|
Jaume Garcia. (2004). Generalized Active Shape Models Applied to Cardiac Function Analysis. Master's thesis, , .
Abstract: Medical imaging is very useful in the assessment and treatment of many diseases. To deal with the great amount of data provided by imaging scanners and extract quantitative information that physicians can interpret, many analysis algorithms have been developed. Any process of analysis always consists of a first step of segmenting some particular structure. In medical imaging, structures are not always well defined and suffer from noise artifacts thus, ordinary segmentation methods are not well suited. The ones that seem to give better results are those based on deformable models. Nevertheless, despite their capability of mixing image features together with smoothness constraints that may compensate for image irregularities, these are naturally local methods, i. e., each node of the active contour evolve taking into account information about its neighbors and some other weak constraints about flexibility and smoothness, but not about the global shape that they should find. Due to the fact that structures to be segmented are the same for all cases but with some inter and intra-patient variation, the incorporation of a priori knowledge about shape in the segmentation method will provide robustness to it. Active Shape Models is an algorithm based on the creation of a shape model called Point Distribution Model. It performs a segmentation using only shapes similar than those previously learned from a training set that capture most of the variation presented by the structure. This algorithm works by updating shape nodes along a normal segment which often can be too restrictive. For this reason we propose a generalization of this algorithm that we call Generalized Active Shape Models and fully integrates the a priori knowledge given by the Point Distribution Model with deformable models or any other appropriate segmentation method. Two different applications to cardiac imaging of this generalized method are developed and promising results are shown.
Keywords: Cardiac Analysis; Deformable Models; Active Contour Models; Active Shape Models; Tagged MRI; HARP; Contrast Echocardiography.
|
|
|
Debora Gil. (2004). Geometric Differential Operators for Shape Modelling (Jordi Saludes i Closa, & Petia Radeva, Eds.). Ph.D. thesis, Ediciones Graficas Rey, Barcelona (Spain).
Abstract: Medical imaging feeds research in many computer vision and image processing fields: image filtering, segmentation, shape recovery, registration, retrieval and pattern matching. Because of their low contrast changes and large variety of artifacts and noise, medical imaging processing techniques relying on an analysis of the geometry of image level sets rather than on intensity values result in more robust treatment. From the starting point of treatment of intravascular images, this PhD thesis ad- dresses the design of differential image operators based on geometric principles for a robust shape modelling and restoration. Among all fields applying shape recovery, we approach filtering and segmentation of image objects. For a successful use in real images, the segmentation process should go through three stages: noise removing, shape modelling and shape recovery. This PhD addresses all three topics, but for the sake of algorithms as automated as possible, techniques for image processing will be designed to satisfy three main principles: a) convergence of the iterative schemes to non-trivial states avoiding image degeneration to a constant image and representing smooth models of the originals; b) smooth asymptotic behav- ior ensuring stabilization of the iterative process; c) fixed parameter values ensuring equal (domain free) performance of the algorithms whatever initial images/shapes. Our geometric approach to the generic equations that model the different processes approached enables defining techniques satisfying all the former requirements. First, we introduce a new curvature-based geometric flow for image filtering achieving a good compromise between noise removing and resemblance to original images. Sec- ond, we describe a new family of diffusion operators that restrict their scope to image level curves and serve to restore smooth closed models from unconnected sets of points. Finally, we design a regularization of snake (distance) maps that ensures its smooth convergence towards any closed shape. Experiments show that performance of the techniques proposed overpasses that of state-of-the-art algorithms.
|
|
|
Manisha Das, Deep Gupta, Petia Radeva, & Ashwini M. Bakde. (2021). Optimized CT-MR neurological image fusion framework using biologically inspired spiking neural model in hybrid ℓ1 - ℓ0 layer decomposition domain. BSPC - Biomedical Signal Processing and Control, 68, 102535.
Abstract: Medical image fusion plays an important role in the clinical diagnosis of several critical neurological diseases by merging complementary information available in multimodal images. In this paper, a novel CT-MR neurological image fusion framework is proposed using an optimized biologically inspired feedforward neural model in two-scale hybrid ℓ1 − ℓ0 decomposition domain using gray wolf optimization to preserve the structural as well as texture information present in source CT and MR images. Initially, the source images are subjected to two-scale ℓ1 − ℓ0 decomposition with optimized parameters, giving a scale-1 detail layer, a scale-2 detail layer and a scale-2 base layer. Two detail layers at scale-1 and 2 are fused using an optimized biologically inspired neural model and weighted average scheme based on local energy and modified spatial frequency to maximize the preservation of edges and local textures, respectively, while the scale-2 base layer gets fused using choose max rule to preserve the background information. To optimize the hyper-parameters of hybrid ℓ1 − ℓ0 decomposition and biologically inspired neural model, a fitness function is evaluated based on spatial frequency and edge index of the resultant fused image obtained by adding all the fused components. The fusion performance is analyzed by conducting extensive experiments on different CT-MR neurological images. Experimental results indicate that the proposed method provides better-fused images and outperforms the other state-of-the-art fusion methods in both visual and quantitative assessments.
|
|
|
Miquel Ferrer, Ernest Valveny, & F. Serratosa. (2009). Median graph: A new exact algorithm using a distance based on the maximum common subgraph. PRL - Pattern Recognition Letters, 30(5), 579–588.
