Segmentation of bone structures in 3D CT images based on continuous max- ow optimization

In this paper an algorithm to carry out the automatic segmentation of bone structures in 3D CT images has been implemented. Automatic segmentation of bone structures is of special interest for radiologists and surgeons to analyze bone diseases or to plan some surgical interventions. This task is very complicated as bones usually present intensities overlapping with those of surrounding tissues. This overlapping is mainly due to the composition of bones and to the presence of some diseases such as Osteoarthritis, Osteoporosis, etc. Moreover, segmentation of bone structures is a very time-consuming task due to the 3D essence of the bones. Usually, this segmentation is implemented manually or with algorithms using simple techniques such as thresholding and thus providing bad results. In this paper gray information and 3D statistical information have been combined to be used as input to a continuous max- ow algorithm. Twenty CT images have been tested and di erent coe cients have been computed to assess the performance of our implementation. Dice and Sensitivity values above 0.91 and 0.97 respectively were obtained. A comparison with Level Sets and thresholding techniques has been carried out and our results outperformed them in terms of accuracy.Ministerio de ciencia e innovación TEC2010-21619-C04-02Junta de Andalucía P11-TIC-772

Pedestrian head detection using automatic scale selection for feature detection and statistical edge curvature analysis

In this report we focus on pedestrian head detection and tracking in video sequences. The task is not trivial in real and complex scenarios where the deformation induced by the perspective field requires a multi-scale analy- sis. Multi-scale shape models for the human head are considered to identify the correct size of the region of interest. Anisotropic diffusion is used as a pre-processing step and edge detection is performed using an automatic scale selection process. A non parametric statistical description is given for the edge curvature and detection is performed by means of goodness-of-fit tests. The head detector is used as a validation tool in a correlation-based tracker. The local maxima of the correlation matrix are analyzed. Tracking is performed associating the displacement vector of the target with that local maximum which maximizes the goodness-of-fit with the distribution of the edge curvature of the head

Hierarchical Image Segmentation using The Watershed Algorithim with A Streaming Implementation

We have implemented a graphical user interface (GUI) based semi-automatic hierarchical segmentation scheme, which works in three stages. In the first stage, we process the original image by filtering and threshold the gradient to reduce the level of noise. In the second stage, we compute the watershed segmentation of the image using the rainfalling simulation approach. In the third stage, we apply two region merging schemes, namely implicit region merging and seeded region merging, to the result of the watershed algorithm. Both the region merging schemes are based on the watershed depth of regions and serve to reduce the over segmentation produced by the watershed algorithm. Implicit region merging automatically produces a hierarchy of regions. In seeded region merging, a selected seed region can be grown from the watershed result, producing a hierarchy. A meaningful segmentation can be simply chosen from the hierarchy produced. We have also proposed and tested a streaming algorithm based on the watershed algorithm, which computes the segmentation of an image without iterative processing of adjacent blocks. We have proved that the streaming algorithm produces the same result as the serial watershed algorithm. We have also discussed the extensibility of the streaming algorithm to efficient parallel implementations

2D and 3D surface image processing algorithms and their applications

This doctoral dissertation work aims to develop algorithms for 2D image segmentation application of solar filament disappearance detection, 3D mesh simplification, and 3D image warping in pre-surgery simulation. Filament area detection in solar images is an image segmentation problem. A thresholding and region growing combined method is proposed and applied in this application. Based on the filament area detection results, filament disappearances are reported in real time. The solar images in 1999 are processed with this proposed system and three statistical results of filaments are presented. 3D images can be obtained by passive and active range sensing. An image registration process finds the transformation between each pair of range views. To model an object, a common reference frame in which all views can be transformed must be defined. After the registration, the range views should be integrated into a non-redundant model. Optimization is necessary to obtain a complete 3D model. One single surface representation can better fit to the data. It may be further simplified for rendering, storing and transmitting efficiently, or the representation can be converted to some other formats. This work proposes an efficient algorithm for solving the mesh simplification problem, approximating an arbitrary mesh by a simplified mesh. The algorithm uses Root Mean Square distance error metric to decide the facet curvature. Two vertices of one edge and the surrounding vertices decide the average plane. The simplification results are excellent and the computation speed is fast. The algorithm is compared with six other major simplification algorithms. Image morphing is used for all methods that gradually and continuously deform a source image into a target image, while producing the in-between models. Image warping is a continuous deformation of a: graphical object. A morphing process is usually composed of warping and interpolation. This work develops a direct-manipulation-of-free-form-deformation-based method and application for pre-surgical planning. The developed user interface provides a friendly interactive tool in the plastic surgery. Nose augmentation surgery is presented as an example. Displacement vector and lattices resulting in different resolution are used to obtain various deformation results. During the deformation, the volume change of the model is also considered based on a simplified skin-muscle model

