Multi-atlas label fusion by using supervised local weighting for brain image segmentation

La segmentación automática de estructuras de interés en imágenes de resonancia magnética cerebral requiere esfuerzos significantes, debido a las formas complicadas, el bajo contraste y la variabilidad anatómica. Un aspecto que reduce el desempeño de la segmentación basada en múltiples atlas es la suposición de correspondencias uno-a-uno entre los voxeles objetivo y los del atlas. Para mejorar el desempeño de la segmentación, las metodologías de fusión de etiquetas incluyen información espacial y de intensidad a través de estrategias de votación ponderada a nivel de voxel. Aunque los pesos se calculan para un conjunto de atlas predefinido, estos no son muy eficientes en etiquetar estructuras intrincadas, ya que la mayoría de las formas de los tejidos no se distribuyen uniformemente en las imágenes. Este artículo propone una metodología de extracción de características a nivel de voxel basado en la combinación lineal de las intensidades de un parche. Hasta el momento, este es el primer intento de extraer características locales maximizando la función de alineamiento de kernel centralizado, buscando construir representaciones discriminativas, superar la complejidad de las estructuras, y reducir la influencia de los artefactos. Para validar los resultados, la estrategia de segmentación propuesta se compara contra la segmentación Bayesiana y la fusión de etiquetas basada en parches en tres bases de datos diferentes. Respecto del índice de similitud Dice, nuestra propuesta alcanza el más alto acierto (90.3% en promedio) con suficiente robusticidad ante los artefactos y respetabilidad apropiada.The automatic segmentation of interest structures is devoted to the morphological analysis of brain magnetic resonance imaging volumes. It demands significant efforts due to its complicated shapes and since it lacks contrast between tissues and intersubject anatomical variability. One aspect that reduces the accuracy of the multi-atlasbased segmentation is the label fusion assumption of one-to-one correspondences between targets and atlas voxels. To improve the performance of brain image segmentation, label fusion approaches include spatial and intensity information by using voxel-wise weighted voting strategies. Although the weights are assessed for a predefined atlas set, they are not very efficient for labeling intricate structures since most tissue shapes are not uniformly distributed in the images. This paper proposes a methodology of voxel-wise feature extraction based on the linear combination of patch intensities. As far as we are concerned, this is the first attempt to locally learn the features by maximizing the centered kernel alignment function. Our methodology aims to build discriminative representations, deal with complex structures, and reduce the image artifacts. The result is an enhanced patch-based segmentation of brain images. For validation, the proposed brain image segmentation approach is compared against Bayesian-based and patch-wise label fusion on three different brain image datasets. In terms of the determined Dice similarity index, our proposal shows the highest segmentation accuracy (90.3% on average); it presents sufficient artifact robustness, and provides suitable repeatability of the segmentation results

Inner-Hair Cells Parameterized-Hardware Implementation for Personalized Auditory Nerve Stimulation

In this paper the hardware implementation of an inner hair cell model is presented. Main features of the design are the use of Meddis’ transduction structure and the methodology for Design with Reusability. Which allows future migration to new hardware and design refinements for speech processing and custom-made hearing aid

Bio-inspired broad-class phonetic labelling

Recent studies have shown that the correct labeling of phonetic classes may help current Automatic Speech Recognition (ASR) when combined with classical parsing automata based on Hidden Markov Models (HMM).Through the present paper a method for Phonetic Class Labeling (PCL) based on bio-inspired speech processing is described. The methodology is based in the automatic detection of formants and formant trajectories after a careful separation of the vocal and glottal components of speech and in the operation of CF (Characteristic Frequency) neurons in the cochlear nucleus and cortical complex of the human auditory apparatus. Examples of phonetic class labeling are given and the applicability of the method to Speech Processing is discussed

On the Selectivity of Planar Microwave Glucose Sensors with Multicomponent Solutions

The development of glucose concentration sensors by means of microwave planar resonant technology is an active field attracting considerable attention from the scientific community. Although showing promising results, the current experimental sensors are facing some fundamental challenges. Among them, the most critical one seems to be the selectivity of glucose concentration against the variations of the concentrations of other components or parameters. In this article, we investigate the selectivity of microwave planar resonant sensors when measuring multicomponent solutions. Three sensors are involved, two of them having been designed looking for a more simplified system with a reduced size, and the third one has been specially developed to improve the sensitivity. The performance of these sensors is thoroughly assessed with a large set of measurements involving multicomponent solutions composed of pure water, NaCl, albumin at different concentrations and glucose at different concentrations. The impact of the simultaneous variations of the concentrations of glucose and albumin on the final measurements is analyzed, and the effective selectivity of the sensors is discussed. The results show a clear influence of the albumin concentration on the measurements of the glucose concentration, thereby pointing to a lack of selectivity for all sensors. This influence has been modeled, and strategies to manage this selectivity challenge are inferredThis research was partially funded by AEI (Spanish Research State Agency) through the Race project (reference PID2019-111023RB-C32). The work of C.G.J. was funded by the Ministry of Universities in the Government of Spain, the European Union–NextGenerationEU and the Miguel Hernández University of Elche through the Margarita Salas postdoctoral program, and also by Conselleria d’Innovació, Universitats, Ciència i Societat Digital in Generalitat Valenciana (Government of Valencia Region) and European Social Fund through the APOSTD postdoctoral program, grant number CIAPOS/2021/267. Partial funding for open access charge: Universidad de Málaga

