Feature Extraction and Classification of Flaws in Radio Graphical Weld Images Using ANN

In this paper, a novel approach for the detection and classification of flaws in weld images is presented. Computer based weld image analysis is most significant method. The method has been applied for detecting and discriminating flaws in the weld that may corresponds false alarms or all possible nine types of weld defects (Slag Inclusion, Wormhole, Porosity, Incomplete penetration, Under cuts, Cracks, Lack of fusion, Weaving fault Slag line), after being successfully tested on80 radiographic images obtained from EURECTEST, International scientific Association Brussels, Belgium, and 24 radiographs of ship weld provided by Technic Control Co. (Poland) were used, obtained from Ioannis Valavanis Greece.. The procedure to detect all the types of flaws and feature extraction is implemented by segmentation algorithm which can overcome computer complexity problem. Our problem focuses on the high performance classification by optimization of feature set by various selection algorithms like sequential forward search (SFS), sequential backward search algorithm (SBS) and sequential forward floating search algorithm (SFFS). Features are important for measuring parameters which leads in directional to understand image. We introduced 23 geometric features, and 14 texture features. The Experimental results show that our proposed method gives good performance of radiographic images

A study of hough transform for weld extraction

The process of joining metals is called welding. At times, selecting a poor quality material or improper usage of welding technologies may cause defects in welded joints. Some of these welded joints have to be tested nondestructively, because their failure can cause lot of damage, for instance in power plants. Radiography is a very common method for non-destructive testing of welds. It is done by certified weld inspectors who have knowledge about weld flaws, looking at the radiograph of the welded joint with naked eye. The judgment of the weld inspector can be biased; subjective, because it is dependent on his/her experience. This manual method can also become very time consuming. Many researches were exploring computer aided examination of radiographic images in early 1990’s. With much advancement in computer vision and image processing technologies, they are being used to find more effective ways of automatic weld inspection. These days, fuzzy based methods are being widely used in this area too. The first step in automatic weld inspection is to locate the welds or find a Region of Interest (ROI) in the radiographic image [7]. In this thesis, a Standard Hough Transform (SHT) based methodology is developed for weld extraction. Firstly, we have done binarization of image to remove the background and non-welds. For binarization, optimal binary threshold is found by a metaheuristic –Simulated annealing. Secondly, we use SHT to generate the Hough Transform matrix of all non-zero points in the binary image. Thirdly, we have explored two different paths to find a meaningful set of lines in the binarized image that are welds. Finally, these lines are verified as weld using a weld-peak detection procedure. Weld-peak detection is also helpful to remove any non-welds that were remaining. We have used 25 digitized radiographic images containing 100 welds to test the method in terms of true detection and false alarm rate

Reconstruction of Patient-Specific Bone Models from X-Ray Radiography

The availability of a patient‐specific bone model has become an increasingly invaluable addition to orthopedic case evaluation and planning [1]. Utilized within a wide range of specialized visualization and analysis tools, such models provide unprecedented wealth of bone shape information previously unattainable using traditional radiographic imaging [2]. In this work, a novel bone reconstruction method from two or more x‐ray images is described. This method is superior to previous attempts in terms of accuracy and repeatability. The new technique accurately models the radiological scene in a way that eliminates the need for expensive multi‐planar radiographic imaging systems. It is also flexible enough to allow for both short and long film imaging using standard radiological protocols, which makes the technology easily utilized in standard clinical setups

Deep learning technology for weld defects classification based on transfer learning and activation features

