Machine Vision Application For Automatic Defect Segmentation In Weld Radiographs

Objektif penyelidikan ini adalah untuk membangunkan satu kaedah peruasan kecacatan kimpalan automatik yang boleh meruas pelbagai jenis kecacatan kimpalan yang wujud dalam imej radiografi kimpalan. Kaedah segmentasi kecacatan automatik yang dibangunkan terdir:i daripada tiga algoritma utama, iaitu algoritma penyingkiran label, algoritma pengenalpastian bahagian kimpalan dan algoritma segmentasi kecacatan kimpalan. Algoritma penyingkiran label dibangunkan untuk mengenalpasti dan menyingkirkan label yang terdapat pada imej radiograf kimpalan secara automatik, sebelum algoritma pengenalpastian bahagian kimpalan dan algortima segmentasi kecacatan diaplikasikan ke atas imej radiografi. Satu algoritma pengenalpastian bahagian kimpalan juga dibangunkan dengan tujuan mengenalpasti bahagian kimpalan dalam imej radiogaf secara automatik dengan menggunakan profil keamatan yang diperoleh daripada imej radiografi. The objective of the research is to develop an automatic weld defect segmentation methodology to segment different types of defects in radiographic images of welds. The segmentation methodology consists of three main algorithms. namely label removal algorithm. weld extraction algorithm and defect segmentation algorithm. The label removal algorithm was developed to detect and remove labels that are printed on weld radiographs automatically before weld extraction algorithm and defect detection algorithm are applied. The weld extraction algorithm was developed to locate and extract welds automatically from the intensity profiles taken across the image by using graphical analysis. This algorithm was able to extract weld from a radiograph regardless of whether the intensity profile is Gaussian or otherwise. This method is an improvement compared to the previous weld extraction methods which are limited to weld image with Gaussian intensity profiles. Finally. a defect segmentation algorithm was developed to segment the defects automatically from the image using background subtraction and rank leveling method

A 3D Framework for Characterizing Microstructure Evolution of Li-Ion Batteries

Lithium-ion batteries are commonly found in many modern consumer devices, ranging from portable computers and mobile phones to hybrid- and fully-electric vehicles. While improving efficiencies and increasing reliabilities are of critical importance for increasing market adoption of the technology, research on these topics is, to date, largely restricted to empirical observations and computational simulations. In the present study, it is proposed to use the modern technique of X-ray microscopy to characterize a sample of commercial 18650 cylindrical Li-ion batteries in both their pristine and aged states. By coupling this approach with 3D and 4D data analysis techniques, the present study aimed to create a research framework for characterizing the microstructure evolution leading to capacity fade in a commercial battery. The results indicated the unique capabilities of the microscopy technique to observe the evolution of these batteries under aging conditions, successfully developing a workflow for future research studies

An Empirical Examination of Frontal Sinus Outline Variability Using Elliptic Fourier Analysis: Implications for Identification, Standardization, and Legal Admissibility

The comparison of frontal sinus radiographs for positive identification has become an increasingly applied and accepted technique among forensic anthropologists, radiologists, and pathologists. However, the current method of outline comparison by visual assessment fails to meet evidence admissibility guidelines as set forth in the 1993 case of Daubert v. Merrell-Dow Pharmaceuticals, Inc. Specifically, no empirical testing of the uniqueness of frontal sinus outlines has ever been performed, there has been no evaluation of the probability of misidentification using the technique, there are no standards controlling the technique’s operation, and there are no subjective standards for confirming or rejecting a putative identification. Despite the fact that identifications based upon frontal sinus radiograph comparisons have been routinely accepted by scientists, medical examiners and law enforcement officers, these shortcomings could pose serious problems if forensic scientists were ever called upon to testify regarding such an identification in trial. This study investigated frontal sinus outline variability using Elliptic Fourier Analysis (EFA), a geometric morphometric approach that fits a closed curve to an ordered set of data points, generating a set of coefficients that can be treated as shape descriptors used as variables in discriminatory or other multivariate analyses, or used to reproduce the outline. By modeling 2-dimensional representations of frontal sinuses (as seen in posterior-anterior cranial radiographs) as closed contours by digitizing their outer borders, differences in their shapes were assessed quantitatively by comparing the Euclidean distances between the EFA-generated outlines. The probability of misidentification was assessed using likelihood ratios and posterior probabilities based on the EFA coefficients. Results showed that there is a quantifiable and significant difference between the shapes of different individuals’ frontal sinus outlines as represented by Euclidean distances, since distances between outlines of different individuals were shown to be significantly larger than those between replicates (simulated antemortem and postmortem) of the same individual. Likelihood ratios using EFA coefficients showed that the probability of a frontal sinus match given the correct identification versus the probability of a match from the population at large was very high, and therefore the probability of misidentification was very low. This study concluded that for individuals with sufficiently remarkable frontal sinus outlines, using EFA coefficients of digitized frontal sinus outlines to estimate the probability of a correct identification, and thereby confirm or reject a presumptive identification, is a reliable technique. Given these results, EFA comparison of frontal sinus outlines is recommended when it may be necessary to provide quantitative substantiation for a forensic identification based on these structures

