Suppression of the contrast of ribs in chest radiographs by means of massive training artificial neural network

ABSTRACT We developed a method for suppression of the contrast of ribs in chest radiographs by means of a massive training artificial neural network (MTANN). The MTANN is a trainable highly nonlinear filter that can be trained by using input chest radiographs and the corresponding teacher images. We used either the soft-tissue image or the bone image obtained by use of a dual-energy subtraction technique as the teacher image for suppression of ribs in chest radiographs. When the soft-tissue images were used as the teacher images, the MTANN directly produced a "soft-tissue-image-like" image where the contrast of ribs was suppressed. When the bone images were used as the teacher images, the MTANN was able to produce a "bone-image-like" image, and then was subtracted from the corresponding chest radiograph to produce a bone-subtracted image where ribs are suppressed. Thus, the two kinds of rib-suppressed images, i.e., the soft-tissue-image-like image and the bone-subtracted image, could be produced by use of the MTANNs trained with two different teacher images. We applied each of the two trained MTANNs to non-training chest radiographs to investigate the difference between the processed images. The results showed that the contrast of ribs in chest radiographs almost disappeared, and was reduced to less than 10% in both processed images. The contrast of ribs was reduced slightly better in the soft-tissue-image-like images than in the bone-subtracted images, whereas soft-tissue opacities such as lung vessels and nodules were maintained better in the bone-subtracted images. Therefore, the use of the bone images as the teacher images for training the MTANN has produced better rib-suppressed images where soft-tissue opacities were substantially maintained. A method for rib suppression using the MTANN would be useful for radiologists as well as CAD schemes in detection of lung diseases such as nodules in chest radiographs

Massive training artificial neural network (MTANN) for detecting abnormalities in medical images

A method of training an artificial neural network (ANN) involves receiving a likelihood distribution map as a teacher image, receiving a training image, moving a local window across sub-regions of the training image to obtain respective sub-region pixel sets, inputting the sub-region pixel sets to the ANN so that it provides output pixel values that are compared to output pixel values of corresponding teacher image pixel values to determine an error, and training the ANN to reduce the error. A method of detecting a target structure in an image involves scanning a local window across sub-regions of the image by moving the local window for each sub-region so as to obtain respective sub-region pixel sets, inputting the sub-region pixel sets to an ANN so that it provides respective output pixel values that represent likelihoods that respective image pixels are part of a target structure, the output pixel values collectively constituting a likelihood distribution map. Another method for detecting a target structure involves training N parallel ANNs on either (A) a same target structure and N mutually different non-target structures, or (B) a same non-target structure and N mutually different target structures, the ANNs outputting N respective indications of whether the image includes a target structure or a non-target structure, and combining the N indications to form a combined indication of whether the image includes a target structure or a non-target structure. The invention provides related apparatus and computer program products storing executable instructions to perform the methods

Massive training artificial neural network (MTANN) for detecting abnormalities in medical images

A method of training an artificial neural network (ANN) involves receiving a likelihood distribution map as a teacher image, receiving a training image, moving a local window across sub-regions of the training image to obtain respective sub-region pixel sets, inputting the sub-region pixel sets to the ANN so that it provides output pixel values that are compared to output pixel values of corresponding teacher image pixel values to determine an error, and training the ANN to reduce the error. A method of detecting a target structure in an image involves scanning a local window across sub-regions of the image by moving the local window for each sub-region so as to obtain respective sub-region pixel sets, inputting the sub-region pixel sets to an ANN so that it provides respective output pixel values that represent likelihoods that respective image pixels are part of a target structure, the output pixel values collectively constituting a likelihood distribution map. Another method for detecting a target structure involves training N parallel ANNs on either (A) a same target structure and N mutually different non-target structures, or (B) a same non-target structure and N mutually different target structures, the ANNs outputting N respective indications of whether the image includes a target structure or a non-target structure, and combining the N indications to form a combined indication of whether the image includes a target structure or a non-target structure. The invention provides related apparatus and computer program products storing executable instructions to perform the methods

Artificial intelligence for advanced manufacturing quality

100 p.This Thesis addresses the challenge of AI-based image quality control systems applied to manufacturing industry, aiming to improve this field through the use of advanced techniques for data acquisition and processing, in order to obtain robust, reliable and optimal systems. This Thesis presents contributions onthe use of complex data acquisition techniques, the application and design of specialised neural networks for the defect detection, and the integration and validation of these systems in production processes. It has been developed in the context of several applied research projects that provided a practical feedback of the usefulness of the proposed computational advances as well as real life data for experimental validation