Weld defects detection using X-ray images is an effective method of nondestructive testing. Conventionally, this work is based on qualified human experts, although it requires their personal intervention for the extraction and classification of heterogeneity. Many approaches have been done using machine learning (ML) and image processing tools to solve those tasks. Although the detection and classification have been enhanced with regard to the problems of low contrast and poor quality, their result is still unsatisfying. Unlike the previous research based on ML, this paper proposes a novel classification method based on deep learning network. In this work, an original approach based on the use of the pretrained network AlexNet architecture aims at the classification of the shortcomings of welds and the increase of the correct recognition in our dataset. Transfer learning is used as methodology with the pretrained AlexNet model. For deep learning applications, a large amount of X-ray images is required, but there are few datasets of pipeline welding defects. For this, we have enhanced our dataset focusing on two types of defects and augmented using data augmentation (random image transformations over data such as translation and reflection). Finally, a fine-tuning technique is applied to classify the welding images and is compared to the deep convolutional activation features (DCFA) and several pretrained DCNN models, namely, VGG-16, VGG-19, ResNet50, ResNet101, and GoogLeNet. The main objective of this work is to explore the capacity of AlexNet and different pretrained architecture with transfer learning for the classification of X-ray images. The accuracy achieved with our model is thoroughly presented. The experimental results obtained on the weld dataset with our proposed model are validated using GDXray database. The results obtained also in the validation test set are compared to the others offered by DCNN models, which show a best performance in less time. This can be seen as evidence of the strength of our proposed classification model.This work has been partially funded by the Spanish Government through Project RTI2018-097088-B-C33 (MINECO/FEDER, UE)

Industrial X-ray Image Analysis with Deep Neural Networks Robust to Unexpected Input Data

X-ray inspection is often an essential part of quality control within quality critical manufacturing industries. Within such industries, X-ray image interpretation is resource intensive and typically conducted by humans. An increased level of automatization would be preferable, and recent advances in artificial intelligence (e.g., deep learning) have been proposed as solutions. However, typically, such solutions are overconfident when subjected to new data far from the training data, so-called out-of-distribution (OOD) data; we claim that safe automatic interpretation of industrial X-ray images, as part of quality control of critical products, requires a robust confidence estimation with respect to OOD data. We explored if such a confidence estimation, an OOD detector, can be achieved by explicit modeling of the training data distribution, and the accepted images. For this, we derived an autoencoder model trained unsupervised on a public dataset with X-ray images of metal fusion welds and synthetic data. We explicitly demonstrate the dangers with a conventional supervised learning-based approach and compare it to the OOD detector. We achieve true positive rates of around 90% at false positive rates of around 0.1% on samples similar to the training data and correctly detect some example OOD data

Artificial Intelligence in Oral Health

This Special Issue is intended to lay the foundation of AI applications focusing on oral health, including general dentistry, periodontology, implantology, oral surgery, oral radiology, orthodontics, and prosthodontics, among others

Automatic knee joint space measurement from plain radiographs

Abstract. Knee osteoarthritis is a common joint disease and one of the leading causes of disability. The disease is characterized by loss of articular cartilage and bone remodeling. Tissue deformations eventually lead to joint space narrowing which can be detected from plain radiographs. Joint space narrowing is typically measured by an experienced radiologist manually, which can be time consuming and error prone process. The aim of this study was to develop and evaluate a fully automatic joint space width measurement method for bilateral knee radiographs. The knee joint was localized from the x-ray images using template matching and the joint space was delineated using active shape model (ASM). Two different automatic joint space measurement methods were tested and the results were validated against manual measurements performed by an experienced researcher. The first joint space width measurements were done by binarizing the joint space and measuring the local thickness of the binary mask using disk fitting. The second method classified bone pixels to tibia and femur. Classification was based on the ASM delineation. Nearest neighbors between femur and tibia were then used to find the joint space width. An automatic method for tibial region of interest (ROI) selection was also implemented. The algorithms used in this thesis were also made publicly available online. The automatically obtained joint space widths were in line with manual measurements. Higher accuracy was obtained using the disk fitting algorithm. Automatic Tibial ROI selection was accurate, although the orientation of the joint was ignored in this study. An open source software with a simple graphical user interface and visualization tools was also developed. Computationally efficient and easily explainable methods were utilized in order to improve accessibility and transparency of computer assisted diagnosis of knee osteoarthritis.Tiivistelmä. Polvinivelrikko on eräs yleisimpiä niveltauteja sekä yksi merkittävimmistä liikuntavammojen aiheuttajista. Nivelrikolle ominaisia piirteitä ovat nivelruston vaurioituminen ja muutokset nivelrustonalaisessa luussa. Kudosten muutokset ja vauriot johtavat lopulta niveltilan kaventumiseen, mikä voidaan havaita röntgenkuvista. Tavallisesti kokenut radiologi tekee niveltilan mittaukset manuaalisesti, mikä vaatii usein paljon aikaa ja on lisäksi virhealtis prosessi. Tämän tutkielman tavoitteena oli kehittää täysin automaattinen niveltilan mittausmenetelmä bilateraalisille polven röntgenkuville. Polvinivel paikallistettiin röntgenkuvista muotoon perustuvalla hahmontunnistuksella ja nivelväli rajattiin käyttämällä aktiivista muodon sovitusta (active shape model, ASM). Nivelvälin mittaukseen käytettiin kahta eri menetelmää, joita verrattiin kokeneen tutkijan tekemiin manuaalisiin mittauksiin. Ensimmäinen nivelvälin mittausmenetelmä sovitti ympyränmuotoisia maskeja niveltilasta tehtyyn binäärimaskiin. Toinen mittausmenetelmä luokitteli luuhun kuuluvat pikselit sääri- ja reisiluuhun. Luokittelu perustui aikaisemmin tehtyyn automaattiseen nivelvälin rajaukseen. Nivelvälin mittaukseen käytettiin lähimpiä naapuripikseleitä sääri- ja reisiluusta. Työssä kehitettiin myös menetelmä automaattiseen sääriluun mielenkiintoalueiden (region of interest, ROI) valintaan. Käytetyt algoritmit ovat julkisesti saatavilla verkossa. Automaattiset nivelväli mittaukset vastasivat manuaalisia mittauksia hyvin. Parempi tarkkuus saatiin käyttämällä ympyrän sovitusta hyödyntävää algoritmia nivelvälin mittaukseen. Sääriluun mielenkiintoalueet onnistuttiin määrittämään automaattisesti, tosin nivelen orientaatiota ei huomioitu tässä työssä. Lisäksi kehitettiin avoimen lähdekoodin ohjelmisto yksinkertaisella graafisella käyttöliittymällä ja visualisointityökaluilla. Työssä käytettiin laskennallisesti tehokkaita ja helposti selitettäviä menetelmiä, mikä edesauttaa tietokoneavusteisen menetelmien käyttöä polvinivelrikon tutkimuksessa

Automated dental identification: A micro-macro decision-making approach

Identification of deceased individuals based on dental characteristics is receiving increased attention, especially with the large volume of victims encountered in mass disasters. In this work we consider three important problems in automated dental identification beyond the basic approach of tooth-to-tooth matching.;The first problem is on automatic classification of teeth into incisors, canines, premolars and molars as part of creating a data structure that guides tooth-to-tooth matching, thus avoiding illogical comparisons that inefficiently consume the limited computational resources and may also mislead the decision-making. We tackle this problem using principal component analysis and string matching techniques. We reconstruct the segmented teeth using the eigenvectors of the image subspaces of the four teeth classes, and then call the teeth classes that achieve least energy-discrepancy between the novel teeth and their approximations. We exploit teeth neighborhood rules in validating teeth-classes and hence assign each tooth a number corresponding to its location in a dental chart. Our approach achieves 82% teeth labeling accuracy based on a large test dataset of bitewing films.;Because dental radiographic films capture projections of distinct teeth; and often multiple views for each of the distinct teeth, in the second problem we look for a scheme that exploits teeth multiplicity to achieve more reliable match decisions when we compare the dental records of a subject and a candidate match. Hence, we propose a hierarchical fusion scheme that utilizes both aspects of teeth multiplicity for improving teeth-level (micro) and case-level (macro) decision-making. We achieve a genuine accept rate in excess of 85%.;In the third problem we study the performance limits of dental identification due to features capabilities. We consider two types of features used in dental identification, namely teeth contours and appearance features. We propose a methodology for determining the number of degrees of freedom possessed by a feature set, as a figure of merit, based on modeling joint distributions using copulas under less stringent assumptions on the dependence between feature dimensions. We also offer workable approximations of this approach

Texture and Colour in Image Analysis

Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews