Automatic Active Contour Modelling and Its Potential Application for Non-Destructive Testing

Active contouring techniques are very useful in medical imaging, digital mapping, non-destructive ultrasonic evaluation, etc. Therefore, we try to explore and investigate the advanced automatic active contouring methods, which can benefit the aforementioned applications. In this thesis work, we study the automatic active contour models adopted for the object characterization, whose extensions could include the potential defect analysis in the ultrasonic non-destructive testing. The active contouring scheme (also called a snake) or an energy minimizing spline, is an algorithm which is very sensitive to the manually marked initial points, and thereby requires an expertly operation. Therefore, we make new research endeavors to handle this major problem and design a new expert-free snake technique, which can lead to the completely automatic contouring technology for the future applications. Even though this initialization problem has been addressed in the literature for quite a while, to the best of our knowledge, there exists no satisfactory solution so far. In this thesis, we propose a novel initialization algorithm for the automatic snake technique, which can possess a faster convergence than other existing automatic contouring methods and also avoid the human operational error incurred in the conventional snake schemes

General Dynamic Surface Reconstruction: Application to the 3D Segmentation of the Left Ventricle

Aquesta tesi descriu la nostra contribució a la reconstrucció tridimensional de les superfícies interna i externa del ventricle esquerre humà. La reconstrucció és un primer procés dins d'una aplicació global de Realitat Virtual dissenyada com una important eina de diagnòstic per a hospitals. L'aplicació parteix de la reconstrucció de les superfícies i proveeix a l'expert de manipulació interactiva del model en temps real, a més de càlculs de volums i de altres paràmetres d'interès. El procés de recuperació de les superfícies es caracteritza per la seva velocitat de convergència, la suavitat a les malles finals i la precisió respecte de les dades recuperades. Donat que el diagnòstic de patologies cardíaques requereix d'experiència, temps i molt coneixement professional, la simulació és un procés clau que millora la eficiència.Els nostres algorismes i implementacions han estat aplicats a dades sintètiques i reals amb diferències relatives a la quantitat de dades inexistents, casuístiques presents a casos patològics i anormals. Els conjunts de dades inclouen adquisicions d'instants concrets i de cicles cardíacs complets. La bondat del sistema de reconstrucció ha estat avaluada mitjançant paràmetres mèdics per a poder comparar els nostres resultats finals amb aquells derivats a partir de programari típic utilitzat pels professionals de la medicina.A més de l'aplicació directa al diagnòstic mèdic, la nostra metodologia permet reconstruccions de tipus genèric en el camp dels Gràfics 3D per ordinador. Les nostres reconstruccions permeten generar models tridimensionals amb un baix cost en quant a la interacció manual necessària i a la càrrega computacional associada. Altrament, el nostre mètode pot entendre's com un robust algorisme de triangularització que construeix superfícies partint de núvols de punts que poden obtenir-se d'escàners làser o sensors magnètics, per exemple.Esta tesis describe nuestra contribución a la reconstrucción tridimensional de las superficies interna y externa del ventrículo izquierdo humano. La reconstrucción es un primer proceso que forma parte de una aplicación global de Realidad Virtual diseñada como una importante herramienta de diagnóstico para hospitales. La aplicación parte de la reconstrucción de las superficies y provee al experto de manipulación interactiva del modelo en tiempo real, además de cálculos de volúmenes y de otros parámetros de interés. El proceso de recuperación de las superficies se caracteriza por su velocidad de convergencia, la suavidad en las mallas finales y la precisión respecto de los datos recuperados. Dado que el diagnóstico de patologías cardíacas requiere experiencia, tiempo y mucho conocimiento profesional, la simulación es un proceso clave que mejora la eficiencia.Nuestros algoritmos e implementaciones han sido aplicados a datos sintéticos y reales con diferencias en cuanto a la cantidad de datos inexistentes, casuística presente en casos patológicos y anormales. Los conjuntos de datos incluyen adquisiciones de instantes concretos y de ciclos cardíacos completos. La bondad del sistema de reconstrucción ha sido evaluada mediante parámetros médicos para poder comparar nuestros resultados finales con aquellos derivados a partir de programario típico utilizado por los profesionales de la medicina.Además de la aplicación directa al diagnóstico médico, nuestra metodología permite reconstrucciones de tipo genérico en el campo de los Gráficos 3D por ordenador. Nuestras reconstrucciones permiten generar modelos tridimensionales con un bajo coste en cuanto a la interacción manual necesaria y a la carga computacional asociada. Por otra parte, nuestro método puede entenderse como un robusto algoritmo de triangularización que construye superficies a partir de nubes de puntos que pueden obtenerse a partir de escáneres láser o sensores magnéticos, por ejemplo.This thesis describes a contribution to the three-dimensional reconstruction of the internal and external surfaces of the human's left ventricle. The reconstruction is a first process fitting in a complete VR application that will serve as an important diagnosis tool for hospitals. Beginning with the surfaces reconstruction, the application will provide volume and interactive real-time manipulation to the model. We focus on speed, precision and smoothness for the final surfaces. As long as heart diseases diagnosis requires experience, time and professional knowledge, simulation is a key-process that enlarges efficiency.The algorithms and implementations have been applied to both synthetic and real datasets with differences regarding missing data, present in cases where pathologies and abnormalities arise. The datasets include single acquisitions and complete cardiac cycles. The goodness of the reconstructions has been evaluated with medical parameters in order to compare our results with those retrieved by typical software used by physicians.Besides the direct application to medicine diagnosis, our methodology is suitable for generic reconstructions in the field of computer graphics. Our reconstructions can serve for getting 3D models at low cost, in terms of manual interaction and CPU computation overhead. Furthermore, our method is a robust tessellation algorithm that builds surfaces from clouds of points that can be retrieved from laser scanners or magnetic sensors, among other available hardware