Robust learning algorithms for spiking and rate-based neural networks

Inspired by the remarkable properties of the human brain, the fields of machine learning, computational neuroscience and neuromorphic engineering have achieved significant synergistic progress in the last decade. Powerful neural network models rooted in machine learning have been proposed as models for neuroscience and for applications in neuromorphic engineering. However, the aspect of robustness is often neglected in these models. Both biological and engineered substrates show diverse imperfections that deteriorate the performance of computation models or even prohibit their implementation. This thesis describes three projects aiming at implementing robust learning with local plasticity rules in neural networks. First, we demonstrate the advantages of neuromorphic computations in a pilot study on a prototype chip. Thereby, we quantify the speed and energy consumption of the system compared to a software simulation and show how on-chip learning contributes to the robustness of learning. Second, we present an implementation of spike-based Bayesian inference on accelerated neuromorphic hardware. The model copes, via learning, with the disruptive effects of the imperfect substrate and benefits from the acceleration. Finally, we present a robust model of deep reinforcement learning using local learning rules. It shows how backpropagation combined with neuromodulation could be implemented in a biologically plausible framework. The results contribute to the pursuit of robust and powerful learning networks for biological and neuromorphic substrates

Forum Bildverarbeitung 2016

Bildverarbeitung spielt in vielen Bereichen der Technik zur schnellen und berührungslosen Datenerfassung eine Schlüsselrolle. Der vorliegende Tagungsband des „Forums Bildverarbeitung“, das am 1. und 2. Dezember 2016 in Karlsruhe als Veranstaltung des Karlsruher Instituts für Technologie und des Fraunhofer-Instituts für Optronik, Systemtechnik und Bildauswertung stattfand, enthält die Aufsätze der eingegangenen Beiträge. Darin wird über aktuelle Trends und Lösungen der Bildverarbeitung berichtet

