Möller-Barlow disease in archaeology: Preliminary study of biochemical detection

Human bone is the most direct source for reconstructing health and living conditions of ancient populations. However, many diseases remain undetected in palaeopathology. Möller-Barlow disease (scurvy) is a historically well-documented metabolic disease and must have been common in clinical and sub-clinical severity. Due to long incubation periods and the subtle nature of bone changes osteological evidence is relatively rare (Brickley & Ives 2008). Möller-Barlow disease is caused by deficiency of dietary vitamin C (ascorbic acid) and evokes symptoms like fatigue, haemorrhage, inflammations, delayed wound healing and pain. Vitamin C is a cofactor for the hydroxylation of the amino acids proline and lysine which are essential for the production of intact connective tissue by cross-linking the propeptides in collagen. In a preliminary study we tested the detectability of Möller-Barlow disease by analysis of relative quantitative variability of hydroxylated amino acids in collagen (Pendery & Koon 2013). Samples (N=9) were taken from children with (n=3, cranium, femur, tibia) and without (n=4, cranium, femur, tibia) apparent bone reactions indicative of Möller-Barlow disease, as well as from adults with lethal traumata (n=2; negative controls). The skeletal remains originated from two early medieval cemeteries from Switzerland. Gas chromatographic (GC) analysis revealed minor differences between the samples. So far children with no pathologic alterations had fairly same values as negative controls while children with bone reactions paradoxically exhibited even slightly higher values of hydroxyproline and hydroxylysine. Future research demands for larger sample size and has to discuss sampling strategies. Beside possible misdiagnosis of Möller-Barlow disease it is arguable if only the newly built bone should be analysed even though this could lead to problems related to small sample quantity. It also remains to be seen to which extent varying turnover rates of different skeletal elements, especially in children, must be taken into account.

Automatic X-ray landmark detection and shape segmentation via data-driven joint estimation of image displacements

In this paper, we propose a new method for fully-automatic landmark detection and shape segmentation in X-ray images. To detect landmarks, we estimate the displacements from some randomly sampled image patches to the (unknown) landmark positions, and then we integrate these predictions via a voting scheme. Our key contribution is a new algorithm for estimating these displacements. Different from other methods where each image patch independently predicts its displacement, we jointly estimate the displacements from all patches together in a data driven way, by considering not only the training data but also geometric constraints on the test image. The displacements estimation is formulated as a convex optimization problem that can be solved efficiently. Finally, we use the sparse shape composition model as the a priori information to regularize the landmark positions and thus generate the segmented shape contour. We validate our method on X-ray image datasets of three different anatomical structures: complete femur, proximal femur and pelvis. Experiments show that our method is accurate and robust in landmark detection, and, combined with the shape model, gives a better or comparable performance in shape segmentation compared to state-of-the art methods. Finally, a preliminary study using CT data shows the extensibility of our method to 3D data.

Early detection of psychosis: helpful or stigmatizing experience? A qualitative study

AIM Despite the large scientific debate concerning potential stigmatizing effects of identifying an individual as being in an at-risk mental state (ARMS) for psychosis, studies investigating this topic from the subjective perspective of patients are rare. This study assesses whether ARMS individuals experience stigmatization and to what extent being informed about the ARMS is experienced as helpful or harmful. METHODS Eleven ARMS individuals, currently participating in the follow-up assessments of the prospective Basel Früherkennung von Psychosen (FePsy; English: Early Detection of Psychosis) study, were interviewed in detail using a semistructured qualitative interview developed for this purpose. Data were analysed using Interpretative Phenomenological Analysis. RESULTS Most individuals experiencing first symptoms reported sensing that there was 'something wrong with them' and felt in need of help. They were relieved that a specific term was assigned to their symptoms. The support received from the early detection centre was generally experienced as helpful. Many patients reported stigmatization and discrimination that appeared to be the result of altered behaviour and social withdrawal due to the prepsychotic symptoms they experienced prior to contact with the early detection clinic. CONCLUSIONS The results suggest that early detection services help individuals cope with symptoms and potential stigmatization rather than enhancing or causing the latter. More emphasis should be put on the subjective experiences of those concerned when debating the advantages and disadvantages of early detection with regard to stigma. There was no evidence for increased perceived stigma and discrimination as a result of receiving information about the ARMS.

Detection of haemorrhagic shock: Are vital signs obsolete? A commentary by Dr M. Luginbühl

The ATLS program by the American college of surgeons is probably the most important globally active training organization dedicated to improve trauma management. Detection of acute haemorrhagic shock belongs to the key issues in clinical practice and thus also in medical teaching. (In this issue of the journal William Schulz and Ian McConachrie critically review the ATLS shock classification Table 1), which has been criticized after several attempts of validation have failed [1]. The main problem is that distinct ranges of heart rate are related to ranges of uncompensated blood loss and that the heart rate decrease observed in severe haemorrhagic shock is ignored [2]. Table 1. Estimated blood loos based on patient's initial presentation (ATLS Students Course Manual, 9th Edition, American College of Surgeons 2012). Class I Class II Class III Class IV Blood loss ml Up to 750 750–1500 1500–2000 >2000 Blood loss (% blood volume) Up to 15% 15–30% 30–40% >40% Pulse rate (BPM) <100 100–120 120–140 >140 Systolic blood pressure Normal Normal Decreased Decreased Pulse pressure Normal or ↑ Decreased Decreased Decreased Respiratory rate 14–20 20–30 30–40 >35 Urine output (ml/h) >30 20–30 5–15 negligible CNS/mental status Slightly anxious Mildly anxious Anxious, confused Confused, lethargic Initial fluid replacement Crystalloid Crystalloid Crystalloid and blood Crystalloid and blood Table options In a retrospective evaluation of the Trauma Audit and Research Network (TARN) database blood loss was estimated according to the injuries in nearly 165,000 adult trauma patients and each patient was allocated to one of the four ATLS shock classes [3]. Although heart rate increased and systolic blood pressure decreased from class I to class IV, respiratory rate and GCS were similar. The median heart rate in class IV patients was substantially lower than the value of 140 min−1 postulated by ATLS. Moreover deterioration of the different parameters does not necessarily go parallel as suggested in the ATLS shock classification [4] and [5]. In all these studies injury severity score (ISS) and mortality increased with in increasing shock class [3] and with increasing heart rate and decreasing blood pressure [4] and [5]. This supports the general concept that the higher heart rate and the lower blood pressure, the sicker is the patient. A prospective study attempted to validate a shock classification derived from the ATLS shock classes [6]. The authors used a combination of heart rate, blood pressure, clinically estimated blood loss and response to fluid resuscitation to classify trauma patients (Table 2) [6]. In their initial assessment of 715 predominantly blunt trauma patients 78% were classified as normal (Class 0), 14% as Class I, 6% as Class II and only 1% as Class III and Class IV respectively. This corresponds to the results from the previous retrospective studies [4] and [5]. The main endpoint used in the prospective study was therefore presence or absence of significant haemorrhage, defined as chest tube drainage >500 ml, evidence of >500 ml of blood loss in peritoneum, retroperitoneum or pelvic cavity on CT scan or requirement of any blood transfusion >2000 ml of crystalloid. Because of the low prevalence of class II or higher grades statistical evaluation was limited to a comparison between Class 0 and Class I–IV combined. As in the retrospective studies, Lawton did not find a statistical difference of heart rate and blood pressure among the five groups either, although there was a tendency to a higher heart rate in Class II patients. Apparently classification during primary survey did not rely on vital signs but considered the rather soft criterion of “clinical estimation of blood loss” and requirement of fluid substitution. This suggests that allocation of an individual patient to a shock classification was probably more an intuitive decision than an objective calculation the shock classification. Nevertheless it was a significant predictor of ISS [6]. Table 2. Shock grade categories in prospective validation study (Lawton, 2014) [6]. Normal No haemorrhage Class I Mild Class II Moderate Class III Severe Class IV Moribund Vitals Normal Normal HR > 100 with SBP >90 mmHg SBP < 90 mmHg SBP < 90 mmHg or imminent arrest Response to fluid bolus (1000 ml) NA Yes, no further fluid required Yes, no further fluid required Requires repeated fluid boluses Declining SBP despite fluid boluses Estimated blood loss (ml) None Up to 750 750–1500 1500–2000 >2000 Table options What does this mean for clinical practice and medical teaching? All these studies illustrate the difficulty to validate a useful and accepted physiologic general concept of the response of the organism to fluid loss: Decrease of cardiac output, increase of heart rate, decrease of pulse pressure occurring first and hypotension and bradycardia occurring only later. Increasing heart rate, increasing diastolic blood pressure or decreasing systolic blood pressure should make any clinician consider hypovolaemia first, because it is treatable and deterioration of the patient is preventable. This is true for the patient on the ward, the sedated patient in the intensive care unit or the anesthetized patients in the OR. We will therefore continue to teach this typical pattern but will continue to mention the exceptions and pitfalls on a second stage. The shock classification of ATLS is primarily used to illustrate the typical pattern of acute haemorrhagic shock (tachycardia and hypotension) as opposed to the Cushing reflex (bradycardia and hypertension) in severe head injury and intracranial hypertension or to the neurogenic shock in acute tetraplegia or high paraplegia (relative bradycardia and hypotension). Schulz and McConachrie nicely summarize the various confounders and exceptions from the general pattern and explain why in clinical reality patients often do not present with the “typical” pictures of our textbooks [1]. ATLS refers to the pitfalls in the signs of acute haemorrhage as well: Advanced age, athletes, pregnancy, medications and pace makers and explicitly state that individual subjects may not follow the general pattern. Obviously the ATLS shock classification which is the basis for a number of questions in the written test of the ATLS students course and which has been used for decades probably needs modification and cannot be literally applied in clinical practice. The European Trauma Course, another important Trauma training program uses the same parameters to estimate blood loss together with clinical exam and laboratory findings (e.g. base deficit and lactate) but does not use a shock classification related to absolute values. In conclusion the typical physiologic response to haemorrhage as illustrated by the ATLS shock classes remains an important issue in clinical practice and in teaching. The estimation of the severity haemorrhage in the initial assessment trauma patients is (and was never) solely based on vital signs only but includes the pattern of injuries, the requirement of fluid substitution and potential confounders. Vital signs are not obsolete especially in the course of treatment but must be interpreted in view of the clinical context. Conflict of interest None declared. Member of Swiss national ATLS core faculty.

Influence of contact points on the performance of caries detection methods in approximal surfaces of primary molars: an in vivo study

This in vivo study aimed to evaluate the influence of contact points on the approximal caries detection in primary molars, by comparing the performance of the DIAGNOdent pen and visual-tactile examination after tooth separation to bitewing radiography (BW). A total of 112 children were examined and 33 children were selected. In three periods (a, b, and c), 209 approximal surfaces were examined: (a) examiner 1 performed visual-tactile examination using the Nyvad criteria (EX1); examiner 2 used DIAGNOdent pen (LF1) and took BW; (b) 1 week later, after tooth separation, examiner 1 performed the second visual-tactile examination (EX2) and examiner 2 used DIAGNOdent again (LF2); (c) after tooth exfoliation, surfaces were directly examined using DIAGNOdent (LF3). Teeth were examined by computed microtomography as a reference standard. Analyses were based on diagnostic thresholds: D1: D 0 = health, D 1 –D 4 = disease; D2: D 0 , D 1 = health, D 2 –D 4 = disease; D3: D 0 –D 2 = health, D 3 , D 4 = disease. At D1, the highest sensitivity/specificity were observed for EX1 (1.00)/LF3 (0.68), respectively. At D2, the highest sensitivity/ specificity were observed for LF3 (0.69)/BW (1.00), respectively. At D3, the highest sensitivity/specificity were observed for LF3 (0.78)/EX1, EX2 and BW (1.00). EX1 showed higher accuracy values than LF1, and EX2 showed similar values to LF2. We concluded that the visual-tactile examination showed better results in detecting sound surfaces and approximal caries lesions without tooth separation. However, the effectiveness of approximal caries lesion detection of both methods was increased by the absence of contact points. Therefore, regardless of the method of detection, orthodontic separating elastics should be used as a complementary tool for the diagnosis of approximal noncavitated lesions in primary molars.

A Specific Mapping Study Using Fluorescence Sentinel Lymph Node Detection in Patients with Intermediate- and High-risk Prostate Cancer Undergoing Extended Pelvic Lymph Node Dissection.

Sentinel lymph node (SLN) detection techniques have the potential to change the standard of surgical care for patients with prostate cancer. We performed a lymphatic mapping study and determined the value of fluorescence SLN detection with indocyanine green (ICG) for the detection of lymph node metastases in intermediate- and high-risk patients undergoing radical prostatectomy and extended pelvic lymph node dissection. A total of 42 patients received systematic or specific ICG injections into the prostate base, the midportion, the apex, the left lobe, or the right lobe. We found (1) that external and internal iliac regions encompass the majority of SLNs, (2) that common iliac regions contain up to 22% of all SLNs, (3) that a prostatic lobe can drain into the contralateral group of pelvic lymph nodes, and (4) that the fossa of Marcille also receives significant drainage. Among the 12 patients who received systematic ICG injections, 5 (42%) had a total of 29 lymph node metastases. Of these, 16 nodes were ICG positive, yielding 55% sensitivity. The complex drainage pattern of the prostate and the low sensitivity of ICG for the detection of lymph node metastases reported in our study highlight the difficulties related to the implementation of SNL techniques in prostate cancer. PATIENT SUMMARY There is controversy about how extensive lymph node dissection (LND) should be during prostatectomy. We investigated the lymphatic drainage of the prostate and whether sentinel node fluorescence techniques would be useful to detect node metastases. We found that the drainage pattern is complex and that the sentinel node technique is not able to replace extended pelvic LND.

Edge Detection by Adaptive Splitting II. The Three-Dimensional Case

In Llanas and Lantarón, J. Sci. Comput. 46, 485–518 (2011) we proposed an algorithm (EDAS-d) to approximate the jump discontinuity set of functions defined on subsets of ℝ d . This procedure is based on adaptive splitting of the domain of the function guided by the value of an average integral. The above study was limited to the 1D and 2D versions of the algorithm. In this paper we address the three-dimensional problem. We prove an integral inequality (in the case d=3) which constitutes the basis of EDAS-3. We have performed detailed computational experiments demonstrating effective edge detection in 3D function models with different interface topologies. EDAS-1 and EDAS-2 appealing properties are extensible to the 3D case

Study about the Problem of Dropping of Subjects by Students During an Academinc Year at the University.

In university studies, it is not unusual for students to drop some of the subjects they have enrolled in for the academic year. They start by not attending lectures, sometimes due to neglect or carelessness, or because they find the subject too difficult, this means that they lose the continuity in the topics that the professor follows. If they try to attend again they discover that they hardly understand anything and become discouraged and so decide to give up attending lectures and study on their own. However some fail to turn up to do their final exams and the failure rate of those who actually do the exams is high. The problem is that this is not only the case with one specific subject, but it is often the same with many subjects. The result is that students arent’s productive enough, wasting time and also prolonging their years of study which entails a great cost for families. Degree courses structured to be conducted and completed in three academic courses, it may in fact take up to an average of six or more academic courses. In this paper, we have studied this problem, which apart from the waste of money and time, produces frustration in the student, who finds that he has not been able to achieve what he had proposed at the beginning of the course. It is quite common, to find students who do not even pass nor 50% of the subjects they had enrolled in for the academic year. If this happens repeatedly to a student, it can be the point when he considers dropping out altogether. This is also a concern for the universities, especially in the early courses. In our experience as professors, we have found that students, who attend lectures regularly and follow the explanations, approach the final exams with confidence and rarely fail the subject. In this proposal we present some techniques and methods carried out to solve in possible, the problem of lack of attendance to lectures. This involves "rewarding students for their assistance and participation in lectures". Rewarding assistance with a "prize" that counts for the final mark on the subject and involving more participation in the development of lectures. We believe that we have to teach students to use the lectures as part of their learning in a non-passive way. We consider the professor's work as fundamental in terms of how to convey the usefulness of these topics explained and the applications that they will have for their professional life in the future. In this way the student see for himself the use and importance of what he is learning. When his participation is required, he will feel more involved and confident participating in the educational system. Finally we present statistical results of studies carried out on different degrees and on different subjects over two consecutive years. In the first year we assessed only the final exams without considering the students attendance, or participation. In the second year, we have applied the techniques and methods proposed here. In addition we have compared the two ways of assessing subjects.

Detection and Tracking of Dynamic Objects. A MultiRobot approach to Critical Infrastructures Surveillance.

Despite that Critical Infrastructures (CIs) security and surveillance are a growing concern for many countries and companies, Multi Robot Systems (MRSs) have not been yet broadly used in this type of facilities. This dissertation presents a novel study of the challenges arisen by the implementation of this type of systems and proposes solutions to specific problems. First, a comprehensive analysis of different types of CIs has been carried out, emphasizing the influence of the different characteristics of the facilities in the design of a security and surveillance MRS. One of the most important needs for the surveillance of a CI is the detection of intruders. From a technical point of view this problem can be abstracted as equivalent to the Detection and Tracking of Mobile Objects (DATMO). This dissertation proposes algorithms to solve this specific problem in a CI environment. Using 3D range images of the environment as input data, two detection algorithms for ground robots have been developed. These detection algorithms provide a list of moving objects in the robot detection area. Direct image differentiation and computer vision techniques are used when the robot is static. Alternatively, multi-layer ground reconstructions are compared to detect the dynamic objects when the robot is moving. Since CIs usually spread over large areas, it is very useful to incorporate aerial vehicles in the surveillance MRS. Therefore, a moving object detection algorithm for aerial vehicles has been also developed. This algorithm compares the real optical flow obtained from a down-face oriented camera with an artificial optical flow computed using a RANSAC based homography matrix. Two tracking algorithms have been developed to follow the moving objects trajectories. These algorithms can efficiently handle occlusions and crossings, as well as exchange information among robots. The multirobot tracking can be applied to any type of communication structure: centralized, decentralized or a combination of both. Even more, the developed tracking algorithms are independent of the detection algorithms and could be potentially used with other detection procedures or even with static sensors, such as cameras. In addition, using the 3D point clouds available to the robots, a relative localization algorithm has been developed to improve the position estimation of a given robot with observations from other robots. All the developed algorithms have been extensively tested in different simulated CIs using the Webots robotics simulator. Furthermore, the algorithms have also been validated with real robots operating in real scenarios. In conclusion, this dissertation presents a multirobot approach to Critical Infrastructure Surveillance, mainly focusing on Detecting and Tracking Dynamic Objects.

Fluoro-sensing applied to detection and identification of hydrocarbons in inland waters. Study of the impact of different UV light sources

This paper presents the detection and identification of hydrocarbons through flu oro-sensing by developing a simple and inexpensive detector for inland water, in contrast to current systems, designed to be used for marine waters at large distances and being extremely costly. To validate the proposed system, three test-benches have been mounted, with various UV-Iight sources. Main application of this system would be detect hydrocarbons pollution in rivers, lakes or dams, which in fact, is of growing interest by administrations.

1RM prediction: a data mining case study

In the last few years there has been a heightened interest in data treatment and analysis with the aim of discovering hidden knowledge and eliciting relationships and patterns within this data. Data mining techniques (also known as Knowledge Discovery in Databases) have been applied over a wide range of fields such as marketing, investment, fraud detection, manufacturing, telecommunications and health. In this study, well-known data mining techniques such as artificial neural networks (ANN), genetic programming (GP), forward selection linear regression (LR) and k-means clustering techniques, are proposed to the health and sports community in order to aid with resistance training prescription. Appropriate resistance training prescription is effective for developing fitness, health and for enhancing general quality of life. Resistance exercise intensity is commonly prescribed as a percent of the one repetition maximum. 1RM, dynamic muscular strength, one repetition maximum or one execution maximum, is operationally defined as the heaviest load that can be moved over a specific range of motion, one time and with correct performance. The safety of the 1RM assessment has been questioned as such an enormous effort may lead to muscular injury. Prediction equations could help to tackle the problem of predicting the 1RM from submaximal loads, in order to avoid or at least, reduce the associated risks. We built different models from data on 30 men who performed up to 5 sets to exhaustion at different percentages of the 1RM in the bench press action, until reaching their actual 1RM. Also, a comparison of different existing prediction equations is carried out. The LR model seems to outperform the ANN and GP models for the 1RM prediction in the range between 1 and 10 repetitions. At 75% of the 1RM some subjects (n = 5) could perform 13 repetitions with proper technique in the bench press action, whilst other subjects (n = 20) performed statistically significant (p < 0:05) more repetitions at 70% than at 75% of their actual 1RM in the bench press action. Rate of perceived exertion (RPE) seems not to be a good predictor for 1RM when all the sets are performed until exhaustion, as no significant differences (p < 0:05) were found in the RPE at 75%, 80% and 90% of the 1RM. Also, years of experience and weekly hours of strength training are better correlated to 1RM (p < 0:05) than body weight. O'Connor et al. 1RM prediction equation seems to arise from the data gathered and seems to be the most accurate 1RM prediction equation from those proposed in literature and used in this study. Epley's 1RM prediction equation is reproduced by means of data simulation from 1RM literature equations. Finally, future lines of research are proposed related to the problem of the 1RM prediction by means of genetic algorithms, neural networks and clustering techniques. RESUMEN En los últimos años ha habido un creciente interés en el tratamiento y análisis de datos con el propósito de descubrir relaciones, patrones y conocimiento oculto en los mismos. Las técnicas de data mining (también llamadas de \Descubrimiento de conocimiento en bases de datos\) se han aplicado consistentemente a lo gran de un gran espectro de áreas como el marketing, inversiones, detección de fraude, producción industrial, telecomunicaciones y salud. En este estudio, técnicas bien conocidas de data mining como las redes neuronales artificiales (ANN), programación genética (GP), regresión lineal con selección hacia adelante (LR) y la técnica de clustering k-means, se proponen a la comunidad del deporte y la salud con el objetivo de ayudar con la prescripción del entrenamiento de fuerza. Una apropiada prescripción de entrenamiento de fuerza es efectiva no solo para mejorar el estado de forma general, sino para mejorar la salud e incrementar la calidad de vida. La intensidad en un ejercicio de fuerza se prescribe generalmente como un porcentaje de la repetición máxima. 1RM, fuerza muscular dinámica, una repetición máxima o una ejecución máxima, se define operacionalmente como la carga máxima que puede ser movida en un rango de movimiento específico, una vez y con una técnica correcta. La seguridad de las pruebas de 1RM ha sido cuestionada debido a que el gran esfuerzo requerido para llevarlas a cabo puede derivar en serias lesiones musculares. Las ecuaciones predictivas pueden ayudar a atajar el problema de la predicción de la 1RM con cargas sub-máximas y son empleadas con el propósito de eliminar o al menos, reducir los riesgos asociados. En este estudio, se construyeron distintos modelos a partir de los datos recogidos de 30 hombres que realizaron hasta 5 series al fallo en el ejercicio press de banca a distintos porcentajes de la 1RM, hasta llegar a su 1RM real. También se muestra una comparación de algunas de las distintas ecuaciones de predicción propuestas con anterioridad. El modelo LR parece superar a los modelos ANN y GP para la predicción de la 1RM entre 1 y 10 repeticiones. Al 75% de la 1RM algunos sujetos (n = 5) pudieron realizar 13 repeticiones con una técnica apropiada en el ejercicio press de banca, mientras que otros (n = 20) realizaron significativamente (p < 0:05) más repeticiones al 70% que al 75% de su 1RM en el press de banca. El ínndice de esfuerzo percibido (RPE) parece no ser un buen predictor del 1RM cuando todas las series se realizan al fallo, puesto que no existen diferencias signifiativas (p < 0:05) en el RPE al 75%, 80% y el 90% de la 1RM. Además, los años de experiencia y las horas semanales dedicadas al entrenamiento de fuerza están más correlacionadas con la 1RM (p < 0:05) que el peso corporal. La ecuación de O'Connor et al. parece surgir de los datos recogidos y parece ser la ecuación de predicción de 1RM más precisa de aquellas propuestas en la literatura y empleadas en este estudio. La ecuación de predicción de la 1RM de Epley es reproducida mediante simulación de datos a partir de algunas ecuaciones de predicción de la 1RM propuestas con anterioridad. Finalmente, se proponen futuras líneas de investigación relacionadas con el problema de la predicción de la 1RM mediante algoritmos genéticos, redes neuronales y técnicas de clustering.

Study of the effects of moving targets in SAR images for their detection.

Synthetic Aperture Radar’s (SAR) are systems designed in the early 50’s that are capable of obtaining images of the ground using electromagnetic signals. Thus, its activity is not interrupted by adverse meteorological conditions or during the night, as it occurs in optical systems. The name of the system comes from the creation of a synthetic aperture, larger than the real one, by moving the platform that carries the radar (typically a plane or a satellite). It provides the same resolution as a static radar equipped with a larger antenna. As it moves, the radar keeps emitting pulses every 1/PRF seconds —the PRF is the pulse repetition frequency—, whose echoes are stored and processed to obtain the image of the ground. To carry out this process, the algorithm needs to make the assumption that the targets in the illuminated scene are not moving. If that is the case, the algorithm is able to extract a focused image from the signal. However, if the targets are moving, they get unfocused and/or shifted from their position in the final image. There are applications in which it is especially useful to have information about moving targets (military, rescue tasks,studyoftheflowsofwater,surveillanceofmaritimeroutes...).Thisfeatureiscalled Ground Moving Target Indicator (GMTI). That is why the study and the development of techniques capable of detecting these targets and placing them correctly in the scene is convenient. In this document, some of the principal GMTI algorithms used in SAR systems are detailed. A simulator has been created to test the features of each implemented algorithm on a general situation with moving targets. Finally Monte Carlo tests have been performed, allowing us to extract conclusions and statistics of each algorithm.

A study of feature combination for vehicle detection based on image processing

Video analytics play a critical role in most recent traffic monitoring and driver assistance systems. In this context, the correct detection and classification of surrounding vehicles through image analysis has been the focus of extensive research in the last years. Most of the pieces of work reported for image-based vehicle verification make use of supervised classification approaches and resort to techniques, such as histograms of oriented gradients (HOG), principal component analysis (PCA), and Gabor filters, among others. Unfortunately, existing approaches are lacking in two respects: first, comparison between methods using a common body of work has not been addressed; second, no study of the combination potentiality of popular features for vehicle classification has been reported. In this study the performance of the different techniques is first reviewed and compared using a common public database. Then, the combination capabilities of these techniques are explored and a methodology is presented for the fusion of classifiers built upon them, taking into account also the vehicle pose. The study unveils the limitations of single-feature based classification and makes clear that fusion of classifiers is highly beneficial for vehicle verification.

Automated detection and classification of brain injury lesions on structural magnetic resonance imaging

El daño cerebral adquirido (DCA) es un problema social y sanitario grave, de magnitud creciente y de una gran complejidad diagnóstica y terapéutica. Su elevada incidencia, junto con el aumento de la supervivencia de los pacientes, una vez superada la fase aguda, lo convierten también en un problema de alta prevalencia. En concreto, según la Organización Mundial de la Salud (OMS) el DCA estará entre las 10 causas más comunes de discapacidad en el año 2020. La neurorrehabilitación permite mejorar el déficit tanto cognitivo como funcional y aumentar la autonomía de las personas con DCA. Con la incorporación de nuevas soluciones tecnológicas al proceso de neurorrehabilitación se pretende alcanzar un nuevo paradigma donde se puedan diseñar tratamientos que sean intensivos, personalizados, monitorizados y basados en la evidencia. Ya que son estas cuatro características las que aseguran que los tratamientos son eficaces. A diferencia de la mayor parte de las disciplinas médicas, no existen asociaciones de síntomas y signos de la alteración cognitiva que faciliten la orientación terapéutica. Actualmente, los tratamientos de neurorrehabilitación se diseñan en base a los resultados obtenidos en una batería de evaluación neuropsicológica que evalúa el nivel de afectación de cada una de las funciones cognitivas (memoria, atención, funciones ejecutivas, etc.). La línea de investigación en la que se enmarca este trabajo de investigación pretende diseñar y desarrollar un perfil cognitivo basado no sólo en el resultado obtenido en esa batería de test, sino también en información teórica que engloba tanto estructuras anatómicas como relaciones funcionales e información anatómica obtenida de los estudios de imagen. De esta forma, el perfil cognitivo utilizado para diseñar los tratamientos integra información personalizada y basada en la evidencia. Las técnicas de neuroimagen representan una herramienta fundamental en la identificación de lesiones para la generación de estos perfiles cognitivos. La aproximación clásica utilizada en la identificación de lesiones consiste en delinear manualmente regiones anatómicas cerebrales. Esta aproximación presenta diversos problemas relacionados con inconsistencias de criterio entre distintos clínicos, reproducibilidad y tiempo. Por tanto, la automatización de este procedimiento es fundamental para asegurar una extracción objetiva de información. La delineación automática de regiones anatómicas se realiza mediante el registro tanto contra atlas como contra otros estudios de imagen de distintos sujetos. Sin embargo, los cambios patológicos asociados al DCA están siempre asociados a anormalidades de intensidad y/o cambios en la localización de las estructuras. Este hecho provoca que los algoritmos de registro tradicionales basados en intensidad no funcionen correctamente y requieran la intervención del clínico para seleccionar ciertos puntos (que en esta tesis hemos denominado puntos singulares). Además estos algoritmos tampoco permiten que se produzcan deformaciones grandes deslocalizadas. Hecho que también puede ocurrir ante la presencia de lesiones provocadas por un accidente cerebrovascular (ACV) o un traumatismo craneoencefálico (TCE). Esta tesis se centra en el diseño, desarrollo e implementación de una metodología para la detección automática de estructuras lesionadas que integra algoritmos cuyo objetivo principal es generar resultados que puedan ser reproducibles y objetivos. Esta metodología se divide en cuatro etapas: pre-procesado, identificación de puntos singulares, registro y detección de lesiones. Los trabajos y resultados alcanzados en esta tesis son los siguientes: Pre-procesado. En esta primera etapa el objetivo es homogeneizar todos los datos de entrada con el objetivo de poder extraer conclusiones válidas de los resultados obtenidos. Esta etapa, por tanto, tiene un gran impacto en los resultados finales. Se compone de tres operaciones: eliminación del cráneo, normalización en intensidad y normalización espacial. Identificación de puntos singulares. El objetivo de esta etapa es automatizar la identificación de puntos anatómicos (puntos singulares). Esta etapa equivale a la identificación manual de puntos anatómicos por parte del clínico, permitiendo: identificar un mayor número de puntos lo que se traduce en mayor información; eliminar el factor asociado a la variabilidad inter-sujeto, por tanto, los resultados son reproducibles y objetivos; y elimina el tiempo invertido en el marcado manual de puntos. Este trabajo de investigación propone un algoritmo de identificación de puntos singulares (descriptor) basado en una solución multi-detector y que contiene información multi-paramétrica: espacial y asociada a la intensidad. Este algoritmo ha sido contrastado con otros algoritmos similares encontrados en el estado del arte. Registro. En esta etapa se pretenden poner en concordancia espacial dos estudios de imagen de sujetos/pacientes distintos. El algoritmo propuesto en este trabajo de investigación está basado en descriptores y su principal objetivo es el cálculo de un campo vectorial que permita introducir deformaciones deslocalizadas en la imagen (en distintas regiones de la imagen) y tan grandes como indique el vector de deformación asociado. El algoritmo propuesto ha sido comparado con otros algoritmos de registro utilizados en aplicaciones de neuroimagen que se utilizan con estudios de sujetos control. Los resultados obtenidos son prometedores y representan un nuevo contexto para la identificación automática de estructuras. Identificación de lesiones. En esta última etapa se identifican aquellas estructuras cuyas características asociadas a la localización espacial y al área o volumen han sido modificadas con respecto a una situación de normalidad. Para ello se realiza un estudio estadístico del atlas que se vaya a utilizar y se establecen los parámetros estadísticos de normalidad asociados a la localización y al área. En función de las estructuras delineadas en el atlas, se podrán identificar más o menos estructuras anatómicas, siendo nuestra metodología independiente del atlas seleccionado. En general, esta tesis doctoral corrobora las hipótesis de investigación postuladas relativas a la identificación automática de lesiones utilizando estudios de imagen médica estructural, concretamente estudios de resonancia magnética. Basándose en estos cimientos, se han abrir nuevos campos de investigación que contribuyan a la mejora en la detección de lesiones. ABSTRACT Brain injury constitutes a serious social and health problem of increasing magnitude and of great diagnostic and therapeutic complexity. Its high incidence and survival rate, after the initial critical phases, makes it a prevalent problem that needs to be addressed. In particular, according to the World Health Organization (WHO), brain injury will be among the 10 most common causes of disability by 2020. Neurorehabilitation improves both cognitive and functional deficits and increases the autonomy of brain injury patients. The incorporation of new technologies to the neurorehabilitation tries to reach a new paradigm focused on designing intensive, personalized, monitored and evidence-based treatments. Since these four characteristics ensure the effectivity of treatments. Contrary to most medical disciplines, it is not possible to link symptoms and cognitive disorder syndromes, to assist the therapist. Currently, neurorehabilitation treatments are planned considering the results obtained from a neuropsychological assessment battery, which evaluates the functional impairment of each cognitive function (memory, attention, executive functions, etc.). The research line, on which this PhD falls under, aims to design and develop a cognitive profile based not only on the results obtained in the assessment battery, but also on theoretical information that includes both anatomical structures and functional relationships and anatomical information obtained from medical imaging studies, such as magnetic resonance. Therefore, the cognitive profile used to design these treatments integrates information personalized and evidence-based. Neuroimaging techniques represent an essential tool to identify lesions and generate this type of cognitive dysfunctional profiles. Manual delineation of brain anatomical regions is the classical approach to identify brain anatomical regions. Manual approaches present several problems related to inconsistencies across different clinicians, time and repeatability. Automated delineation is done by registering brains to one another or to a template. However, when imaging studies contain lesions, there are several intensity abnormalities and location alterations that reduce the performance of most of the registration algorithms based on intensity parameters. Thus, specialists may have to manually interact with imaging studies to select landmarks (called singular points in this PhD) or identify regions of interest. These two solutions have the same inconvenient than manual approaches, mentioned before. Moreover, these registration algorithms do not allow large and distributed deformations. This type of deformations may also appear when a stroke or a traumatic brain injury (TBI) occur. This PhD is focused on the design, development and implementation of a new methodology to automatically identify lesions in anatomical structures. This methodology integrates algorithms whose main objective is to generate objective and reproducible results. It is divided into four stages: pre-processing, singular points identification, registration and lesion detection. Pre-processing stage. In this first stage, the aim is to standardize all input data in order to be able to draw valid conclusions from the results. Therefore, this stage has a direct impact on the final results. It consists of three steps: skull-stripping, spatial and intensity normalization. Singular points identification. This stage aims to automatize the identification of anatomical points (singular points). It involves the manual identification of anatomical points by the clinician. This automatic identification allows to identify a greater number of points which results in more information; to remove the factor associated to inter-subject variability and thus, the results are reproducible and objective; and to eliminate the time spent on manual marking. This PhD proposed an algorithm to automatically identify singular points (descriptor) based on a multi-detector approach. This algorithm contains multi-parametric (spatial and intensity) information. This algorithm has been compared with other similar algorithms found on the state of the art. Registration. The goal of this stage is to put in spatial correspondence two imaging studies of different subjects/patients. The algorithm proposed in this PhD is based on descriptors. Its main objective is to compute a vector field to introduce distributed deformations (changes in different imaging regions), as large as the deformation vector indicates. The proposed algorithm has been compared with other registration algorithms used on different neuroimaging applications which are used with control subjects. The obtained results are promising and they represent a new context for the automatic identification of anatomical structures. Lesion identification. This final stage aims to identify those anatomical structures whose characteristics associated to spatial location and area or volume has been modified with respect to a normal state. A statistical study of the atlas to be used is performed to establish which are the statistical parameters associated to the normal state. The anatomical structures that may be identified depend on the selected anatomical structures identified on the atlas. The proposed methodology is independent from the selected atlas. Overall, this PhD corroborates the investigated research hypotheses regarding the automatic identification of lesions based on structural medical imaging studies (resonance magnetic studies). Based on these foundations, new research fields to improve the automatic identification of lesions in brain injury can be proposed.