A coarse-to-fine approach to prostate boundary segmentation in ultrasound images

BACKGROUND: In this paper a novel method for prostate segmentation in transrectal ultrasound images is presented. METHODS: A segmentation procedure consisting of four main stages is proposed. In the first stage, a locally adaptive contrast enhancement method is used to generate a well-contrasted image. In the second stage, this enhanced image is thresholded to extract an area containing the prostate (or large portions of it). Morphological operators are then applied to obtain a point inside of this area. Afterwards, a Kalman estimator is employed to distinguish the boundary from irrelevant parts (usually caused by shadow) and generate a coarsely segmented version of the prostate. In the third stage, dilation and erosion operators are applied to extract outer and inner boundaries from the coarsely estimated version. Consequently, fuzzy membership functions describing regional and gray-level information are employed to selectively enhance the contrast within the prostate region. In the last stage, the prostate boundary is extracted using strong edges obtained from selectively enhanced image and information from the vicinity of the coarse estimation. RESULTS: A total average similarity of 98.76%(± 0.68) with gold standards was achieved. CONCLUSION: The proposed approach represents a robust and accurate approach to prostate segmentation

ANALYSIS OF VOCAL FOLD KINEMATICS USING HIGH SPEED VIDEO

Vocal folds are the twin in-folding of the mucous membrane stretched horizontally across the larynx. They vibrate modulating the constant air flow initiated from the lungs. The pulsating pressure wave blowing through the glottis is thus the source for voiced speech production. Study of vocal fold dynamics during voicing are critical for the treatment of voice pathologies. Since the vocal folds move at 100 - 350 cycles per second, their visual inspection is currently done by strobosocopy which merges information from multiple cycles to present an apparent motion. High Speed Digital Laryngeal Imaging(HSDLI) with a temporal resolution of up to 10,000 frames per second has been established as better suited for assessing the vocal fold vibratory function through direct recording. But the widespread use of HSDLI is limited due to lack of consensus on the modalities like features to be examined. Development of the image processing techniques which circumvents the need for the tedious and time consuming effort of examining large volumes of recording has room for improvement. Fundamental questions like the required frame rate or resolution for the recordings is still not adequately answered. HSDLI cannot get the absolute physical measurement of the anatomical features and vocal fold displacement. This work addresses these challenges through improved signal processing. A vocal fold edge extraction technique with subpixel accuracy, suited even for hard to record pediatric population is developed first. The algorithm which is equally applicable for pediatric and adult subjects, is implemented to facilitate user inspection and intervention. Objective features describing the fold dynamics, which are extracted from the edge displacement waveform are proposed and analyzed on a diverse dataset of healthy males, females and children. The sampling and quantization noise present in the recordings are analyzed and methods to mitigate them are investigated. A customized Kalman smoothing and spline interpolation on the displacement waveform is found to improve the feature estimation stability. The relationship between frame rate, spatial resolution and vibration for efficient capturing of information is derived. Finally, to address the inability to measure physical measurement, a structured light projection calibrated with respect to the endoscope is prototyped

Filtering of image sequences: on line edge detection and motion reconstruction

L'argomento della Tesi riguarda líelaborazione di sequenze di immagini, relative ad una scena in cui uno o pi˘ oggetti (possibilmente deformabili) si muovono e acquisite da un opportuno strumento di misura. A causa del processo di misura, le immagini sono corrotte da un livello di degradazione. Si riporta la formalizzazione matematica dellíinsieme delle immagini considerate, dellíinsieme dei moti ammissibili e della degradazione introdotta dallo strumento di misura. Ogni immagine della sequenza acquisita ha una relazione con tutte le altre, stabilita dalla legge del moto della scena. Líidea proposta in questa Tesi Ë quella di sfruttare questa relazione tra le diverse immagini della sequenza per ricostruire grandezze di interesse che caratterizzano la scena. Nel caso in cui si conosce il moto, líinteresse Ë quello di ricostruire i contorni dellíimmagine iniziale (che poi possono essere propagati attraverso la stessa legge del moto, in modo da ricostruire i contorni della generica immagine appartenente alla sequenza in esame), stimando líampiezza e del salto del livello di grigio e la relativa localizzazione. Nel caso duale si suppone invece di conoscere la disposizione dei contorni nellíimmagine iniziale e di avere un modello stocastico che descriva il moto; líobiettivo Ë quindi stimare i parametri che caratterizzano tale modello. Infine, si presentano i risultati dellíapplicazione delle due metodologie succitate a dati reali ottenuti in ambito biomedicale da uno strumento denominato pupillometro. Tali risultati sono di elevato interesse nellíottica di utilizzare il suddetto strumento a fini diagnostici

