True-false lumen segmentation of aortic dissection using multi-scale wavelet analysis and generative-discriminative model matching

Computer aided diagnosis in the medical image domain requires sophisticated probabilistic models to formulate quantitative behavior in image space. In the diagnostic process detailed knowledge of model performance with respect to accuracy, variability, and uncertainty is crucial. This challenge has lead to the fusion of two successful learning schools namely generative and discriminative learning. In this paper, we propose a generative-discriminative learning approach to predict object boundaries in medical image datasets. In our approach, we perform probabilistic model matching of both modeling domains to fuse into the prediction step appearance and structural information of the object of interest while exploiting the strength of both learning paradigms. In particular, we apply our method to the task of true-false lumen segmentation of aortic dissections an acute disease that requires automated quantification for assisted medical diagnosis. We report empirical results for true-false lumen discrimination of aortic dissection segmentation showing superior behavior of the hybrid generative-discriminative approach over their non hybrid generative counterpart

Symmetry Maps of Free-Form Curve Segments Via Wave Propagation

This paper presents an approach for computing the symmetries (skeletons) of an edge map consisting of a collection of curve segments. This approach is a combination of analytic computations in the style of computational geometry and discrete propagations on a grid in the style of the numerical solutions of PDE's. Specifically, waves from each of the initial curve segments are initialized and propagated as a discrete wavefront along discrete directions. In addition, to avoid error built up due to the discrete nature of propagation, shockwaves are detected and explicitly propagated along a secondary dynamic grid. The propagation of shockwaves, integrated with the propagation of the wavefront along discrete directions, leads to an exact simulation of propagation by the Eikonal equation. The resulting symmetries are simply the collection of shockwaves formed in this process which can be manipulated locally, exactly, and efficiently under local changes in an edge map (gap completion, remova..

Perceptual Organization as Object Recognition Divided by Two 1 Background

Perceptual grouping is the organization of visual elements into groups which depict an increased level of regularity. Gestalt psychologists posited that visual elements should be grouped based on certain laws including proximity, good continuation, symmetry, among others, in such a way as to generate “good form”. While these ideas are intuitive and appealing, their implementation as a computational framework has been challenging. A classical approach [12, 3, 9, 6] aims to capture these constraints by quantifying the local interaction of edge elements in a global optimization scheme, using curves as the intermediate-level representation. While the use of curves to embed local interactions among edges has led to significant progress, the results of these schemes also indicate that additional information, both local and global, is needed to disambiguate groupings. In general, the segregation of “figure from ground”, or the segmentation problem, remain unsolved with strong indications that a purely low-level processing solution is not always possible. Object recognition is the retrieval of the most similar category to a given “figure ” from a database of stored models. Two main approaches dominate previous work on recognition: bottom-up approaches advocate segmentation followed by recognition, while top-down approaches utilize low-level features to select models which are then matched against the image. The drawback of a purely bottom up approach is that segmentation is generally not possible, except under controlled environments or for restricted application domains. The drawback of a purely top-down approach is the complexity of the process in the face of numerous combinations of possibilities. The notion that visual organizatio