Radar Signal Processing for Interference Mitigation

It is necessary for radars to suppress interferences to near the noise level to achieve the best performance in target detection and measurements. In this dissertation work, innovative signal processing approaches are proposed to effectively mitigate two of the most common types of interferences: jammers and clutter. Two types of radar systems are considered for developing new signal processing algorithms: phased-array radar and multiple-input multiple-output (MIMO) radar. For phased-array radar, an innovative target-clutter feature-based recognition approach termed as Beam-Doppler Image Feature Recognition (BDIFR) is proposed to detect moving targets in inhomogeneous clutter. Moreover, a new ground moving target detection algorithm is proposed for airborne radar. The essence of this algorithm is to compensate for the ground clutter Doppler shift caused by the moving platform and then to cancel the Doppler-compensated clutter using MTI filters that are commonly used in ground-based radar systems. Without the need of clutter estimation, the new algorithms outperform the conventional Space-Time Adaptive Processing (STAP) algorithm in ground moving target detection in inhomogeneous clutter. For MIMO radar, a time-efficient reduced-dimensional clutter suppression algorithm termed as Reduced-dimension Space-time Adaptive Processing (RSTAP) is proposed to minimize the number of the training samples required for clutter estimation. To deal with highly heterogeneous clutter more effectively, we also proposed a robust deterministic STAP algorithm operating on snapshot-to-snapshot basis. For cancelling jammers in the radar mainlobe direction, an innovative jamming elimination approach is proposed based on coherent MIMO radar adaptive beamforming. When combined with mutual information (MI) based cognitive radar transmit waveform design, this new approach can be used to enable spectrum sharing effectively between radar and wireless communication systems. The proposed interference mitigation approaches are validated by carrying out simulations for typical radar operation scenarios. The advantages of the proposed interference mitigation methods over the existing signal processing techniques are demonstrated both analytically and empirically

CT-gestützte Evaluation der Trachea beim Zwergkaninchen.: Eine Grundlagenstudie zur Erstellung evidenz-basierter Intubationsempfehlungen

Einleitung. Die Intubation bei Kaninchen kleiner Rassen wird stets als technisch an- spruchsvoll beschrieben. Außerdem neigt das Kaninchen in besonderer Weise zu intubations-assoziierten Komplikationen. Die Auswahl geeigneter Endotrachealtuben erfolgt bisher empirisch und Studien zu den Größenverhältnissen adulter Tiere mit einer Körpermasse unter 2 kg lagen nicht vor. Ziele der Untersuchung. Ziel der vorliegenden Untersuchung war, morphometrische Daten über die anatomischen Verhältnisse der Atemwege des Zwergkaninchens zu ermitteln, die als Basis für Empfehlungen zur Intubation dienen können. Material und Methoden. Die vorliegende Studie untersuchte 35 Tiere mit Körper- massen zwischen 0,61 - 2,15 kg. Die Tiere wurden in Allgemeinanästhesie mit Isoflu- ran nach Einleitung mit Propofol untersucht. Es wurden Computertomographien des Hals- und Thoraxbereiches in Brustbauch- oder Rückenlage mit einer Schichtdicke von 0,7 mm angefertigt. Diese wurden mit einer automatisierten Auswertungssoft- ware zur Rekonstruktion und Analyse von Atemwegen vermessen. Es wurden Ver- gleichsmessungen der Trachealdurchmesser und -querschnittsflächen an Gefrier- schnitten anatomischer Präparate von fünf Tieren durchgeführt. Die Messpunkte wurden durch die Übergänge der Halswirbel definiert.Ergebnisse. Die Gültigkeit der vorgenommen Messungen wurde durch eine Bland- Altmann-Analyse der Ergebnisse aus anatomischen Präparaten und Computertomo- graphie bestätigt. Die Messpunkte zeigten Mittelwerte zwischen 2,49 – 3,39 mm im minimalen Durchmesser, 3,78 – 4,06 mm im maximalen Durchmesser und eine Trachealfläche zwischen 7,04 – 11,09 mm2. Im Bereich des Kehlkopfes waren teil- weise keine auswertbaren Messungen möglich. An den übrigen Messpunkten zeig- ten sich signifikante Korrelationen (Pearson-Korrelationskoeffizient) zur Körpermasse des minimalen Durchmesser zwischen 0,44 – 0,67, des maximalen Durchmessers von 0,6 – 0,68 und zur Trachealfläche zwischen 0,47 – 0,72. Zur Scheitelsteißlänge lagen die Korrelationen zum minimalen Durchmesser zwischen 0,49 – 0,68, zum maximalen Durchmesser bei 0,47 – 0,69 und zur Trachealquerschnittsfläche zwi- schen 0,58 – 0,65. Auf Grund dieser Korrelation wurde eine Diskriminanzanalyse durchgeführt und eine Formel zur Vorhersage der Endotrachealtubusgröße erstellt. Diese sagt mit einer Genauigkeit von 67,6% die passende Tubusgröße voraus. Ge- schlecht und Alter hatten keinen Einfluss auf die Größe der Trachea und spielen da- mit keine Rolle bei der Wahl des Tubus. Schlussfolgerung. Die vorliegende Studie zeigt, dass die Computertomographie auch bei sehr kleinen Tieren gut für die Evaluation der Atemwege geeignet ist. Die Trachealdimensionen zeigen eine signifikante Korrelation zur Körpermasse und zur Scheitelsteißlänge. Beide Parameter sind damit geeignete Marker zur Auswahl eines Endotrachealtubus beim Kaninchen und mit Hilfe der hier gefundenen Daten ist eine evidenzbasierte Intubationsempfehlung für Zwergkaninchen möglich.:INHALTSVERZEICHNIS 1. 2. EINLEITUNG UND FRAGESTELLUNG ............................................................... 1 LITERATURÜBERSICHT...................................................................................... 3 2.1 Anatomie des Atmungstraktes..................................................................................... 3 2.1.1 Anatomie der Kehlkopfes ..................................................................................... 3 2.1.2 Anatomie der Trachea .......................................................................................... 3 2.1.3 Einflussfaktoren auf Größenverhältnisse des Atmungstraktes ............................. 4 2.1.3.1 Alter.................................................................................................................4 2.1.3.2 Geschlecht......................................................................................................4 2.1.3.3 Ventilation.......................................................................................................5 2.1.3.4 Anästhesie......................................................................................................5 2.2 Intraoperative Atemwegssicherung beim Kaninchen .................................................. 6 2.2.1 Endotracheale Intubation...................................................................................... 7 2.2.1.1 BlindeorotrachealeIntubation........................................................................7 2.2.1.2 OrotrachealeIntubationunterSicht................................................................8 2.2.1.3 Invasive Techniken zur orotrachealen Intubation .......................................... 8 2.2.1.4 NasotrachealeIntubation................................................................................9 2.2.2 Larynxmasken ...................................................................................................... 9 2.2.2.1 HumaneLanrynxmasken..............................................................................10 2.2.2.2 VeterinärmedizinischeLarynxmasken..........................................................11 2.2.3 Vor- und Nachteile der Intubation ...................................................................... 12 2.2.4 Intubations-assoziierte Komplikationen .............................................................. 13 2.3 Methoden zur Evaluation der Größenverhältnisse im Atmungstrakt ......................... 14 2.3.1 In vivo ................................................................................................................. 14 2.3.1.1 Endoskopie...................................................................................................15 2.3.1.2 Projektionsradiographie................................................................................15 2.3.1.3 Ultraschall.....................................................................................................16 2.3.1.4 Magnetresonanztomographie.......................................................................17 2.3.1.5. Computertomographie ................................................................................. 18 2.3.2 Ex vivo ................................................................................................................ 20 2.3.2.1 Präparation...................................................................................................21 2.3.2.2 Korrosionspräparate.....................................................................................21 2.4 Evaluation der Trachealgröße und Relation zu anatomischen Bezugspunkten bei verschiedenen Tierarten .................................................................................................... 22 2.4.1 Mensch ............................................................................................................... 22 2.4.2 Hund ................................................................................................................... 23 I 3. 2.4.3 Katze................................................................................................................... 23 2.4.4 Kaninchen........................................................................................................... 24 2.4.5 Andere Tierarten................................................................................................. 25 2.5 Wirkungen der in der Studie eingesetzten Anästhetika............................................. 25 2.5.1 Propofol .............................................................................................................. 25 2.5.2 Isofluran .............................................................................................................. 26 PATIENTEN, MATERIAL UND METHODEN...................................................... 27 3.1 Patientenauswahl ...................................................................................................... 27 3.2 Ablauf ........................................................................................................................ 27 3.3 CT-Untersuchung ...................................................................................................... 29 3.4 Anfertigung von Gefrierschnitten ............................................................................... 32 3.5 Analysesoftware ........................................................................................................ 32 3.5.1 Analyse der CT-Bilder ........................................................................................ 32 3.5.1.1 FestlegungderMesspunkte..........................................................................33 3.5.1.2 DurchführungenderMessungenanMesspunkten.......................................34 3.5.1.3 MessungderTracheallänge.........................................................................34 3.5.1.4 BestimmungderTrachealform......................................................................35 3.5.2 Vermessen der Gefrierpräparate ........................................................................ 37 3.6 Statistik ...................................................................................................................... 38 ERGEBNISSE ..................................................................................................... 39 4.1 Tiere .......................................................................................................................... 39 4.2 Übereinstimmung von Gefrierschnitten und Computertomographie ......................... 40 4.3 Einfluss der Lagerung auf die Messungen ................................................................ 44 4.4 Trachealdurchmesser ................................................................................................ 44 4.5 Trachealfläche ........................................................................................................... 47 4.6 Trachealform ............................................................................................................. 48 4.7 Tracheallänge ............................................................................................................ 49 4.8 Korrelationen ............................................................................................................. 50 4.8.1 Korrelation zur Körpermasse .............................................................................. 50 4.8.2 Korrelation zur Scheitelsteißlänge ...................................................................... 57 4.8.3 Korrelation zum Alter .......................................................................................... 66 4.8.4 Korrelation zum Geschlecht ............................................................................... 66 4.9 Diskriminanzanalyse zur Auswahl eines Endotrachealtubus .................................... 66 DISKUSSION....................................................................................................... 69 5.1 Diskussion der Methodik ........................................................................................... 69 5.1.1 Patientenauswahl ............................................................................................... 69 4. 5. II 6. 7. 8. 9. 5.1.2 Anästhesieverfahren........................................................................................... 72 5.1.3 Computertomographie ........................................................................................ 74 5.1.4 Anfertigung von Gefrierschnitten ........................................................................ 76 5.2 Diskussion der Ergebnisse ........................................................................................ 77 5.2.1 Vergleich der gefundenen Trachealdimensionen mit vorhandenen Studien ...... 78 5.2.2 Korrelation Trachealdimensionen mit morphometrischen Markerpunkten ......... 78 5.2.3 Trachealform....................................................................................................... 79 5.2.4 Einfluss der Atemdynamik auf die Messung im Bereich des Kehlkopfes ........... 80 5.2.5 Geschlechtsdimorphismus im Bereich der Trachea ........................................... 81 5.2.6 Vergleich der Vorhersagegenauigkeit mit anderen Formeln zur Tubusauswahl 81 5.3 Klinische Schlussfolgerung........................................................................................ 82 5.4 Praxisrelevanz ........................................................................................................... 83 ZUSAMMENFASSUNG....................................................................................... 84 SUMMARY .......................................................................................................... 86 LITERATURVERZEICHNIS ................................................................................ 88 ANHANG ........................................................................................................... 103 9.1 Abbildungsverzeichnis ............................................................................................... 103 9.2 Tabellenverzeichnis ................................................................................................... 105 DANKSAGUNG ...................................................................................................... 10

Multiple Object Tracking with Occlusion Handling

Object tracking is an important problem with wide ranging applications. The purpose is to detect object contours and track their motion in a video. Issues of concern are to be able to map objects correctly between two frames, and to be able to track through occlusion. This thesis discusses a novel framework for the purpose of object tracking which is inspired from image registration and segmentation models. Occlusion of objects is also detected and handled in this framework in an appropriate manner. The main idea of our tracking framework is to reconstruct the sequence of images in the video. The process involves deforming all the objects in a given image frame, called the initial frame. Regularization terms are used to govern the deformation of the shape of the objects. We use elastic and viscous fluid model as the regularizer. The reconstructed frame is formed by combining the deformed objects with respect to the depth ordering. The correct reconstruction is selected by parameters that minimize the difference between the reconstruction and the consecutive frame, called the target frame. These parameters provide the required tracking information, such as the contour of the objects in the target frame including the occluded regions. The regularization term restricts the deformation of the object shape in the occluded region and thus gives an estimate of the object shape in this region. The other idea is to use a segmentation model as a measure in place of the frame difference measure. This is separate from image segmentation procedure, since we use the segmentation model in a tracking framework to capture object deformation. Numerical examples are presented to demonstrate tracking in simple and complex scenes, alongwith occlusion handling capability of our model. Segmentation measure is shown to be more robust with regard to accumulation of tracking error

Estado de conservación de la Puya raimondii Harms mediante técnicas de teledetección y modelos Deep Learning en el área de conservación regional bosque de Puya Raimondi - Titankayocc, Ayacucho

Los estudios de la Puya raimondii Harms en el Perú son escasos, pese a su valor ecológico y económico para los ecosistemas altoandinos. Actualmente, su situación es grave debido a las amenazas climáticas y antropogénicas que afectan en el crecimiento poblacional de la especie. Consecuencia de ello, la P. raimondii se encuentra declarada en peligro de extinción, ya que presenta poca variabilidad genética para soportar dichos cambios; además, produce una sola inflorescencia al final de su periodo vegetativo. De manera que, el objetivo general de esta tesis es estudiar y evaluar el estado de conservación de la P. raimondii a través de la teledetección y el uso de nuevas técnicas de detección de objetos como son los algoritmos de Deep Learning aplicado en un área representativa de puyas como es el Área de Conservación Regional Bosque de Puya Raimondi - Titankayocc, departamento de Ayacucho. La metodología implica el uso de herramientas de Sistemas de Información Geográfica y análisis espacial basado en la geoestadística para estimar el número de individuos a través de imágenes satelitales de Google Earth; posteriormente, calcular los valores de las variables ambientales como el Índice de Vegetación de Diferencia Normalizada (NDVI) y el Índice de Rugosidad del Terreno (TRI) provenientes de satélites de alta resolución, CBERS-4A y SRTM respectivamente; finalmente, discretizar la información hallada para caracterizar el hábitat de la P. raimondii dentro del área de conservación. En ese sentido, los resultados alcanzados concluyeron en la detección de 58 607 individuos usando imágenes Google Earth. Asimismo, la actividad fotosintética registrada tenía como valor promedio un 0.23 según el NDVI; de igual manera, para el caso del TRI se identificaron los hábitats más propicios para la especie los cuales fueron suelos rugosos ligeros a elevados ubicados principalmente en los ejes Este y Sur. Dicho esto, la propuesta de nuevas estrategias para el estudio de conservación implicó abordar los conceptos relacionados a la ecología vegetal, análisis espacial e inteligencia artificial.Studies on Puya raimondii Harms in Peru are scarce, despite its ecological and economic value for high Andean ecosystems. Currently, its situation is serious due to climate and anthropogenic threats that affect the population growth of the species. As a result, P. raimondii has been declared in danger of extinction since it has little genetic variability to withstand such changes; in addition, it produces only one inflorescence at the end of its vegetative period. Therefore, the general objective of this thesis is to study and evaluate the conservation status of P. raimondii through remote sensing and the use of new object detection techniques such as Deep Learning algorithms applied in a representative area of puyas, namely the Regional Conservation Area of Puya Raimondi Forest - Titankayocc, department of Ayacucho. The methodology involves the use of Geographic Information Systems tools and spatial analysis based on geostatistics to estimate the number of individuals through Google Earth satellite images; subsequently, calculate the values of environmental variables such as the Normalized Difference Vegetation Index (NDVI) and the Terrain Roughness Index (TRI) from high-resolution satellites, CBERS-4A and SRTM respectively; finally, discretize the information found to characterize the habitat of P. raimondii within the conservation area. In this sense, the results achieved concluded in the detection of 58,607 individuals using Google Earth images. Likewise, the registered photosynthetic activity had an average value of 0.23 according to the NDVI; similarly, in the case of the TRI, the most favorable habitats for the species were identified, which were light rugged soils to elevated ones located mainly in the eastern and southern axes. That said, the proposal of new strategies for conservation study implied addressing concepts related to plant ecology, spatial analysis and artificial intelligence