Monitoring Neurological disease in Phonation

It is well known that many neurological diseases leave a fingerprint in voice and speech production. The dramatic impact of these pathologies in life quality is a growing concert. Many techniques have been designed for the detection, diagnose and monitoring the neurological disease. Most of them are costly or difficult to extend to primary services. The present paper shows that some neurological diseases can be traced a the level of voice production. The detection procedure would be based on a simple voice test. The availability of advanced tools and methodologies to monitor the organic pathology of voice would facilitate the implantation of these tests. The paper hypothesizes some of the underlying mechanisms affecting the production of voice and presents a general description of the methodological foundations for the voice analysis system which can estimate correlates to the neurological disease. A case of study is presented from spasmodic dysphonia to illustrate the possibilities of the methodology to monitor other neurological problems as well

On the Selectivity of Planar Microwave Glucose Sensors with Multicomponent Solutions

The development of glucose concentration sensors by means of microwave planar resonant technology is an active field attracting considerable attention from the scientific community. Although showing promising results, the current experimental sensors are facing some fundamental challenges. Among them, the most critical one seems to be the selectivity of glucose concentration against the variations of the concentrations of other components or parameters. In this article, we investigate the selectivity of microwave planar resonant sensors when measuring multicomponent solutions. Three sensors are involved, two of them having been designed looking for a more simplified system with a reduced size, and the third one has been specially developed to improve the sensitivity. The performance of these sensors is thoroughly assessed with a large set of measurements involving multicomponent solutions composed of pure water, NaCl, albumin at different concentrations and glucose at different concentrations. The impact of the simultaneous variations of the concentrations of glucose and albumin on the final measurements is analyzed, and the effective selectivity of the sensors is discussed. The results show a clear influence of the albumin concentration on the measurements of the glucose concentration, thereby pointing to a lack of selectivity for all sensors. This influence has been modeled, and strategies to manage this selectivity challenge are inferred

Characterization of speech from amyotrophic lateral sclerosis by neuromorphic processing

Amyotrophic Lateral Sclerosis is a severe disease, which dramatically reduces the speech communication skills of patients as disease progresses. The present study is devoted to define accurate and objective estimates to characterize the loss of communication skills, to help clinicians and therapists in monitoring disease progression and in deciding on rehabilitation interventions. The methodology proposed is based on the perceptual (neuromorphic)definition of speech dinamics, concentrated in vowel sound in character and duration. We present the results from a longitudinal study carried out in an ALS patient during one year. Discussion addresses future actions

Metodologías activas para seguridad de la información en grados desde la perspectiva de ciencia de la computación

En este trabajo enmarcado en la aplicación de las tecnologías en la enseñanza superior, se estudia la incorporación de metodologías activas en diferentes asignaturas de grado relacionadas con la seguridad de la información. Estas asignaturas tienen diferencias y aspectos en común, teniendo una posible continuación en titulaciones de máster. Se coordina el diseño e implementación de los recursos y metodologías y se evalúan los resultados obtenidos, extrayendo conclusiones importantes de cara al futuro

Neuromechanical modelling of articulatory movements from surface electromyography and speech formants

Speech articulation is produced by the movements of muscles in the larynx, pharynx, mouth and face. Therefore speech shows acoustic features as formants which are directly related with neuromotor actions of these muscles. The first two formants are strongly related with jaw and tongue muscular activity. Speech can be used as a simple and ubiquitous signal, easy to record and process, either locally or on e-Health platforms. This fact may open a wide set of applications in the study of functional grading and monitoring neurodegenerative diseases. A relevant question, in this sense, is how far speech correlates and neuromotor actions are related. This preliminary study is intended to find answers to this question by using surface electromyographic recordings on the masseter and the acoustic kinematics related with the first formant. It is shown in the study that relevant correlations can be found among the surface electromyographic activity (dynamic muscle behavior) and the positions and first derivatives of the first formant (kinematic variables related to vertical velocity and acceleration of the joint jaw and tongue biomechanical system). As an application example, it is shown that the probability density function associated to these kinematic variables is more sensitive than classical features as Vowel Space Area (VSA) or Formant Centralization Ratio (FCR) in characterizing neuromotor degeneration in Parkinson’s Disease