Abstract: Median graphs have been presented as a useful tool for capturing the essential information of a set of graphs. Nevertheless, computation of optimal solutions is a very hard problem. In this work we present a new and more efficient optimal algorithm for the median graph computation. With the use of a particular cost function that permits the definition of the graph edit distance in terms of the maximum common subgraph, and a prediction function in the backtracking algorithm, we reduce the size of the search space, avoiding the evaluation of a great amount of states and still obtaining the exact median. We present a set of experiments comparing our new algorithm against the previous existing exact algorithm using synthetic data. In addition, we present the first application of the exact median graph computation to real data and we compare the results against an approximate algorithm based on genetic search. These experimental results show that our algorithm outperforms the previous existing exact algorithm and in addition show the potential applicability of the exact solutions to real problems.
|
|
|
Debora Gil, Sergio Vera, Agnes Borras, Albert Andaluz, & Miguel Angel Gonzalez Ballester. (2017). Anatomical Medial Surfaces with Efficient Resolution of Branches Singularities. MIA - Medical Image Analysis, 35, 390–402.
Abstract: Medial surfaces are powerful tools for shape description, but their use has been limited due to the sensibility existing methods to branching artifacts. Medial branching artifacts are associated to perturbations of the object boundary rather than to geometric features. Such instability is a main obstacle for a condent application in shape recognition and description. Medial branches correspond to singularities of the medial surface and, thus, they are problematic for existing morphological and energy-based algorithms. In this paper, we use algebraic geometry concepts in an energy-based approach to compute a medial surface presenting a stable branching topology. We also present an ecient GPU-CPU implementation using standard image processing tools. We show the method computational eciency and quality on a custom made synthetic database. Finally, we present some results on a medical imaging application for localization of abdominal pathologies.
Keywords: Medial Representations; Shape Recognition; Medial Branching Stability ; Singular Points
|
|
|
Patricia Marquez, Debora Gil, & Aura Hernandez-Sabate. (2012). A Complete Confidence Framework for Optical Flow. In Rita Cucchiara V. M. Andrea Fusiello (Ed.), 12th European Conference on Computer Vision – Workshops and Demonstrations (Vol. 7584, pp. 124–133). LNCS. Florence, Italy, October 7-13, 2012: Springer-Verlag.
Abstract: Medial representations are powerful tools for describing and parameterizing the volumetric shape of anatomical structures. Existing methods show excellent results when applied to 2D objects, but their quality drops across dimensions. This paper contributes to the computation of medial manifolds in two aspects. First, we provide a standard scheme for the computation of medial manifolds that avoid degenerated medial axis segments; second, we introduce an energy based method which performs independently of the dimension. We evaluate quantitatively the performance of our method with respect to existing approaches, by applying them to synthetic shapes of known medial geometry. Finally, we show results on shape representation of multiple abdominal organs, exploring the use of medial manifolds for the representation of multi-organ relations.
Keywords: Optical flow, confidence measures, sparsification plots, error prediction plots
|
|
|
Sergio Vera, Debora Gil, Agnes Borras, F. Javier Sanchez, Frederic Perez, & Marius G. Linguraru. (2011). Computation and Evaluation of Medial Surfaces for Shape Representation of Abdominal Organs. In In H. Yoshida et al (Ed.), Workshop on Computational and Clinical Applications in Abdominal Imaging (Vol. 7029, pp. 223–230). Springer Berlin Heidelberg.
Abstract: Medial representations are powerful tools for describing and parameterizing the volumetric shape of anatomical structures. Existing methods show excellent results when applied to 2D objects, but their quality drops across dimensions. This paper contributes to the computation of medial manifolds in two aspects. First, we provide a standard scheme for the computation of medial manifolds that avoid degenerated medial axis segments; second, we introduce an energy based method which performs independently of the dimension. We evaluate quantitatively the performance of our method with respect to existing approaches, by applying them to synthetic shapes of known medial geometry. Finally, we show results on shape representation of multiple abdominal organs, exploring the use of medial manifolds for the representation of multi-organ relations.
|
|
|
Sergio Vera, Debora Gil, Agnes Borras, F. Javier Sanchez, Frederic Perez, Marius G. Linguraru, et al. (2012). Computation and Evaluation of Medial Surfaces for Shape Representation of Abdominal Organs. In H. Yoshida et al (Ed.), Workshop on Computational and Clinical Applications in Abdominal Imaging (Vol. 7029, 223–230). LNCS. Berlin: Springer Link.
