Juan J. Cerrolaza

Taxonomic study of polynomial regressions applied to the calibration of video-oculographic systems

Of gaze tracking techniques, video-oculography (VOG) is one of the most attractive because of its versatility and simplicity. VOG systems based on general purpose mapping methods use simple polynomial expressions to estimate a users point of regard. Although the behaviour of such systems is generally acceptable, a detailed study of the calibration process is needed to facilitate progress in improving accuracy and tolerance to user head movement. To date, there has been no thorough comparative study of how mapping equations affect final system response. After developing a taxonomic classification of calibration functions, we examine over 400,000 models and evaluate the validity of several conventional assumptions. The rigorous experimental procedure employed enabled us to optimize the calibration process for a real VOG gaze tracking system and, thereby, halve the calibration time without detrimental effect on accuracy or tolerance to head movement.

Error characterization and compensation in eye tracking systems

The development of systems that track the eye while allowing head movement is one of the most challenging objectives of gaze tracking researchers. Tracker accuracy decreases as the subject moves from the calibration position and is especially influenced by changes in depth with respect to the screen. In this paper, we demonstrate that the pattern of error produced due to user movement mainly depends on the system configuration and hardware element placement rather than the user. Thus, we suggest alternative calibration techniques for error reduction that compensate for the lack of accuracy due to subject movement. Using these techniques, we can achieve an error reduction of more than 50%.

Hierarchical Statistical Shape Models of Multiobject Anatomical Structures: Application to Brain MRI

The accurate segmentation of subcortical brain structures in magnetic resonance (MR) images is of crucial importance in the interdisciplinary field of medical imaging. Although statistical approaches such as active shape models (ASMs) have proven to be particularly useful in the modeling of multiobject shapes, they are inefficient when facing challenging problems. Based on the wavelet transform, the fully generic multiresolution framework presented in this paper allows us to decompose the interobject relationships into different levels of detail. The aim of this hierarchical decomposition is twofold: to efficiently characterize the relationships between objects and their particular localities. Experiments performed on an eight-object structure defined in axial cross sectional MR brain images show that the new hierarchical segmentation significantly improves the accuracy of the segmentation, and while it exhibits a remarkable robustness with respect to the size of the training set.

Study of Polynomial Mapping Functions in Video-Oculography Eye Trackers

Gaze-tracking data have been used successfully in the design of new input devices and as an observational technique in usability studies. Polynomial-based Video-Oculography (VOG) systems are one of the most attractive gaze estimation methods thanks to their simplicity and ease of implementation. Although the functionality of these systems is generally acceptable, there has been no thorough comparative study to date of how the mapping equations affect the final system response. After developing a taxonomic classification of calibration functions, we examined over 400,000 models and evaluated the validity of several conventional assumptions. Our rigorous experimental procedure enabled us to optimize the calibration process for a real VOG gaze-tracking system and halve the calibration time while avoiding a detrimental effect on the accuracy or tolerance to head movement. Finally, a geometry-based method is implemented and tested. The results and performance is compared with those obtained by the general purpose expressions.

Automatic multi-resolution shape modeling of multi-organ structures

Point Distribution Models (PDM) are among the most popular shape description techniques and their usefulness has been demonstrated in a wide variety of medical imaging applications. However, to adequately characterize the underlying modeled population it is essential to have a representative number of training samples, which is not always possible. This problem is especially relevant as the complexity of the modeled structure increases, being the modeling of ensembles of multiple 3D organs one of the most challenging cases. In this paper, we introduce a new GEneralized Multi-resolution PDM (GEM-PDM) in the context of multi-organ analysis able to efficiently characterize the different inter-object relations, as well as the particular locality of each object separately. Importantly, unlike previous approaches, the configuration of the algorithm is automated thanks to a new agglomerative landmark clustering method proposed here, which equally allows us to identify smaller anatomically significant regions within organs. The significant advantage of the GEM-PDM method over two previous approaches (PDM and hierarchical PDM) in terms of shape modeling accuracy and robustness to noise, has been successfully verified for two different databases of sets of multiple organs: six subcortical brain structures, and seven abdominal organs. Finally, we propose the integration of the new shape modeling framework into an active shape-model-based segmentation algorithm. The resulting algorithm, named GEMA, provides a better overall performance than the two classical approaches tested, ASM, and hierarchical ASM, when applied to the segmentation of 3D brain MRI.