Automated visual tracking for studying the ontogeny of zebrafish swimming

The zebrafish Danio rerio is a widely used model organism in studies of genetics, developmental biology, and recently, biomechanics. In order to quantify changes in swimming during all stages of development, we have developed a visual tracking system that estimates the posture of fish. Our current approach assumes planar motion of the fish, given image sequences taken from a top view. An accurate geometric fish model is automatically designed and fit to the images at each time frame. Our approach works across a range of fish shapes and sizes and is therefore well suited for studying the ontogeny of fish swimming, while also being robust to common environmental occlusions. Our current analysis focuses on measuring the influence of vertebra development on the swimming capabilities of zebrafish. We examine wild-type zebrafish and mutants with stiff vertebrae (stocksteif) and quantify their body kinematics as a function of their development from larvae to adult (mutants made available by the Hubrecht laboratory, The Netherlands). By tracking the fish, we are able to measure the curvature and net acceleration along the body that result from the fish's body wave. Here, we demonstrate the capabilities of the tracking system for the escape response of wild-type zebrafish and stocksteif mutant zebrafish. The response was filmed with a digital high-speed camera at 1500 frames s–1. Our approach enables biomechanists and ethologists to process much larger datasets than possible at present. Our automated tracking scheme can therefore accelerate insight in the swimming behavior of many species of (developing) fish

A Bayesian Approach For Image-Based Underwater Target Tracking And Navigation [TC1800. A832 2007 f rb].

Operasi pemeriksaan dan pemantauan di dasar laut merupakan aktiviti penting untuk industri di luar persisiran pantai terutamanya bagi tujuan pembangunan dan pemasangan infrastruktur. Sejak kebelakangan ini, pemasangan struktur di dasar laut seperti saluran paip gas atau petroleum dan kabel telekomunikasi telah meningkat. Pemeriksaan rutin adalah sangat mustahak untuk mencegah kerosakan. Undersea inspections and surveys are important requirements for offshore industry and mining organisation for various infra-structures installations. During the last decade, the use of underwater structure installations, such as oil or gas pipeline and telecommunication cables has increased many folds

Intelligent tracking of handball players at F.C. Porto and FADEUP

Tese de Mestrado Integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

Automatic Detectors for Underwater Soundscape Measurements

Environmental impact regulations require that marine industrial operators quantify their contribution to underwater noise scenes. Automation of such assessments becomes feasible with the successful categorisation of sounds into broader classes based on source types – biological, anthropogenic and physical. Previous approaches to passive acoustic monitoring have mostly been limited to a few specific sources of interest. In this study, source-independent signal detectors are developed and a framework is presented for the automatic categorisation of underwater sounds into the aforementioned classes