Abstract: Medial representations are powerful tools for describing and parameterizing the volumetric shape of anatomical structures. Existing methods show excellent results when applied to 2D
objects, but their quality drops across dimensions. This paper contributes to the computation of medial manifolds in two aspects. First, we provide a standard scheme for the computation of medial
manifolds that avoid degenerated medial axis segments; second, we introduce an energy based method which performs independently of the dimension. We evaluate quantitatively the performance of our
method with respect to existing approaches, by applying them to synthetic shapes of known medial geometry. Finally, we show results on shape representation of multiple abdominal organs,
exploring the use of medial manifolds for the representation of multi-organ relations.
Keywords: medial manifolds, abdomen.
|
|
|
Sergio Vera, Miguel Angel Gonzalez Ballester, & Debora Gil. (2012). A medial map capturing the essential geometry of organs. In ISBI Workshop on Open Source Medical Image Analysis software (1691 - 1694). IEEE.
Abstract: Medial representations are powerful tools for describing and parameterizing the volumetric shape of anatomical structures. Accurate computation of one pixel wide medial surfaces is mandatory. Those surfaces must represent faithfully the geometry of the volume. Although morphological methods produce excellent results in 2D, their complexity and quality drops across dimensions, due to a more complex description of pixel neighborhoods. This paper introduces a continuous operator for accurate and efficient computation of medial structures of arbitrary dimension. Our experiments show its higher performance for medical imaging applications in terms of simplicity of medial structures and capability for reconstructing the anatomical volume
Keywords: Medial Surface Representation, Volume Reconstruction,Geometry , Image reconstruction , Liver , Manifolds , Shape , Surface morphology , Surface reconstruction
|
|
|
Sergio Vera, Miguel Angel Gonzalez Ballester, & Debora Gil. (2012). Optimal Medial Surface Generation for Anatomical Volume Representations. In MichaelW. David and Vannier H. and H. Yoshida (Ed.), Abdominal Imaging. Computational and Clinical Applications (Vol. 7601, pp. 265–273). Lecture Notes in Computer Science. Springer Berlin Heidelberg.
Abstract: Medial representations are a widely used technique in abdominal organ shape representation and parametrization. Those methods require good medial manifolds as a starting point. Any medial
surface used to parametrize a volume should be simple enough to allow an easy manipulation and complete enough to allow an accurate reconstruction of the volume. Obtaining good quality medial
surfaces is still a problem with current iterative thinning methods. This forces the usage of generic, pre-calculated medial templates that are adapted to the final shape at the cost of a drop in volume reconstruction.
This paper describes an operator for generation of medial structures that generates clean and complete manifolds well suited for their further use in medial representations of abdominal organ volumes. While being simpler than thinning surfaces, experiments show its high performance in volume reconstruction and preservation of medial surface main branching topology.
Keywords: Medial surface representation; volume reconstruction
|
|
|
Murad Al Haj, Carles Fernandez, Zhanwu Xiong, Ivan Huerta, Jordi Gonzalez, & Xavier Roca. (2011). Beyond the Static Camera: Issues and Trends in Active Vision. In Th.B. Moeslund, A. Hilton, V. Krüger, & L. Sigal (Eds.), Visual Analysis of Humans: Looking at People (pp. 11–30). Springer London.
Abstract: Maximizing both the area coverage and the resolution per target is highly desirable in many applications of computer vision. However, with a limited number of cameras viewing a scene, the two objectives are contradictory. This chapter is dedicated to active vision systems, trying to achieve a trade-off between these two aims and examining the use of high-level reasoning in such scenarios. The chapter starts by introducing different approaches to active cameras configurations. Later, a single active camera system to track a moving object is developed, offering the reader first-hand understanding of the issues involved. Another section discusses practical considerations in building an active vision platform, taking as an example a multi-camera system developed for a European project. The last section of the chapter reflects upon the future trends of using semantic factors to drive smartly coordinated active systems.
|
|
|
Raul Gomez, Lluis Gomez, Jaume Gibert, & Dimosthenis Karatzas. (2018). Learning from# Barcelona Instagram data what Locals and Tourists post about its Neighbourhoods. In 15th European Conference on Computer Vision Workshops (Vol. 11134, pp. 530–544). LNCS.
Abstract: Massive tourism is becoming a big problem for some cities, such as Barcelona, due to its concentration in some neighborhoods. In this work we gather Instagram data related to Barcelona consisting on images-captions pairs and, using the text as a supervisory signal, we learn relations between images, words and neighborhoods. Our goal is to learn which visual elements appear in photos when people is posting about each neighborhood. We perform a language separate treatment of the data and show that it can be extrapolated to a tourists and locals separate analysis, and that tourism is reflected in Social Media at a neighborhood level. The presented pipeline allows analyzing the differences between the images that tourists and locals associate to the different neighborhoods. The proposed method, which can be extended to other cities or subjects, proves that Instagram data can be used to train multi-modal (image and text) machine learning models that are useful to analyze publications about a city at a neighborhood level. We publish the collected dataset, InstaBarcelona and the code used in the analysis.
|
|
|
Maria Ines Torres, Javier Mikel Olaso, Cesar Montenegro, Riberto Santana, A.Vazquez, Raquel Justo, et al. (2019). The EMPATHIC project: mid-term achievements. In 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments (pp. 629–638).
Abstract: Maria Ines Torres; Javier Mikel Olaso, César Montenegro, Riberto Santana, A. Vázquez, Raquel Justo, J. A. Lozano, Stephan Schlögl, Gérard Chollet, Nazim Dugan, M. Irvine, N. Glackin, C. Pickard, Anna Esposito, Gennaro Cordasco, Alda Troncone, Dijana Petrovska-Delacrétaz, Aymen Mtibaa, Mohamed Amine Hmani, M. S. Korsnes, L. J. Martinussen, Sergio Escalera, C. Palmero Cantariño, Olivier Deroo, O. Gordeeva, Jofre Tenorio-Laranga, E. Gonzalez-Fraile, Begoña Fernández-Ruanova, A. Gonzalez-Pinto
|
|
|
Sergio Escalera, Alicia Fornes, O. Pujol, Petia Radeva, Gemma Sanchez, & Josep Llados. (2009). Blurred Shape Model for Binary and Grey-level Symbol Recognition. PRL - Pattern Recognition Letters, 30(15), 1424–1433.
Abstract: Many symbol recognition problems require the use of robust descriptors in order to obtain rich information of the data. However, the research of a good descriptor is still an open issue due to the high variability of symbols appearance. Rotation, partial occlusions, elastic deformations, intra-class and inter-class variations, or high variability among symbols due to different writing styles, are just a few problems. In this paper, we introduce a symbol shape description to deal with the changes in appearance that these types of symbols suffer. The shape of the symbol is aligned based on principal components to make the recognition invariant to rotation and reflection. Then, we present the Blurred Shape Model descriptor (BSM), where new features encode the probability of appearance of each pixel that outlines the symbols shape. Moreover, we include the new descriptor in a system to deal with multi-class symbol categorization problems. Adaboost is used to train the binary classifiers, learning the BSM features that better split symbol classes. Then, the binary problems are embedded in an Error-Correcting Output Codes framework (ECOC) to deal with the multi-class case. The methodology is evaluated on different synthetic and real data sets. State-of-the-art descriptors and classifiers are compared, showing the robustness and better performance of the present scheme to classify symbols with high variability of appearance.
|
|
|
Naila Murray, Maria Vanrell, Xavier Otazu, & C. Alejandro Parraga. (2011). Saliency Estimation Using a Non-Parametric Low-Level Vision Model. In IEEE conference on Computer Vision and Pattern Recognition (pp. 433–440).
Abstract: Many successful models for predicting attention in a scene involve three main steps: convolution with a set of filters, a center-surround mechanism and spatial pooling to construct a saliency map. However, integrating spatial information and justifying the choice of various parameter values remain open problems. In this paper we show that an efficient model of color appearance in human vision, which contains a principled selection of parameters as well as an innate spatial pooling mechanism, can be generalized to obtain a saliency model that outperforms state-of-the-art models. Scale integration is achieved by an inverse wavelet transform over the set of scale-weighted center-surround responses. The scale-weighting function (termed ECSF) has been optimized to better replicate psychophysical data on color appearance, and the appropriate sizes of the center-surround inhibition windows have been determined by training a Gaussian Mixture Model on eye-fixation data, thus avoiding ad-hoc parameter selection. Additionally, we conclude that the extension of a color appearance model to saliency estimation adds to the evidence for a common low-level visual front-end for different visual tasks.
Keywords: Gaussian mixture model;ad hoc parameter selection;center-surround inhibition windows;center-surround mechanism;color appearance model;convolution;eye-fixation data;human vision;innate spatial pooling mechanism;inverse wavelet transform;low-level visual front-end;nonparametric low-level vision model;saliency estimation;saliency map;scale integration;scale-weighted center-surround response;scale-weighting function;visual task;Gaussian processes;biology;biology computing;colour vision;computer vision;visual perception;wavelet transforms
|
|