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Segmentation of anatomical structures in medical images is often based on a voxel/pixel classification approach. Deep learning systems, such as convolutional neural networks (CNNs), can infer a hierarchical representation of images that fosters categorization. We propose a novel system for voxel classification integrating three 2D CNNs, which have a(More)
According to Marr's paradigm of computational vision the first process is an extraction of relevant features. The goal of this paper is to quantify and characterize the information carried by features using image-structure measured at feature-points to reconstruct images. In this way, we indirectly evaluate the concept of feature-based image analysis. The(More)
Manifolds are widely used to model non-linearity arising in a range of computer vision applications. This paper treats statistics on manifolds and the loss of accuracy occurring when linearizing the mani-fold prior to performing statistical operations. Using recent advances in manifold computations, we present a comparison between the non-linear analog of(More)
The importance of manifolds and Riemannian geometry is spreading to applied fields in which the need to model non-linear structure has spurred widespread interest in geometry. The transfer of interest has created demand for methods for computing classical constructs of geometry on manifolds occurring in practical applications. This paper develops initial(More)
This study presents a fully automatic, data-driven approach for texture-based quantitative analysis of chronic obstructive pulmonary disease (COPD) in pulmonary computed tomography (CT) images. The approach uses supervised learning where the class labels are, in contrast to previous work, based on measured lung function instead of on manually annotated(More)
The purpose of this report 1 is to deene optic ow for scalar and density images without using a priori knowledge other than its deening conservation principle, and to incorporate measurement duality, notably the scale-space paradigm. It is argued that the design of optic ow based applications may beneet from a manifest separation between factual image(More)
OBJECTIVE We investigated whether breast cancer is predicted by a breast cancer risk mammographic texture resemblance (MTR) marker. METHODS A previously published case-control study included 495 women of which 245 were diagnosed with breast cancer. In baseline mammograms, 2-4 years prior to diagnosis, the following mammographic parameters were analysed(More)