Junctions are the intersection points of three or more intensity surfaces in an image. An analysis of zero crossings and the gradient near junctions demonstrates that gradient-based edge detection schemes fragment edges at junctions. This fragmentation is caused by the intrinsic pairing of zero crossings and a destructive interference of edge gradients at junctions. Using the previous gradient analysis, we propose a junction detector that finds junctions in edge maps by following gradient ridges and using the minimum direction of saddle points in the gradient. The junction detector is demonstrated on real imagery and previous approaches to junction detection are discussed.

Angiography is an invasive procedure since contrast medium is injected into circulatory system of patients and the mostly preferred technique is X-ray angiography. For diagnosis, treatment planning, and risk assessment purposes, interventional radiologists utilize visual inspection to determine connectivity relations between vessels. This situation leads angiography to be more invasive, since it requires additional injection of contrast medium and X-ray dose. This thesis work presents a 3-D vascular connectivity tracking toolkit for automated extraction of vascular networks in 3-D medical images. The proposed method automatically extracts the vascular network connected to a user-defined point in a user-defined direction, and requires no further user interaction. The toolkit prevents additional injection of contrast agent and X-ray dose, saves time for the interventional radiologist. While the algorithm is applicable on all 3-D angiography images, performance of the method is observed on 3-D catheter angiography image of cerebrovascular structures. The algorithm iteratively tracks gravity centers of vascular branches in the user-defined direction, preserving connection to the user-defined point. Curvy branches are tracked even if they have discontinuous portions. Since this tracking method does not depend on lumen diameter and intensity differences, branches with stenoses and branches having large intensity difference can be successfully tracked. Skeletonization and junction detection methods are described, which are used to detect the sub branches, indirectly connected to the point. These methods are capable of handling bifurcations, trifurcations, and junctions having more branches. However, false junctions occurring due to superposition of vessels are not eliminated.

Les caractéristiques locales sont essentielles dans de nombreux domaines de l’analyse d’images comme la détection et la reconnaissance d’objets, la recherche d’images, etc. Ces dernières années, plusieurs détecteurs dits locaux ont été proposés pour extraire de telles caractéristiques. Ces détecteurs locaux fonctionnent généralement bien pour certaines applications, mais pas pour toutes. Prenons, par exemple, une application de recherche dans une large base d’images. Dans ce cas, un détecteur à base de caractéristiques binaires pourrait être préféré à un autre exploitant des valeurs réelles. En effet, la précision des résultats de recherche pourrait être moins bonne tout en restant raisonnable, mais probablement avec un temps de réponse beaucoup plus court. En général, les détecteurs locaux sont utilisés en combinaison avec une méthode d’indexation. En effet, une méthode d’indexation devient nécessaire dans le cas où les ensembles de points traités sont composés de milliards de points, où chaque point est représenté par un vecteur de caractéristiques de grande dimension. / Local features are of central importance to deal with many different problems in image analysis and understanding including image registration, object detection and recognition, image retrieval, etc. Over the years, many local detectors have been presented to detect such features. Such a local detector usually works well for some particular applications but not all. Taking an application of image retrieval in large database as an example, an efficient method for detecting binary features should be preferred to other real-valued feature detection methods. The reason is easily seen: it is expected to have a reasonable precision of retrieval results but the time response must be as fast as possible. Generally, local features are used in combination with an indexing scheme. This is highly needed for the case where the dataset is composed of billions of data points, each of which is in a high-dimensional feature vector space.