Adaptive morphological filters based on a multiple orientation vector field dependent on image local features

This paper addresses the formulation of adaptive morphological filters based on spatially-variant structuring elements. The adaptivity of these filters is achieved by modifying the shape and orientation of the structuring elements according to a multiple orientation vector field. This vector field is provided by means of a bank of directional openings which can take into account the possible multiple orientations of the contours in the image. After reviewing and formalizing the definition of the spatially-variant dilation, erosion, opening and closing, the proposed structuring elements are described. These spatially-variant structuring elements are based on ellipses which vary over the image domain adapting locally their orientation according to the multiple orientation vector field and their shape (the eccentricity of the ellipses) according to the distance to relevant contours of the objects. The proposed adaptive morphological filters are used on gray-level images and are compared with spatially-invariant filters, with spatially-variant filters based on a single orientation vector field, and with adaptive morphological bilateral filters. Results show that the morphological filters based on a multiple orientation vector field are more adept at enhancing and preserving structures which contains more than one orientation

Design of wide-beam leaky-wave antenna arrays based on the bilinear transformation of IIR digital filters and the Z transform

In the pioneering work, the radiation diagrams of leaky wave antenna arrays can achieve attenuation nulls and gains at specific angles by manually placing the zeros and the poles in the Z domain of the corresponding discrete linear time-invariant (LTI) system. This handcrafted design procedure does not allow radiation diagrams with wide beams since the interaction between poles involved in the wide beam and their corresponding leaky modes cannot be easily handled. To overcome this limitation, this paper describes a novel method for designing radiation diagrams of leaky-wave antenna arrays based on the theory of IIR discrete filters. The proposed method relies on the design of discrete filters with the prototypes of analog low-pass filters defined by Butterworth and Chebyshev type I polynomials, whose roots along with the bilinear transformation provide the location of the poles and the zeros of the discrete LTI system and, therefore, the parameters of the leaky-wave antenna array. Results with different designs and a comparison with other approaches show the utility and effectiveness of this novel method to design wide-beam leaky-wave antenna arrays.This work was supported by the Spanish National project PID2019-103982RB-C42/AEI/10.13039/501100011033

Analysis of the asymmetry between both eyes in early diagnosis of glaucoma combining features extracted from retinal images and OCTs into classification models

This study aims to analyze the asymmetry between both eyes of the same patient for the early diagnosis of glaucoma. Two imaging modalities, retinal fundus images and optical coherence tomographies (OCTs), have been considered in order to compare their different capabilities for glaucoma detection. From retinal fundus images, the difference between cup/disc ratio and the width of the optic rim has been extracted. Analogously, the thickness of the retinal nerve fiber layer has been measured in spectral-domain optical coherence tomographies. These measurements have been considered as asymmetry characteristics between eyes in the modeling of decision trees and support vector machines for the classification of healthy and glaucoma patients. The main contribution of this work is indeed the use of different classification models with both imaging modalities to jointly exploit the strengths of each of these modalities for the same diagnostic purpose based on the asymmetry characteristics between the eyes of the patient. The results show that the optimized classification models provide better performance with OCT asymmetry features between both eyes (sensitivity 80.9%, specificity 88.2%, precision 66.7%, accuracy 86.5%) than with those extracted from retinographies, although a linear relationship has been found between certain asymmetry features extracted from both imaging modalities. Therefore, the resulting performance of the models based on asymmetry features proves their ability to differentiate healthy from glaucoma patients using those metrics. Models trained from fundus characteristics are a useful option as a glaucoma screening method in the healthy population, although with lower performance than those trained from the thickness of the peripapillary retinal nerve fiber layer. In both imaging modalities, the asymmetry of morphological characteristics can be used as a glaucoma indicator, as detailed in this work.This work has been partially supported by Spanish National projects AES2017–PI17/00771, AES2017–PI17/00821 (Instituto de Salud Carlos III), and Regional project 20901/PI/18 (Fundación Séneca)

A hybrid framework for efficient and accurate orientation estimation based on single and multiple orientation vector fields

This article presents a hybrid framework for efficient and accurate orientation estimation. The proposed scheme combines the single orientation information given by a novel method and the multiple orientation information provided by a bank of linear orientated morphological openings. The single orientations are estimated by means of an energy-minimization Gaussian filtering which solves the drawback related to phase changes of other methods. After describing the formulation of these two approaches for estimating the existing orientations in the pixels of an image, several strategies have been analyzed to fuse and discriminate the information of both orientation vector fields in the resulting hybrid orientation vector field. The objective of the proposed hybrid method is to reduce the computational cost involved in calculating multiple orientations only in those pixels where they exist while maintaining the accuracy provided by the single orientation method in the remaining pixels. To this end, strategies ranging from a threshold in the multiple orientation vector field to a convolutional neural network trained with a set of patterns specifically designed to detect pixels with multiple orientations, passing through the Harris corner detector, have been tested to identify those pixels where multiple orientations exist. Results on natural and synthetic images show the accuracy and the computational efficiency achieved by the proposed hybrid framework to provide the vector field with single and multiple orientations

3D mechanical characterization of artificial muscles with stereoscopic computer vision and active contours

Artificial muscles are formed by attaching a conducting polymeric film to a non-conducting one. Applying an electrical current on the muscle. a macroscopic bending movement appears on it. Study of curvature variations and related parameters, such as speed of motion or energy of curvature, is necessary for improving the efficiency of these devices. In a previous work. a one-cam computer vision system was developed to estimate motion parameters in 2D with precise results. In this paper, a two-cam stereo vision system is proposed to process the image sequence and track the muscle in 3D. Active contours models are employed in motion detection and mechanical parameters estimation. Results prove the validity of this approach, allowing automatic testing on the research into artificial muscles.This work was supported by MCYI' BQLJ2001-047

Regularizador híbrido para el registro a medida de imágenes médicas

Durante los últimos dos años, ha surgido un gran interés por encontrar nuevos términos de regularización que resulten especialmente adecuados para el registro de imágenes médicas. Los ejemplos más recientes en la literatura están basados en derivadas de primer y/o segundo orden. En este trabajo se propone un nuevo regularizador, basado en derivadas de orden fraccionario, para el registro de imágenes médicas. Puede considerarse como una generalización de los métodos de registro por difusión (derivadas de primer orden) y por curvatura (derivadas de segundo orden), pero con la estrategia propuesta es posible obtener mejores resultados en el registro final desde el punto de vista variacional (i.e., en términos tanto de similitud entre las imágenes como de suavidad en la transformación estimada), y en un menor número de iteraciones del algoritmo de registro.Ministerio de Ciencia y Tecnología a través del proyecto TEC2006-13338/TCM, y por la Agencia Regional de Ciencia y Tecnología (Fundación Séneca) a través del proyecto 03122/PI/05

Multiple feature models for image matching

The common approach to image matching is to detect spatial features present in both images and create a mapping that relates both images. The main draw back of this method takes place when more than one matching is likely. A first simplification to this ambiguity is to represent with apara-metric model the point locus where the matching is highly likely,and then use a POCS(projection on to convex sets)procedure combined with Tikhonov regularization that results in the mapping vectors. However,if there is more than one model perpixel,the regularization and constrainforcing process faces a multiplechoice dilemma that has no easy solution. This work proposes a frame work to overcome this draw back: the combined projection over multiple models base don the norm of the projection–pointdis-tance. This approach is tested on a stereo-pair that presents multiple choices of similar likelihood.This work is partially supported by the Spanish Ministerio de Ciencia y Tecnología,under grant TIC2002-03033

Determination of bifurcation angles of the retinal vascular tree through multiple orientation estimation based on regularized morphological openings

This paper describes a new approach to compute bifurcation angles in retinal images. This approach is based on the estimation of multiple orientations at each pixel of a gray retinal image. The main orientations are provided by directional openings whose outputs are regularized in order to extend the orientation information to the whole image. The detection of vessel bifurcations is based on the coexistence of two or more than two different main orientations at the same pixel. Once the bifurcations and crossovers has been identified, bifurcation angles are calculated. The proposed procedure of computing bifurcation angles by means of orientation estimation at all pixels of the gray level image is much more stable than those methods which are based on the skeleton of the vascular tree, since a slight variation of a pixel of the skeleton can produce a significant change in the angle valueThis work was supported by Ministerio de Economía y Competitividad of Spain,Project ACRIMA (TIN2013-46751-R)

A Self-Training Framework for Glaucoma Grading In OCT B-Scans

[EN] In this paper, we present a self-training-based framework for glaucoma grading using OCT B-scans under the presence of domain shift. Particularly, the proposed two-step learning methodology resorts to pseudo-labels generated during the first step to augment the training dataset on the target domain, which is then used to train the final target model. This allows transferring knowledge-domain from the unlabeled data. Additionally, we propose a novel glaucoma-specific backbone which introduces residual and attention modules via skip-connections to refine the embedding features of the latent space. By doing this, our model is capable of improving state-of-the-art from a quantitative and interpretability perspective. The reported results demonstrate that the proposed learning strategy can boost the performance of the model on the target dataset without incurring in additional annotation steps, by using only labels from the source examples. Our model consistently outperforms the baseline by 1¿3% across different metrics and bridges the gap with respect to training the model on the labeled target data.We gratefully acknowledge the support of the Generalitat Valenciana (GVA) for the donation of the DGX A100 used for this work, action co-financed by the European Union through the Programa Operativo del Fondo Europeo de Desarrollo Regional (FEDER) de la Comunitat Valenciana 2014-2020 (IDIFEDER/2020/030).García-Pardo, JG.; Colomer, A.; Verdú-Monedero, R.; Dolz, J.; Naranjo Ornedo, V. (2021). A Self-Training Framework for Glaucoma Grading In OCT B-Scans. IEEE. 1281-1285. https://doi.org/10.23919/EUSIPCO54536.2021.9616159S1281128

Registro variacional óptimo de imágenes médicas

La correcta selección de parámetros en los métodos de registro no paramétrico de imagen es un problema aún sin resolver. No hay acuerdo sobre cuáles son valores óptimos de estos parámetros, que dependen de las propias imágenes a registrar. Para abordar este problema, en este trabajo se propone un método que consta de dos pasos para obtener los parámetros que nos ofrecen desde un punto de vista variacional el compromiso óptimo entre la similitud de las imágenes registradas y la suavidad de la transformación resultante