Framework for extracting and solving combination puzzles

Selles töös uuritakse, kuidas arvuti nägemisega seotud algoritme on võimalik rakendada objektide tuvastuse probleemile. Täpsemalt, kas arvuti nägemist on võimalik kasutada päris maailma kombinatoorsete probleemide lahendamiseks. Idee kasutada arvuti rakendust probleemide lahendamiseks, tulenes tähelepanekust, et probleemide lahenduse protsessid on kõik enamasti algoritmid. Sellest võib järeldada, et arvutid sobivad algoritmiliste probleemide lahendamiseks paremini kui inimesed, kellel võib sama ülesande peale kuluda kordades kauem. Siiski ei vaatle arvutid probleeme samamoodi nagu inimesed ehk nad ei saa probleeme analüüsida. Niisiis selle töö panuseks saab olema erinevate arvuti nägemise algoritmide uurimine, mille eesmärgiks on päris maailma kombinatoorsete probleemide tõlgendamine abstraktseteks struktuurideks, mida arvuti on võimeline mõistma ning lahendama.Praegu on antud valdkonnas vähe materiali, mis annab hea võimaluse panustada sellesse valdkonda. Seda saavutatakse läbi empiirilise uurimise testide kogumiku kujul selleks, et veenduda millised lähenemised on kõige paremad. Nende eesmärkide saavutamiseks töötati läbi suur hulk arvuti nägemisega seotud materjale ning teooriat. Lisaks võeti ka arvesse reaalaja toimingute tähtsus, mida võib näha erinevate liikumisest struktuuri eraldavate algoritmide(SLAM, PTAM) õpingutest, mida hiljem edukalt kasutati navigatsiooni ja liitreaalsuse probleemide lahendamiseks. Siiski tuleb mainida, et neid algoritme ei kasutatud objektide omaduste tuvastamiseks.See töö uurib, kuidas saab erinevaid lähenemisi kasutada selleks, et aidata vähekogenud kasutajaid kombinatoorsete päris maailma probleemide lahendamisel. Lisaks tekib selle töö tulemusena võimalus tuvastada objektide liikumist (translatsioon, pöörlemine), mida saab kasutada koos virutaalse probleemi mudeliga, et parandada kasutaja kogemust.This thesis describes and investigates how computer vision algorithms and stereo vision algorithms may be applied to the problem of object detection. In particular, if computer vision can aid on puzzle solving. The idea to use computer application for puzzle solving came from the fact that all solution techniques are algorithms in the end. This fact leads to the conclusion that algorithms are well solved by machines, for instance, a machine requires milliseconds to compute the solution while a human can handle this in minutes or hours. Unfortunately, machines cannot see puzzles from human perspective thus cannot analyze them. Hence, the contribution of this thesis is to study different computer vision approaches from non-related solutions applied to the problem of translating the physical puzzle model into the abstract structure that can be understood and solved by a machine.Currently, there is a little written on this subject, therefore, there is a great chance to contribute. This is achieved through empirical research represented as a set of experiments in order to ensure which approaches are suitable. To accomplish these goals huge amount of computer vision theory has been studied. In addition, the relevance of real-time operations was taken into account. This was manifested through the Different real-time Structure from Motion algorithms (SLAM, PTAM) studies that were successfully applied for navigation or augmented reality problems; however, none of them for object characteristics extraction.This thesis examines how these different approaches can be applied to the given problem to help inexperienced users solve the combination puzzles. Moreover, it produces a side effect which is a possibility to track objects movement (rotation, translation) that can be used for manipulating a rendered game puzzle and increase interactivity and engagement of the user

Statistical and image processing techniques for remote sensing in agricultural monitoring and mapping