Segmentation of kidney in 3D-ultrasound images using Gabor-based appearance models

This paper presents a new segmentation method for 3D ultrasound images of the pediatric kidney. Based on the popular active shape models, the algorithm is tailored to deal with the particular challenges raised by US images. First, a weighted statistical shape model allows to compensate the image variation with the propagation direction of the US wavefront. Second, an orientation correction approach is used to create a Gabor-based appearance model for each landmark at different scales. This multiscale characteristic is incorporated into the segmentation algorithm, creating a hierarchical approach where different appearance models are considered as the segmentation process evolves. The performance of the algorithm was evaluated on a dataset of 14 cases, both healthy and pathological, obtaining an average Dices coefficient of 0.85, an average point-to-point distance of 4.07 mm, and 0.12 average relative volume difference.

Deep Learning Guided Partitioned Shape Model for Anterior Visual Pathway Segmentation

Analysis of cranial nerve systems, such as the anterior visual pathway (AVP), from MRI sequences is challenging due to their thin long architecture, structural variations along the path, and low contrast with adjacent anatomic structures. Segmentation of a pathologic AVP (e.g., with low-grade gliomas) poses additional challenges. In this work, we propose a fully automated partitioned shape model segmentation mechanism for AVP steered by multiple MRI sequences and deep learning features. Employing deep learning feature representation, this framework presents a joint partitioned statistical shape model able to deal with healthy and pathological AVP. The deep learning assistance is particularly useful in the poor contrast regions, such as optic tracts and pathological areas. Our main contributions are: 1) a fast and robust shape localization method using conditional space deep learning, 2) a volumetric multiscale curvelet transform-based intensity normalization method for robust statistical model, and 3) optimally partitioned statistical shape and appearance models based on regional shape variations for greater local flexibility. Our method was evaluated on MRI sequences obtained from 165 pediatric subjects. A mean Dice similarity coefficient of 0.779 was obtained for the segmentation of the entire AVP (optic nerve only =0.791) using the leave-one-out validation. Results demonstrated that the proposed localized shape and sparse appearance-based learning approach significantly outperforms current state-of-the-art segmentation approaches and is as robust as the manual segmentation.

Geometry Issues of Gaze Estimation

Video-oculography (VOG) is a non-intrusive method used to estimate gaze. The method is based on a remote camera(s) that provides images of the eyes that are processed by a computer to estimate the point at which the subject is gazing in the area of interest. Normally, infrared (IR) light-emitting diodes (LEDs) are used in the system, as this light is not visible to humans. The objective of the light source is to increase the quality of the image and to produce reflections on the cornea. These reflections can be observed in the acquired images (see Figure 1) and represent useful features for gaze estimation.

Shape Constraint Strategies: Novel Approaches and Comparative Robustness.

Active Shape Models are some of the most actively researched model-based segmentation approaches. An accurate estimation of the shape probability distribution is essential to provide the prior knowledge that makes ASMs able to handle the large inherent variability of anatomical structures, differentiating between allowed and invalid instances. Under the typical assumption of normality the subspace of allowed shapes (SAS) is confined within a hyperellipsoid. Although the approximation of the SAS by a hypercube provides computational advantages, this simplification allows the occurrence of highly improbable instances. In addition, a high dependency on the rest of the configuration parameters is observed when the general segmentation algorithm incorporates the hypercube simplification. In this work, a new, efficient hyperelliptical approximation of the SAS based on the Newton-Raphson optimisation method is presented. To perform a detailed comparative study of the effect that four different SAS estimation approaches have on the general segmentation process, a generalisation of the typical two-factor factorial design is used on two different image databases. The results obtained by means of this statistical technique not only reveal the superiority of the new hyperelliptical method in terms of both accuracy and robustness but also provide information of great interest for optimising the segmentation process.

Renal Segmentation From 3D Ultrasound via Fuzzy Appearance Models and Patient-Specific Alpha Shapes

Ultrasound (US) imaging is the primary imaging modality for pediatric hydronephrosis, which manifests as the dilation of the renal collecting system (CS). In this paper, we present a new framework for the segmentation of renal structures, kidney and CS, from 3DUS scans. First, the kidney is segmented using an active shape model-based approach, tailored to deal with the challenges raised by US images. A weighted statistical shape model allows to compensate the image variation with the propagation direction of the US wavefront. The model is completed with a new fuzzy appearance model and a multi-scale omnidirectional Gabor-based appearance descriptor. Next, the CS is segmented using an active contour formulation, which combines contour- and intensity-based terms. The new positive alpha detector presented here allows to control the propagation process by means of a patient-specific stopping function created from the bands of adipose tissue within the kidney. The performance of the new segmentation approach was evaluated on a dataset of 39 cases, showing an average Dices coefficient of 0.86±0.05 for the kidney, and 0.74 ± 0.10 for the CS segmentation, respectively. These promising results demonstrate the potential utility of this framework for the US-based assessment of the severity of pediatric hydronephrosis.