A Survey on Artificial Intelligence Techniques for Biomedical Image Analysis in Skeleton-Based Forensic Human Identification

This paper represents the first survey on the application of AI techniques for the analysis of biomedical images with forensic human identification purposes. Human identification is of great relevance in today’s society and, in particular, in medico-legal contexts. As consequence, all technological advances that are introduced in this field can contribute to the increasing necessity for accurate and robust tools that allow for establishing and verifying human identity. We first describe the importance and applicability of forensic anthropology in many identification scenarios. Later, we present the main trends related to the application of computer vision, machine learning and soft computing techniques to the estimation of the biological profile, the identification through comparative radiography and craniofacial superimposition, traumatism and pathology analysis, as well as facial reconstruction. The potentialities and limitations of the employed approaches are described, and we conclude with a discussion about methodological issues and future research.Spanish Ministry of Science, Innovation and UniversitiesEuropean Union (EU) PGC2018-101216-B-I00Regional Government of Andalusia under grant EXAISFI P18-FR-4262Instituto de Salud Carlos IIIEuropean Union (EU) DTS18/00136European Commission H2020-MSCA-IF-2016 through the Skeleton-ID Marie Curie Individual Fellowship 746592Spanish Ministry of Science, Innovation and Universities-CDTI, Neotec program 2019 EXP-00122609/SNEO-20191236European Union (EU)Xunta de Galicia ED431G 2019/01European Union (EU) RTI2018-095894-B-I0

Prototypes for Content-Based Image Retrieval in Clinical Practice

Content-based image retrieval (CBIR) has been proposed as key technology for computer-aided diagnostics (CAD). This paper reviews the state of the art and future challenges in CBIR for CAD applied to clinical practice

Cone beam computed tomography in the diagnosis of Stafne bone cavity: Report of seven cases and review of the open-access literature.

Stafne bone cavity (SBC) is a rare entity to find on panoramic radiography and on cone beam computed tomography. We reviewed in a systematic way the open-access literature from PubMed and DOAJ. We also proposed a new methodology consisting of collaboration with private practitioners, application of participative science approach, and open science practices, and using social media tool to obtain and describe seven different cases of SBC. We finally propose a new matrix table for classification of anatomical types of SBC already described and those yet to be described in open-access literature.Stafne bone cavity (SBC) is a rare entity to find on panoramic radiography and on cone beam computed tomography. We reviewed in a systematic way the open-access literature from PubMed and DOAJ. We also proposed a new methodology consisting of collaboration with private practitioners, application of participative science approach, and open science practices, and using social media tool to obtain and describe seven different cases of SBC. We finally propose a new matrix table for classification of anatomical types of SBC already described and those yet to be described in open-access literature

Image processing for plastic surgery planning

This thesis presents some image processing tools for plastic surgery planning. In particular, it presents a novel method that combines local and global context in a probabilistic relaxation framework to identify cephalometric landmarks used in Maxillofacial plastic surgery. It also uses a method that utilises global and local symmetry to identify abnormalities in CT frontal images of the human body. The proposed methodologies are evaluated with the help of several clinical data supplied by collaborating plastic surgeons