Combining omnidirectional vision with polarization vision for robot navigation

La polarisation est le phénomène qui décrit les orientations des oscillations des ondes lumineuses qui sont limitées en direction. La lumière polarisée est largement utilisée dans le règne animal,à partir de la recherche de nourriture, la défense et la communication et la navigation. Le chapitre (1) aborde brièvement certains aspects importants de la polarisation et explique notre problématique de recherche. Nous visons à utiliser un capteur polarimétrique-catadioptrique car il existe de nombreuses applications qui peuvent bénéficier d'une telle combinaison en vision par ordinateur et en robotique, en particulier pour l'estimation d'attitude et les applications de navigation. Le chapitre (2) couvre essentiellement l'état de l'art de l'estimation d'attitude basée sur la vision.Quand la lumière non-polarisée du soleil pénètre dans l'atmosphère, l'air entraine une diffusion de Rayleigh, et la lumière devient partiellement linéairement polarisée. Le chapitre (3) présente les motifs de polarisation de la lumière naturelle et couvre l'état de l'art des méthodes d'acquisition des motifs de polarisation de la lumière naturelle utilisant des capteurs omnidirectionnels (par exemple fisheye et capteurs catadioptriques). Nous expliquons également les caractéristiques de polarisation de la lumière naturelle et donnons une nouvelle dérivation théorique de son angle de polarisation.Notre objectif est d'obtenir une vue omnidirectionnelle à 360 associée aux caractéristiques de polarisation. Pour ce faire, ce travail est basé sur des capteurs catadioptriques qui sont composées de surfaces réfléchissantes et de lentilles. Généralement, la surface réfléchissante est métallique et donc l'état de polarisation de la lumière incidente, qui est le plus souvent partiellement linéairement polarisée, est modifiée pour être polarisée elliptiquement après réflexion. A partir de la mesure de l'état de polarisation de la lumière réfléchie, nous voulons obtenir l'état de polarisation incident. Le chapitre (4) propose une nouvelle méthode pour mesurer les paramètres de polarisation de la lumière en utilisant un capteur catadioptrique. La possibilité de mesurer le vecteur de Stokes du rayon incident est démontré à partir de trois composants du vecteur de Stokes du rayon réfléchi sur les quatre existants.Lorsque les motifs de polarisation incidents sont disponibles, les angles zénithal et azimutal du soleil peuvent être directement estimés à l'aide de ces modèles. Le chapitre (5) traite de l'orientation et de la navigation de robot basées sur la polarisation et différents algorithmes sont proposés pour estimer ces angles dans ce chapitre. A notre connaissance, l'angle zénithal du soleil est pour la première fois estimé dans ce travail à partir des schémas de polarisation incidents. Nous proposons également d'estimer l'orientation d'un véhicule à partir de ces motifs de polarisation.Enfin, le travail est conclu et les possibles perspectives de recherche sont discutées dans le chapitre (6). D'autres exemples de schémas de polarisation de la lumière naturelle, leur calibrage et des applications sont proposées en annexe (B).Notre travail pourrait ouvrir un accès au monde de la vision polarimétrique omnidirectionnelle en plus des approches conventionnelles. Cela inclut l'orientation bio-inspirée des robots, des applications de navigation, ou bien la localisation en plein air pour laquelle les motifs de polarisation de la lumière naturelle associés à l'orientation du soleil à une heure précise peuvent aboutir à la localisation géographique d'un véhiculePolarization is the phenomenon that describes the oscillations orientations of the light waves which are restricted in direction. Polarized light has multiple uses in the animal kingdom ranging from foraging, defense and communication to orientation and navigation. Chapter (1) briefly covers some important aspects of polarization and explains our research problem. We are aiming to use a polarimetric-catadioptric sensor since there are many applications which can benefit from such combination in computer vision and robotics specially robot orientation (attitude estimation) and navigation applications. Chapter (2) mainly covers the state of art of visual based attitude estimation.As the unpolarized sunlight enters the Earth s atmosphere, it is Rayleigh-scattered by air, and it becomes partially linearly polarized. This skylight polarization provides a signi cant clue to understanding the environment. Its state conveys the information for obtaining the sun orientation. Robot navigation, sensor planning, and many other applications may bene t from using this navigation clue. Chapter (3) covers the state of art in capturing the skylight polarization patterns using omnidirectional sensors (e.g fisheye and catadioptric sensors). It also explains the skylight polarization characteristics and gives a new theoretical derivation of the skylight angle of polarization pattern. Our aim is to obtain an omnidirectional 360 view combined with polarization characteristics. Hence, this work is based on catadioptric sensors which are composed of reflective surfaces and lenses. Usually the reflective surface is metallic and hence the incident skylight polarization state, which is mostly partially linearly polarized, is changed to be elliptically polarized after reflection. Given the measured reflected polarization state, we want to obtain the incident polarization state. Chapter (4) proposes a method to measure the light polarization parameters using a catadioptric sensor. The possibility to measure the incident Stokes is proved given three Stokes out of the four reflected Stokes. Once the incident polarization patterns are available, the solar angles can be directly estimated using these patterns. Chapter (5) discusses polarization based robot orientation and navigation and proposes new algorithms to estimate these solar angles where, to the best of our knowledge, the sun zenith angle is firstly estimated in this work given these incident polarization patterns. We also propose to estimate any vehicle orientation given these polarization patterns. Finally the work is concluded and possible future research directions are discussed in chapter (6). More examples of skylight polarization patterns, their calibration, and the proposed applications are given in appendix (B). Our work may pave the way to move from the conventional polarization vision world to the omnidirectional one. It enables bio-inspired robot orientation and navigation applications and possible outdoor localization based on the skylight polarization patterns where given the solar angles at a certain date and instant of time may infer the current vehicle geographical location.DIJON-BU Doc.électronique (212319901) / SudocSudocFranceF

Proceedings. 22. Workshop Computational Intelligence, Dortmund, 6. - 7. Dezember 2012

Dieser Tagungsband enthält die Beiträge des 22. Workshops "Computational Intelligence" des Fachausschusses 5.14 der VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik (GMA) der vom 6. - 7. Dezember 2012 in Dortmund stattgefunden hat. Die Schwerpunkte sind Methoden, Anwendungen und Tools für - Fuzzy-Systeme, - Künstliche Neuronale Netze, - Evolutionäre Algorithmen und - Data-Mining-Verfahren sowie der Methodenvergleich anhand von industriellen Anwendungen und Benchmark-Problemen

Aeronautical engineering: A continuing bibliography with indexes (supplement 301)

This bibliography lists 1291 reports, articles, and other documents introduced into the NASA scientific and technical information system in Feb. 1994. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment, and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics