In this work, we present a multi-modal machine learning method to automate early glaucoma diagnosis. The proposed methodology introduces two novel aspects for automated diagnosis not previously explored in the literature: simultaneous use of ocular fundus images from both eyes and integration with the patient's additional clinical data. We begin by establishing a baseline, termed monocular mode, which adheres to the traditional approach of considering the data from each eye as a separate instance. We then explore the binocular mode, investigating how combining information from both eyes of the same patient can enhance glaucoma diagnosis accuracy. This exploration employs the PAPILA dataset, comprising information from both eyes, clinical data, ocular fundus images, and expert segmentation of these images. Additionally, we compare two image-derived data modalities: direct ocular fundus images and morphological data from manual expert segmentation. Our method integrates Gradient-Boosted Decision Trees (GBDT) and Convolutional Neural Networks (CNN), specifically focusing on the MobileNet, VGG16, ResNet-50, and Inception models. SHAP values are used to interpret GBDT models, while the Deep Explainer method is applied in conjunction with SHAP to analyze the outputs of convolutional-based models. Our findings show the viability of considering both eyes, which improves the model performance. The binocular approach, incorporating information from morphological and clinical data yielded an AUC of 0.796 (±0.003 at a 95% confidence interval), while the CNN, using the same approach (both eyes), achieved an AUC of 0.764 (±0.005 at a 95% confidence interval).

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.

This paper addresses the functional localization of intra-patient images of the brain. Functional images of the brain (fMRI and PET) provide information about brain function and metabolism whereas anatomical images (MRI and CT) supply the localization of structures with high spatial resolution. The goal is to find the geometric correspondence between functional and anatomical images in order to complement and fuse the information provided by each imaging modality. The proposed approach is based on a variational formulation of the image registration problem in the frequency domain. It has been implemented as a C/C[Formula: see text] library which is invoked from a GUI. This interface is routinely used in the clinical setting by physicians for research purposes (Inscanner, Alicante, Spain), and may be used as well for diagnosis and surgical planning. The registration of anatomic and functional intra-patient images of the brain makes it possible to obtain a geometric correspondence which allows for the localization of the functional processes that occur in the brain. Through 18 clinical experiments, it has been demonstrated how the proposed approach outperforms popular state-of-the-art registration methods in terms of efficiency, information theory-based measures (such as mutual information) and actual registration error (distance in space of corresponding landmarks).

This paper presents the formulation and implementation of multidimensional deformable models in the frequency domain. The models are defined by finite element discretization, using B-splines as shape function. The frequency-based formulation allows us to obtain an efficient iterative system in matrix form, suitable for models of any dimension. The aim of this approach is the use of these models for fast segmentation and motion tracking of three-dimensional objects with non rigid motion or deformation from three-dimensional datasets. To show the results of the process, a two dimensional model is applied for the characterization of topographic data.

Atherosclerosis is a cardiovascular disease very widespread into population. It is characterized by a thickening of the arterial walls, which affects blood flow. The intima-media thickness (IMT) has emerged as a reliable early indicator of this pathology.

The presence of metal artifacts in dental CT prevents the correct exploration and planning of dental interventions. This paper addresses a new paradigm in metal artifact reduction that uses the backprojected data available in the DICOM files. The method, based on variational image registration and morphological lambda reconstruction, enhances the image quality using not only the information of the artifacted image (horizontal approach) but also the information of adjoining images (vertical approach). Some preliminary results involving different CT scanners and patients are presented and discussed.

This paper deals with the theory and applications of spatially-variant discrete mathematical morphology. We review and formalize the definition of spatially variant dilation/erosion and opening/closing for binary and gray-level images using exclusively the structuring function, without resorting to complement. This theoretical framework allows to build morphological operators whose structuring elements can locally adapt their shape and orientation across the dominant direction of the structures in the image. The shape and orientation of the structuring element at each pixel are extracted from the image under study: the orientation is given by means of a diffusion process of the average square gradient field, which regularizes and extends the orientation information from the edges of the objects to the homogeneous areas of the image; and the shape of the orientated structuring elements can be linear or it can be given by the distance to relevant edges of the objects. The proposed filters are used on binary and gray-level images for enhancement of anisotropic features such as coherent, flow-like structures. Results of spatially-variant erosions/dilations and openings/closings-based filters prove the validity of this theoretical sound and novel approach.

Arteriosclerosis is one of the most extended diseases of the arterial walls which may lead to serious heart disease. The intima-medial thickness (IMT) of the carotid artery has emerged as an early and reliable indicator of arteriosclerosis. It is measured using ultrasound B-scan. The IMT is still measured visually by the doctor. To solve all the problems related to the manual procedure employed, a segmentation of the image is presented. Thanks to this segmentation, the intima and media walls are better defined along all its length, allowing the extraction of more measurements like the mean, maximum or minimum IMT and even studying its evolution through time. To achieve this segmentation, different image processing techniques have been used, such as anisotropic diffusion or geodesic active contours. With the process here presented, the IMT calculated is comparable with that extracted by the specialist, but avoiding the drawbacks of a visual procedure.

This paper shows a comparison of spatially-variant discrete operators for denoising gray-level images. These non-iterative operators use a neighborhood that varies over space, adapting their shape and orientation according to the data of the image under study. The orientation of the neighborhood is computed by means of a diffusion process of the average square gradient field, which regularizes and extends the orientation information from the edges of the objects to the homogeneous areas of the image; and the shape of the orientated neighborhood can be either a linear segment or a rectangle of anisotropy given by the distance to relevant edges of the objects. Results on gray-level images show the ability of spatially-variant morphological operators for adaptively preserving the main structures in the image while reducing the noise.

In this letter, a novel theoretical framework defined in the frequency domain for variational nonparametric image registration is proposed. It provides more efficient implementations than existing theoretical approaches for the most common registration techniques. The presented frequency domain analysis adds simplicity to the variational image registration implementations and allows for an easy extension to higher dimensions by using the multidimensional Fourier transform of multidimensional signals.

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 drawback of this method takes place when more than one matching is likely. A first simplification to this ambiguity is to represent with a parametric model the point locus where the matching is highly likely, and then use a POCS (projection onto convex sets) procedure combined with Tikhonov regularization that results in the mapping vectors. However, if there is more than one model per pixel, the regularization and constraint-forcing process faces a multiple-choice dilemma that has no easy solution. This work proposes a framework to overcome this drawback: the combined projection over multiple models based on the L/sub k/, norm of the projection-point distance. This approach is tested on a stereo-pair that presents multiple choices of similar likelihood.

Artificial muscles are formed by attaching a conducting polymeric film to a non-conducting one. The flow of an electric current produces a macroscopic bending movement on the muscle. A good characterization of both, motion rate and energy of curvature, is required for improving the efficiency of these devices. In this paper, a two-cam stereo vision system is proposed to acquire and process the image sequence and a 3D snake for tracking the muscle. From the curve given by the snake, mechanical parameters of the artificial muscle can be estimated. The movements along the life cycle of the muscle can be compared with the energy consumed in each cycle. This is necessary for determining the span life of these devices in applications where they work as actuators. Results prove the validity of this approach.

Active deformable models are simple tools, very popular in computer vision and computer graphics, for solving ill-posed problems or mimic real physical systems. The classical formulation is given in the spatial domain, the motor of the procedure is a second-order linear system, and rigidity and elasticity are the basic parameters for its characterization. This paper proposes a novel formulation based on a frequency-domain analysis: The internal energy functional and the Lagrange minimization are performed entirely in the frequency domain, which leads to a simple formulation and design. The frequency-based implementation offers important computational savings in comparison to the original one, a feature that is improved by the efficient hardware and software computation of the FFT algorithm. This new formulation focuses on the stiffness spectrum, allowing the possibility of constructing deformable models apart from the elasticity and rigidity-based original formulation. Simulation examples validate the theoretical results.

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.

In radiotherapy (RT), organ motion caused by breathing prevents accurate patient positioning, radiation dose, and target volume determination. Most of the motion-compensated trial techniques require collaboration of the patient and expensive equipment. Estimating the motion between two computed tomography (CT) three-dimensional scans at the extremes of the breathing cycle and including this information in the RT planning has been shyly considered, mainly because that is a tedious manual task. This paper proposes a method to compute in a fully automatic fashion the spatial correspondence between those sets of volumetric CT data. Given the large ambiguity present in this problem, the method aims to reduce gradually this uncertainty through two main phases: a similarity-parametrization data analysis and a projection-regularization phase. Results on a real study show a high accuracy in establishing the spatial correspondence between both sets. Embedding this method in RT planning tools is foreseen, after making some suggested improvements and proving the validity of the two-scan approach.