Fabio Martínez

Segmentation of pelvic structures for planning CT using a geometrical shape model tuned by a multi-scale edge detector.

Accurate segmentation of the prostate and organs at risk in computed tomography (CT) images is a crucial step for radiotherapy planning. Manual segmentation, as performed nowadays, is a time consuming process and prone to errors due to the a high intra- and inter-expert variability. This paper introduces a new automatic method for prostate, rectum and bladder segmentation in planning CT using a geometrical shape model under a Bayesian framework. A set of prior organ shapes are first built by applying principal component analysis to a population of manually delineated CT images. Then, for a given individual, the most similar shape is obtained by mapping a set of multi-scale edge observations to the space of organs with a customized likelihood function. Finally, the selected shape is locally deformed to adjust the edges of each organ. Experiments were performed with real data from a population of 116 patients treated for prostate cancer. The data set was split in training and test groups, with 30 and 86 patients, respectively. Results show that the method produces competitive segmentations w.r.t standard methods (averaged dice = 0.91 for prostate, 0.94 for bladder, 0.89 for rectum) and outperforms the majority-vote multi-atlas approaches (using rigid registration, free-form deformation and the demons algorithm).

Simulation of normal and pathological gaits using a fusion knowledge strategy

BackgroundGait distortion is the first clinical manifestation of many pathological disorders. Traditionally, the gait laboratory has been the only available tool for supporting both diagnosis and prognosis, but under the limitation that any clinical interpretation depends completely on the physician expertise. This work presents a novel human gait model which fusions two important gait information sources: an estimated Center of Gravity (CoG) trajectory and learned heel paths, by that means allowing to reproduce kinematic normal and pathological patterns. The CoG trajectory is approximated with a physical compass pendulum representation that has been extended by introducing energy accumulator elements between the pendulum ends, thereby emulating the role of the leg joints and obtaining a complete global gait description. Likewise, learned heel paths captured from actual data are learned to improve the performance of the physical model, while the most relevant joint trajectories are estimated using a classical inverse kinematic rule. The model is compared with standard gait patterns, obtaining a correlation coefficient of 0.96. Additionally,themodel simulates neuromuscular diseases like Parkinson (phase 2, 3 and 4) and clinical signs like the Crouch gait, case in which the averaged correlation coefficient is 0.92.

A kinematic method for computing the motion of the body centre-of-mass (CoM) during walking: a Bayesian approach

The gait pattern of a particular patient can be altered in a large set of pathologies. Tracking the body centre-of-mass (CoM) during the gait allows a quantitative evaluation of these diseases at comparing the gait with normal patterns. A correct estimation of this variable is still an open question because of its non-linearity and inaccurate location. This paper presents a novel strategy for tracking the CoM, using a biomechanical gait model whose parameters are determined by a Bayesian strategy. A particle filter is herein implemented for predicting the model parameters from a set of markers located at the sacral zone. The present approach is compared with other conventional tracking methods and decreases the calculated root mean squared error in about a 56% in the x-axis and 59% in the y-axis.

Automatic analysis and characterization of the hummingbird wings motion using dense optical flow features

A new method for automatic analysis and characterization of recorded hummingbird wing motion is proposed. The method starts by computing a multiscale dense optical flow field, which is used to segment the wings, i.e., pixels with larger velocities. Then, the kinematic and deformation of the wings were characterized as a temporal set of global and local measures: a global angular acceleration as a time function of each wing and a local acceleration profile that approximates the dynamics of the different wing segments. Additionally, the variance of the apparent velocity orientation estimates those wing foci with larger deformation. Finally a local measure of the orientation highlights those regions with maximal deformation. The approach was evaluated in a total of 91 flight cycles, captured using three different setups. The proposed measures follow the yaw turn hummingbird flight dynamics, with a strong correlation of all computed paths, reporting a standard deviation of [Formula: see text] and [Formula: see text] for the global angular acceleration and the global wing deformation respectively.