Basic Science to Clinical Research: Segmentation of Ultrasound and Modelling in Clinical Informatics

The world of basic science is a world of minutia; it boils down to improving even a fraction of a percent over the baseline standard. It is a domain of peer reviewed fractions of seconds and the world of squeezing every last ounce of efficiency from a processor, a storage medium, or an algorithm. The field of health data is based on extracting knowledge from segments of data that may improve some clinical process or practice guideline to improve the time and quality of care. Clinical informatics and knowledge translation provide this information in order to reveal insights to the world of improving patient treatments, regimens, and overall outcomes. In my world of minutia, or basic science, the movement of blood served an integral role. The novel detection of sound reverberations map out the landscape for my research. I have applied my algorithms to the various anatomical structures of the heart and artery system. This serves as a basis for segmentation, active contouring, and shape priors. The algorithms presented, leverage novel applications in segmentation by using anatomical features of the heart for shape priors and the integration of optical flow models to improve tracking. The presented techniques show improvements over traditional methods in the estimation of left ventricular size and function, along with plaque estimation in the carotid artery. In my clinical world of data understanding, I have endeavoured to decipher trends in Alzheimer’s disease, Sepsis of hospital patients, and the burden of Melanoma using mathematical modelling methods. The use of decision trees, Markov models, and various clustering techniques provide insights into data sets that are otherwise hidden. Finally, I demonstrate how efficient data capture from providers can achieve rapid results and actionable information on patient medical records. This culminated in generating studies on the burden of illness and their associated costs. A selection of published works from my research in the world of basic sciences to clinical informatics has been included in this thesis to detail my transition. This is my journey from one contented realm to a turbulent one

Spring-Charged Particles Model to Improved Shape Recovery:An Application for X-Ray Spinal Segmentation

Deformable models are widely used in medical image segmentation methods, to find not only single but also multiple objects within an image. They have the ability to follow the contours of an object of interest, define the boundary of ROI (Region Of Interest) and improve shape recovery. However, these methods still have limitations in cases of low image quality or clutter. This paper presents a new deformable model, the Spring-Charged Particles Model (SCPM). It simulates the movement of positively charged particles connected by springs, attracted towards the contour of objects of interest which is charged negatively, according to the gradient-magnitude image. Springs prevent the particles from moving away and keep the particles at appropriate distances without reducing their flexibility. SCPM was tested on simple shape images and on frontal X-ray images of scoliosis patients. Artificial noise was added to the simple images to examine the robustness of the method. Several configurations of springs and positively charged-particles were evaluated by determining the best spinal segmentation result. The performance of SCPM was compared to the Charged Fluid Model (CFM), Active Contours, and a convolutional neural network (CNN) with U-Net architecture to measure its ability for determining the curvature of the spinal column from frontal X-Ray images. The results show that SCPM is better at segmenting the spine and determining its curvature, as indicated by the highest Area Score value of 0.837, and the lowest standard deviation value of 0.028

Image based real-time ice load prediction tool for ship and offshore platform in managed ice field

The increased activities in arctic water warrant modelling of ice properties and ice-structure interaction forces to ensure safe operations of ships and offshore platforms. Several established analytical and numerical ice force estimation models can be found in the literature. Recently, researchers have been working on Machine Learning (ML) based, data-driven force predictors trained on experimental data and field measurement. Application of both traditional and ML-based image processing for extracting information from ice floe images has also been reported in recent literature; because extraction of ice features from real-time videos and images can significantly improve ice force prediction. However, there exists room for improvement in those studies. For example, accurate extraction of ice floe information is still challenging because of their complex and varied shapes, colour similarities and reflection of light on them. Besides, real ice floes are often found in groups with overlapped and/or connected boundaries, making detecting even more challenging due to weaker edges in such situations. The development of an efficient coupled model, which will extract information from the ice floe images and train a force predictor based on the extracted dataset, is still an open problem. This research presents two Hybrid force prediction models. Instead of using analytical or numerical approaches, the Hybrid models directly extract floe characteristics from the images and later train ML-based force predictors using those extracted floe parameters. The first model extracted ice features from images using traditional image processing techniques and then used SVM and FFNN to develop two separate force predictors. The improved ice image processing technique used here can extract useful ice properties from a closely connected, unevenly illuminated floe field with various floe sizes and shapes. The second model extracted ice features from images using RCNN and then trained two separate force predictors using SVM and FFNN, similar to the first model. The dataset for training SVM and FFNN force predictors involved variables extracted from the image (floe number, density, sizes, etc.) and variables taken from the experimental analysis results (ship speed, floe thickness, force etc.). The performance of both Hybrid models in terms of image segmentation and force prediction, are analyzed and compared to establish their validity and applicability. Nevertheless, there exists room for further development of the proposed Hybrid models. For example, extend the current models to include more data and investigate other machine learning and deep learning-based network architectures to predict the ice force directly from the image as an input

Geometrically Induced Force Interaction for Three-Dimensional Deformable Models

This work introduces a novel 3D deformable model that is based on a geometrically induced external force field, which can be conveniently generalised to arbitrary dimensions. This external force field is based on hypothesised interactions between the relative geometries of the deformable model and the object boundary. The relative geometrical configurations contribute to a dynamic vector force field that changes accordingly as the deformable model evolves. In addition, we show that by enhancing the geometrical interaction field with a nonlocal edge preserving algorithm, the new model can effectively overcome image noise. We provide a comprehensive comparative study and show that the proposed method achieves significant improvements against existing techniques