Effective Volumetric Feature Modeling and Coarse Correspondence via Improved 3DSIFT and Spectral Matching

This paper presents a nonrigid coarse correspondence computation algorithm for volumetric images. Our matching algorithm first extracts then correlates image features based on a revised and improved 3DSIFT (I3DSIFT) algorithm. With a scale-related keypoint reorientation and descriptor construction, this feature correlation is less sensitive to image rotation and scaling. Then, we present an improved spectral matching (ISM) algorithm on correlated features to obtain a one-to-one mapping between corresponded features. One can effectively extend this feature correspondence to dense correspondence between volume images. Our algorithm can benefit nonrigid volumetric image registration in many tasks such as motion modeling in medical image analysis and processing

Automatic 3D segmentation of the prostate on magnetic resonance images for radiotherapy planning

Abstract. Accurate segmentation of the prostate, the seminal vesicles, the bladder and the rectum is a crucial step for planning radiotherapy (RT) procedures. Modern radiotherapy protocols have included the delineation of the pelvic organs in magnetic resonance images (MRI), as the guide to the therapeutic beam irradiation over the target organ. However, this task is highly inter and intra-expert variable and may take about 20 minutes per patient, even for trained experts, constituting an important burden in most radiological services. Automatic or semi-automatic segmentation strategies might then improve the efficiency by decreasing the measured times while conserving the required accuracy. This thesis presents a fully automatic prostate segmentation framework that selects the most similar prostates w.r.t. a test prostate image and combines them to estimate the segmentation for the test prostate. A robust multi-scale analysis establishes the set of most similar prostates from a database, independently of the acquisition protocol. Those prostates are then non-rigidly registered towards the test image and fusioned by a linear combination. The proposed approach was evaluated using a MRI public dataset of patients with benign hyperplasia or cancer, following different acquisition protocols, namely 26 endorectal and 24 external. Evaluating under a leave-one-out scheme, results show reliable segmentations, obtaining an average dice coefficient of 79%, when comparing with the expert manual segmentation.La delineación exacta de la próstata, las vesículas seminales, la vejiga y el recto es un paso fundamental para el planeamiento de procedimientos de radioterapia. Protocolos modernos han incluido la delineación de los órganos pélvicos en imágenes de resonancia magnética (IRM), como la guia para la irradiación del haz terapéutico sobre el órgano objetivo. Sin embargo, esta tarea es altamente variable intra e inter-experto y puede tomar al rededor de 20 minutos por paciente, incluso para expertos entrenados, convirtiéndose en una carga importante en la mayoría de los servicios de radiología. Métodos automáticos o semi-automáticos podrían mejorar la eficiencia disminuyendo los tiempos medidos mientras se conserva la precisión requerida. Este trabajo presenta una estrategia de segmentación de la próstata completamente automático que selecciona las prostatas más similares con respecto a una imagen de resonancia magnética de prueba y combina las delineaciones asociadas a dichas imágenes para estimar la segmentación de la imagen de prueba. Un análisis multiescala robusto permite establecer el conjunto de las próstatas más parecidas de una base de datos, independiente del protocolo de adquisición. Las imágenes seleccionadas son registradas de forma no rigida con respecto a la imagen de prueba y luego son fusionadas mediante una combinación lineal. El enfoque propuesto fue evaluado utilizando un conjunto público de imágenes de resonancia magnética de pacientes con hiperplasia benigna o con cancer, con diferentes protocolos de adquisición, esto es 26 externas y 24 endorectales. Este trabajo fue evaluado bajo un esquema leave-one-out, cuyos resultados mostraron segmentaciones confiables, obteniendo un DSC promedio de 79%, cuando se compararon los resultados obtenidos con las segmentaciones manuales de expertos.Maestrí

Digital Image Processing Applications

Digital image processing can refer to a wide variety of techniques, concepts, and applications of different types of processing for different purposes. This book provides examples of digital image processing applications and presents recent research on processing concepts and techniques. Chapters cover such topics as image processing in medical physics, binarization, video processing, and more

Study of Computational Image Matching Techniques: Improving Our View of Biomedical Image Data

