Automatic Lumbar Vertebrae Segmentation in Fluoroscopic Images via Optimised Concurrent Hough Transform

Low back pain is a very common problem in the industrialised countries and its associated cost is enormous. Diagnosis of the underlying causes can be extremely difficult. Many studies have focused on mechanical disorders of the spine. Digital videofluoroscopy (DVF) was widely used to obtain images for motion studies. This can provide motion sequences of the lumbar spine, but the images obtained often suffer due to noise, exacerbated by the very low radiation dosage. Thus determining vertebrae position within the image sequence presents a considerable challenge. In this paper, we show how our new approach can automatically detect the positions and borders of vertebrae concurrently, relieving many of the problems experienced in other approaches. First, we use phase congruency to relieve difficulty associated with threshold selection in edge detection of the illumination variant DVF images. Then, our new Hough transform approach is applied to determine the moving vertebrae, concurrently. We include optimisation via a genetic algorithm as without it the extraction of moving multiple vertebrae is computationally daunting. Our results show that this new approach can indeed provide extractions of position and rotation which appear to be of sufficient quality to aid therapy and diagnosis of spinal disorders

Tracking Lumbar Vertebrae in Digital Videofluoroscopic Video Automatically

Low back pain becomes one of the significant problem in the industrialized world. Efficient and effective spinal motion analysis is required to understand low back pain and to aid the diagnosis. Videofluoroscopy provides a cost effective way for such analysis. However, common approaches are tedious and time consuming due to the low quality of the images. Physicians have to extract the vertebrae manually in most cases and thus continuous motion analysis is hardly achieved. In this paper, we propose a system which can perform automatic vertebrae segmentation and tracking. Operators need to define exact location of landmarks in the first frame only. The proposed system will continuously learn the texture pattern along the edge and the dynamics of the vertebrae in the remaining frames. The system can estimate the location of the vertebrae based on the learnt texture and dynamics throughout the sequence. Experimental results show that the proposed system can segment vertebrae from videofluoroscopic images automatically and accurately. © Springer-Verlag 2004.postprintThe International Workshop on Medical Imaging and Augmented Reality (MIAR 2004), Beijing, China, 19-20 August 2004. In Lecture Notes in Computer Science, 2004, v. 3150, p. 154-16

Live Wire Segmentation Tool for Osteophyte Detection in Lumbar Spine X-Ray Images

Computer-assisted vertebra segmentation in x-ray images is a challenging problem. Inter-subject variability and the generally poor contrast of digitized radiograph images contribute to the segmentation difficulty. In this paper, a semi-automated live wire approach is investigated for vertebrae segmentation. The live wire approach integrates initially selected user points with dynamic programming to generate a closed vertebra boundary. In order to assess the degree to which vertebra features are conserved using the live wire technique, convex hull-based features to characterize anterior osteophytes in lumbar vertebrae are determined for live wire and manually segmented vertebrae. Anterior osteophyte discrimination was performed over 405 lumbar vertebrae, 204 abnormal vertebrae with anterior osteophytes and 201 normal vertebrae. A leave-one-out standard back propagation neural network was used for vertebrae segmentation. Experimental results show that manual segmentation yielded slightly better discrimination results than the live wire technique

Analyse et amélioration de méthodes de reconstruction 3D de l'aorte à partir d'une séquence d'images tomodensitométriques

Le sujet concerne la reconstruction 3D de l'aorte à partir d'une séquence de coupes issues d'un appareil scanner à rayons X. Nous analysons des méthodes utilisées dans les services d'imagerie médicale, principalement basées sur des techniques de seuillage et de soustraction d'images. Nous proposons ensuite des techniques d'amélioration de ces méthodes en réalisant semi-automatiquement l'extraction d'une seule structure anatomique ciblée. Notre méthode vise à mettre en oeuvre un modèle de contours actifs

Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review

The medical image analysis field has traditionally been focused on the development of organ-, and disease-specific methods. Recently, the interest in the development of more 20 comprehensive computational anatomical models has grown, leading to the creation of multi-organ models. Multi-organ approaches, unlike traditional organ-specific strategies, incorporate inter-organ relations into the model, thus leading to a more accurate representation of the complex human anatomy. Inter-organ relations are not only spatial, but also functional and physiological. Over the years, the strategies 25 proposed to efficiently model multi-organ structures have evolved from the simple global modeling, to more sophisticated approaches such as sequential, hierarchical, or machine learning-based models. In this paper, we present a review of the state of the art on multi-organ analysis and associated computation anatomy methodology. The manuscript follows a methodology-based classification of the different techniques 30 available for the analysis of multi-organs and multi-anatomical structures, from techniques using point distribution models to the most recent deep learning-based approaches. With more than 300 papers included in this review, we reflect on the trends and challenges of the field of computational anatomy, the particularities of each anatomical region, and the potential of multi-organ analysis to increase the impact of 35 medical imaging applications on the future of healthcare.Comment: Paper under revie

Active shape models with focus on overlapping problems applied to plant detection and soil pore analysis

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Reconstruction 3D personnalisée de la colonne vertébrale à partir d'images radiographiques non-calibrées

Les systèmes de reconstruction stéréo-radiographique 3D -- La colonne vertébrale -- La scoliose idiopathique adolescente -- Évolution des systèmes de reconstruction 3D -- Filtres de rehaussement d'images -- Techniques de segmentation -- Les méthodes de calibrage -- Les méthodes de reconstruction 3D -- Problématique, hypothèses, objectifs et méthode générale -- Three-dimensional reconstruction of the scoliotic spine and pelvis from uncalibrated biplanar X-ray images -- A versatile 3D reconstruction system of the spine and pelvis for clinical assessment of spinal deformities -- Simulation experiments -- Clinical validation -- A three-dimensional retrospective analysis of the evolution of spinal instrumentation for the correction of adolescent idiopathic scoliosis -- Auto-calibrage d'un système à rayons-X à partir de primitives de haut niveau -- Segmentation de la colonne vertébrale -- Approche hiérarchique d'auto-calibrage d'un système d'acquisition à rayons-X -- Personalized 3D reconstruction of the scoliotic spine from hybrid statistical and X-ray image-based models -- Validation protocol