Throughout most of history, increasing agricultural production has been largely driven by expanded land use, and – especially in the 19th and 20th century – by technological innovation in breeding, genetics and agrochemistry as well as intensification through mechanization and industrialization. More recently, information technology, digitalization and automation have started to play a more significant role in achieving higher productivity with lower environmental impact and reduced use of resources. This includes two trends on opposite scales: precision farming applying detailed observations on sub-field level to support local management, and large-scale agricultural monitoring observing regional patterns in plant health and crop productivity to help manage macroeconomic and environmental trends. In both contexts, remote sensing imagery plays a crucial role that is growing due to decreasing costs and increasing accessibility of both data and means of processing and analysis. The large archives of free imagery with global coverage, can be expected to further increase adoption of remote sensing techniques in coming years. This thesis addresses multiple aspects of remote sensing in agriculture by presenting new techniques in three distinct research topics: (1) remote sensing data assimilation in dynamic crop models; (2) agricultural field boundary detection from remote sensing observations; and (3) contour extraction and field polygon creation from remote sensing imagery. These key objectives are achieved through combining methods of probability analysis, uncertainty quantification, evolutionary learning and swarm intelligence, graph theory, image processing, deep learning and feature extraction. Four new techniques have been developed. Firstly, a new data assimilation technique based on statistical distance metrics and probability distribution analysis to achieve a flexible representation of model- and measurement-related uncertainties. Secondly, a method for detecting boundaries of agricultural fields based on remote sensing observations designed to only rely on image-based information in multi-temporal imagery. Thirdly, an improved boundary detection approach based on deep learning techniques and a variety of image features. Fourthly, a new active contours method called Graph-based Growing Contours (GGC) that allows automatized extractionof complex boundary networks from imagery. The new approaches are tested and evaluated on multiple study areas in the states of Schleswig-Holstein, Niedersachsen and Sachsen-Anhalt, Germany, based on combine harvester measurements, cadastral data and manual mappings. All methods were designed with flexibility and applicability in mind. They proved to perform similarly or better than other existing methods and showed potential for large-scale application and their synergetic use. Thanks to low data requirements and flexible use of inputs, their application is neither constrained to the specific applications presented here nor the use of a specific type of sensor or imagery. This flexibility, in theory, enables their use even outside of the field of remote sensing.Landwirtschaftliche Produktivitätssteigerung wurde historisch hauptsächlich durch Erschließung neuer Anbauflächen und später, insbesondere im 19. und 20. Jahrhundert, durch technologische Innovation in Züchtung, Genetik und Agrarchemie sowie Intensivierung in Form von Mechanisierung und Industrialisierung erreicht. In jüngerer Vergangenheit spielen jedoch Informationstechnologie, Digitalisierung und Automatisierung zunehmend eine größere Rolle, um die Produktivität bei reduziertem Umwelteinfluss und Ressourcennutzung weiter zu steigern. Daraus folgen zwei entgegengesetzte Trends: Zum einen Precision Farming, das mithilfe von Detailbeobachtungen die lokale Feldarbeit unterstützt, und zum anderen großskalige landwirtschaftliche Beobachtung von Bestands- und Ertragsmustern zur Analyse makroökonomischer und ökologischer Trends. In beiden Fällen spielen Fernerkundungsdaten eine entscheidende Rolle und gewinnen dank sinkender Kosten und zunehmender Verfügbarkeit, sowohl der Daten als auch der Möglichkeiten zu ihrer Verarbeitung und Analyse, weiter an Bedeutung. Die Verfügbarkeit großer, freier Archive von globaler Abdeckung werden in den kommenden Jahren voraussichtlich zu einer zunehmenden Verwendung führen. Diese Dissertation behandelt mehrere Aspekte der Fernerkundungsanwendung in der Landwirtschaft und präsentiert neue Methoden zu drei Themenbereichen: (1) Assimilation von Fernerkundungsdaten in dynamischen Agrarmodellen; (2) Erkennung von landwirtschaftlichen Feldgrenzen auf Basis von Fernerkundungsbeobachtungen; und (3) Konturextraktion und Erstellung von Polygonen aus Fernerkundungsaufnahmen. Zur Bearbeitung dieser Zielsetzungen werden verschiedene Techniken aus der Wahrscheinlichkeitsanalyse, Unsicherheitsquantifizierung, dem evolutionären Lernen und der Schwarmintelligenz, der Graphentheorie, dem Bereich der Bildverarbeitung, Deep Learning und Feature-Extraktion kombiniert. Es werden vier neue Methoden vorgestellt. Erstens, eine neue Methode zur Datenassimilation basierend auf statistischen Distanzmaßen und Wahrscheinlichkeitsverteilungen zur flexiblen Abbildung von Modell- und Messungenauigkeiten. Zweitens, eine neue Technik zur Erkennung von Feldgrenzen, ausschließlich auf Basis von Bildinformationen aus multi-temporalen Fernerkundungsdaten. Drittens, eine verbesserte Feldgrenzenerkennung basierend auf Deep Learning Methoden und verschiedener Bildmerkmale. Viertens, eine neue Aktive Kontur Methode namens Graph-based Growing Contours (GGC), die es erlaubt, komplexe Netzwerke von Konturen aus Bildern zu extrahieren. Alle neuen Ansätze werden getestet und evaluiert anhand von Mähdreschermessungen, Katasterdaten und manuellen Kartierungen in verschiedenen Testregionen in den Bundesländern Schleswig-Holstein, Niedersachsen und Sachsen-Anhalt. Alle vorgestellten Methoden sind auf Flexibilität und Anwendbarkeit ausgelegt. Im Vergleich zu anderen Methoden zeigten sie vergleichbare oder bessere Ergebnisse und verdeutlichten das Potenzial zur großskaligen Anwendung sowie kombinierter Verwendung. Dank der geringen Anforderungen und der flexiblen Verwendung verschiedener Eingangsdaten ist die Nutzung nicht nur auf die hier beschriebenen Anwendungen oder bestimmte Sensoren und Bilddaten beschränkt. Diese Flexibilität erlaubt theoretisch eine breite Anwendung, auch außerhalb der Fernerkundung