 2005). By contrast, involvement of the spinal cord has been characterized in relatively lower detail, mostly because of the anatomical features of this structure that limit the standardization of its evaluation. Consequently, a large uncertainty still exists about the mechanisms underlying ALS-induced damage in the spinal cord and its relationship with cortical impairment. We recently reported the potential of the Hough transform in delineating spinal cord structure and metabolic activity in a population of ALS patients subjected to FDG PET/CT (Marini et al., 2016). Speci\ufb01cally, this classical pattern recognition approach for the automatic identi\ufb01cation of straight lines in the image has been recently extended to the recognition of more complex shapes. This computational 3D approach enabled the extraction of spinal cord metabolic information from whole body images and per- mitted us to document increased glucose consumption, possibly representing a potential and independent prognostic marker (Marini et al., 2016). In the present study, we simultaneously analysed brain and spinal cord FDG uptake in a series of prospectively recruited patients submitted to brain and wholebody PET/CT

Graph Clustering, Variational Image Segmentation Methods and Hough Transform Scale Detection for Object Measurement in Images

© 2016, Springer Science+Business Media New York. We consider the problem of scale detection in images where a region of interest is present together with a measurement tool (e.g. a ruler). For the segmentation part, we focus on the graph-based method presented in Bertozzi and Flenner (Multiscale Model Simul 10(3):1090–1118, 2012) which reinterprets classical continuous Ginzburg–Landau minimisation models in a totally discrete framework. To overcome the numerical difficulties due to the large size of the images considered, we use matrix completion and splitting techniques. The scale on the measurement tool is detected via a Hough transform-based algorithm. The method is then applied to some measurement tasks arising in real-world applications such as zoology, medicine and archaeology

Machine learning in galaxy groups detection

The detection of galaxy groups and clusters is of great importance in the field of astrophysics. In particular astrophysicists are interested in the evolution and formation of these systems, as well as the interactions that occur within galaxy groups and clusters. In this thesis, we developed a probabilistic model capable of detecting galaxy groups and clusters based on the Hough transform. We called this approach probabilistic Hough transform based on adaptive local kernel (PHTALK). PHTALK was tested on a 3D realistic galaxy and mass assembly (GAMA) mock data catalogue (at close redshift z < 0:1) (mock data: contains information related to galaxies' position, redshift and other properties). We compared the performance of our PHTALK method with the performance of two versions of the standard friends-of-friends (FoF) method. As a performance measures, we used the precision versus recall curve. Furthermore, to test the efficiency of recovering the galaxy groups' and clusters' properties, we also used completeness and reliability, fragmentation and merging, velocity and mass estimation of the detected groups. The new PHTALK method outperformed the FoF methods in terms of reducing the detection of spurious agglomerations (false positives (FPs)). This smaller sensitivity to the false positive (FP) is mainly due to the clear description of the galaxy groups' model based on astrophysical prior knowledge; in particular, the fingers of god (FoG) pattern (a pattern formed by the projected velocity dispersion of galaxies, inside a galaxy group, along the line of sight). However, the FoF methods seem to outperform the PHTALK in terms of detecting galaxy groups or clusters that do not follow the FoG pattern. The main advantage of our probabilistic model is its flexibility to incorporate any prior knowledge expressed in terms of a galaxy group model