Image matching techniques are proven to be necessary in various fields of science and engineering, with many new methods and applications introduced over the years. In this PhD thesis, several computational image matching methods are introduced and investigated for improving the analysis of various biomedical image data. These improvements include the use of matching techniques for enhancing visualization of cross-sectional imaging modalities such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), denoising of retinal Optical Coherence Tomography (OCT), and high quality 3D reconstruction of surfaces from Scanning Electron Microscope (SEM) images. This work greatly improves the process of data interpretation of image data with far reaching consequences for basic sciences research. The thesis starts with a general notion of the problem of image matching followed by an overview of the topics covered in the thesis. This is followed by introduction and investigation of several applications of image matching/registration in biomdecial image processing: a) registration-based slice interpolation, b) fast mesh-based deformable image registration and c) use of simultaneous rigid registration and Robust Principal Component Analysis (RPCA) for speckle noise reduction of retinal OCT images. Moving towards a different notion of image matching/correspondence, the problem of view synthesis and 3D reconstruction, with a focus on 3D reconstruction of microscopic samples from 2D images captured by SEM, is considered next. Starting from sparse feature-based matching techniques, an extensive analysis is provided for using several well-known feature detector/descriptor techniques, namely ORB, BRIEF, SURF and SIFT, for the problem of multi-view 3D reconstruction. This chapter contains qualitative and quantitative comparisons in order to reveal the shortcomings of the sparse feature-based techniques. This is followed by introduction of a novel framework using sparse-dense matching/correspondence for high quality 3D reconstruction of SEM images. As will be shown, the proposed framework results in better reconstructions when compared with state-of-the-art sparse-feature based techniques. Even though the proposed framework produces satisfactory results, there is room for improvements. These improvements become more necessary when dealing with higher complexity microscopic samples imaged by SEM as well as in cases with large displacements between corresponding points in micrographs. Therefore, based on the proposed framework, a new approach is proposed for high quality 3D reconstruction of microscopic samples. While in case of having simpler microscopic samples the performance of the two proposed techniques are comparable, the new technique results in more truthful reconstruction of highly complex samples. The thesis is concluded with an overview of the thesis and also pointers regarding future directions of the research using both multi-view and photometric techniques for 3D reconstruction of SEM images

Simulation Guidée par l’Image pour la Réalité Augmentée durant la Chirurgie Hépatique

The main objective of this thesis is to provide surgeons with tools for pre and intra-operative decision support during minimally invasive hepaticsurgery. These interventions are usually based on laparoscopic techniques or, more recently, flexible endoscopy. During such operations, the surgeon tries to remove a significant number of liver tumors while preserving the functional role of the liver. This involves defining an optimal hepatectomy, i.e. ensuring that the volume of post-operative liver is at least at 55% of the original liver and the preserving at hepatic vasculature. Although intervention planning can now be considered on the basis of preoperative patient-specific, significant movements of the liver and its deformations during surgery data make this very difficult to use planning in practice. The work proposed in this thesis aims to provide augmented reality tools to be used in intra-operative conditions in order to visualize the position of tumors and hepatic vascular networks at any time.L’objectif principal de cette thèse est de fournir aux chirurgiens des outils d’aide à la décision pré et per-opératoire lors d’interventions minimalement invasives en chirurgie hépatique. Ces interventions reposent en général sur des techniques de laparoscopie ou plus récemment d’endoscopie flexible. Lors de telles interventions, le chirurgien cherche à retirer un nombre souvent important de tumeurs hépatiques, tout en préservant le rôle fonctionnel du foie. Cela implique de définir une hépatectomie optimale, c’est à dire garantissant un volume du foie post-opératoire d’au moins 55% du foie initial et préservant au mieux la vascularisation hépatique. Bien qu’une planification de l’intervention puisse actuellement s’envisager sur la base de données pré-opératoire spécifiques au patient, les mouvements importants du foie et ses déformations lors de l’intervention rendent cette planification très difficile à exploiter en pratique. Les travaux proposés dans cette thèse visent à fournir des outils de réalité augmentée utilisables en conditions per-opératoires et permettant de visualiser à chaque instant la position des tumeurs et réseaux vasculaires hépatiques

Registration of histology and magnetic resonance imaging of the brain

Combining histology and non-invasive imaging has been attracting the attention of the medical imaging community for a long time, due to its potential to correlate macroscopic information with the underlying microscopic properties of tissues. Histology is an invasive procedure that disrupts the spatial arrangement of the tissue components but enables visualisation and characterisation at a cellular level. In contrast, macroscopic imaging allows non-invasive acquisition of volumetric information but does not provide any microscopic details. Through the establishment of spatial correspondences obtained via image registration, it is possible to compare micro- and macroscopic information and to recover the original histological arrangement in three dimensions. In this thesis, I present: (i) a survey of the literature relative to methods for histology reconstruction with and without the help of 3D medical imaging; (ii) a graph-theoretic method for histology volume reconstruction from sets of 2D sections, without external information; (iii) a method for multimodal 2D linear registration between histology and MRI based on partial matching of shape-informative boundaries