A Motion Descriptor Based on Statistics of Optical Flow Orientations for Action Classification in Video-Surveillance

This work introduces a novel motion descriptor that enables human activity classification in video-surveillance applications. The method starts by computing a dense optical flow, providing instantaneous velocity information for every pixel. The obtained flow is then characterized by a per-frameorientation histogram, weighted by the norm, with orientations quantized to 32 principal directions. Finally, a set of global characteristics is determined from the temporal series obtained from each histogram bin, forming a descriptor vector. The method was evaluated using a 192-dimensional descriptor with the classical Weizmann action dataset, obtaining an average accuracy of 95%. For more complex surveillance scenarios, the method was assessed with the VISOR dataset, achieving a 96.7% of accuracy in a classification task performed using a Support Vector Machine (SVM) classifier.

A Gaussian Mixture Representation of Gesture Kinematics for On-Line Sign Language Video Annotation

Sign languages (SLs) are visuo-gestural representations used by deaf communities. Recognition of SLs usually requires manual annotations, which are expert dependent, prone to errors and time consuming. This work introduces a method to support SL annotations based on a motion descriptor that characterizes dynamic gestures in videos. The proposed approach starts by computing local kinematic cues, represented as mixtures of Gaussians which together correspond to gestures with a semantic equivalence in the sign language corpora. At each frame, a spatial pyramid partition allows a fine-to-coarse sub-regional description of motion-cues distribution. Then for each sub-region, a histogram of motion-cues occurrence is built, forming a frame-gesture descriptor which can be used for on-line annotation. The proposed approach is evaluated using a bag-of-features framework, in which every frame-level histogram is mapped to an SVM. Experimental results show competitive results in terms of accuracy and time computation for a signing dataset.

Representing activities with layers of velocity statistics for multiple human action recognition in surveillance applications

A novel action recognition strategy in a video-surveillance context is herein presented. The method starts by computing a multiscale dense optical flow, from which spatial apparent movement regions are clustered as Regions of Interest (RoIs). Each ROI is summarized at each time by an orientation histogram. Then, a multilayer structure dynamically stores the orientation histograms associated to any of the found RoI in the scene and a set of cumulated temporal statistics is used to label that RoI using a previously trained support vector machine model. The method is evaluated using classic human action and public surveillance datasets, with two different tasks: (1) classification of short sequences containing individual actions, and (2) Frame-level recognition of human action in long sequences containing simultaneous actions. The accuracy measurements are: 96:7% (sequence rate) for the classification task, and 95:3% (frame rate) for recognition in surveillance scenes.

A Novel Right Ventricle Segmentation Approach from Local Spatio-temporal MRI Information

This paper presents a novel method that follows the right ventricle (RV) shape during a whole cardiac cycle in magnetic resonance sequences (MRC). The proposed approach obtains an initial coarse segmentation by a bidirectional per pixel motion descriptor. Then a refined segmentation is obtained by fusing the previous segmentation with geometrical observations at each frame. A main advantage of the proposed approach is a robust MRI heart characterization without any prior information. The proposed approach achieves a Dice Score of 0.62 evaluated over 32 patients.

Automatic characterization of the Parkinson disease by classifying the ipsilateral coordination and spatiotemporal gait patterns

Traditionally, the Parkinson disease is diagnosed and followed up by conventional clinical tests that are fully dependent on the expert experience. The diffuse boundary between normal and early Parkinson stages and the high variability of gait patterns difficult any objective characterization of this disease. An automatic characterization of the disease is herein proposed by mixing up different measures of the ipsilateral coordination and spatiotemporal gait patterns which are then classified with a classical support vector machine. The strategy was evaluated in a population with Parkinson and healthy control subjects, obtaining an average accuracy of 87% for the task of classification.

Characterizing the eye trajectory during the gait towards Parkinson stage identification

Parkinson’s Disease characterization is commonly carried out by measuring a motor abnormality that may affect an optimal locomotion. However, such gait characterization is far from achieving accurate and sensible early detection of this disease, dealying between 6 months to 3 years a first diagnosis. Current research has identified the eye movements (EM) as a powerful biomarker that may detect and identify PD, even in early stages. However, this eye analysis is now performed under fully controlled conditions and strict protocols, for which the patient must follow a set of routine movements in a static position. Such protocols however loss some natural eye movements during the gait that may help to promptly highlight the disease. This work presents preliminary results characterizing and analyzing the center of mass of the eye movement during the gait, captured using a high speed camera. An automatic tracking strategy was herein implemented to follow the eye during the locomotion. Promising results were obtained from a set of real patients diagnosed with parkinson diseases in stages of 1 y 3, which show strong differences among the computed signals.