A comprehensive approach for the efficient acquisition and processing of hyperspectral images and sequence

Programa Oficial de Doctorado en Computación. 5009P01[Abstract] Despite the scientific and technological developments achieved during the last two decades in the hyperspectral field, some methodological, operational and conceptual issues have restricted the progress, promotion and popular dissemination of this technology. These shortcomings include the specialized knowledge required for the acquisition of hyperspectral images, the shortage of publicly accessible hyperspectral image repositories with reliable ground truth images or the lack of methodologies that allow for the adaptation of algorithms to particular user or application processing needs. The work presented here has the objective of contributing to the hyperspectral field with procedures for the automatic acquisition of hyperspectral scenes, including the hardware adaptation of our own imagers and the development of methods for the calibration and correction of the hyperspectral datacubes, the creation of a publicly available hyperspectral repository of well categorized and labeled images and the design and implementation of novel computational intelligence based processing techniques that solve typical issues related to the segmentation and denoising of hyperspectral images as well as sequences of them taking into account their temporal evolution.[Resumen] A pesar de los desarrollos tecnológicos y científicos logrados en el campo hiperespectral durante las dos últimas décadas, alg\mas limitaciones de tipo metodológico, operacional y conceptual han restringido el progreso, difusión y popularización de esta tecnología, entre ellas, el conocimiento especializado requerido en la adquisición de imágenes hiperespectrales, la carencia de repositorios de imágenes hiperespectrales con etiquetados fiables y de acceso público o la falta de metodologías que posibiliten la adaptación de algoritmos a usuarios o necesidades de procesamiento concretas. Este trabajo doctoral tiene el objetivo de contribuir al campo hiperespectral con procedimientos para la adquisición automática de escenas hiperespectrales, incluyendo la adaptación hardware de cámaras hiperespectrales propias y el desarrollo de métodos para la calibración y corrección de cubos de datos hiperespectrales; la creación de un repositorio hiperespectral de acceso público con imágenes categorizadas y con verdades de terreno fiables; y el diseño e implementación de técnicas de procesamiento basadas en inteligencia computacional para la resolución de problemas típicamente relacionados con las tareas de segmentación y eliminación de ruido en imágenes estáticas y secuencias de imágenes hiperespectrales teniendo en consideración su evolución temporal.[Resumo] A pesar dos desenvolvementos tecnolóxicos e científicos logrados no campo hiperespectral durante as dúas últimas décadas, algunhas lirrútacións de tipo metodolóxico¡ operacional e conceptual restrinxiron o progreso) difusión e popularización desta tecnoloxía, entre elas, o coñecemento especializado requirido na adquisición de imaxes hiperespectrales¡ a carencia de repositorios de irnaxes hiperespectrales con etiquetaxes fiables e de acceso público ou a falta de metodoloxías que posibiliten a adaptación de algoritmos a usuarios ou necesidades de procesamento concretas. Este traballo doutoral ten o obxectívo de contribuir ao campo hiperespectral con procedementos para a adquisición automática de eicenas hiperespectrais, incluíndo a adaptación hardware de cámaras hiperespectrales propias e o desenvolvemento de métodos para a calibración e corrección de cubos de datos hiperespectrais; a creación dun repositorio hiperespectral de acceso público con imaxes categorizadas e con verdades de terreo fiables; e o deseño e implementación de técnicas de procesamento baseadas en intelixencia computacional para a resolución de problemas tipicamente relacionado~ coas tarefas de segmentación e eliminación de ruído en imaxes estáticas e secuencias de imaxes hiperespectrai~ tendo en consideración a súa evolución temporal

An Approach for the Customized High-Dimensional Segmentation of Remote Sensing Hyperspectral Images

Abstract: This paper addresses three problems in the field of hyperspectral image segmentation: the fact that the way an image must be segmented is related to what the user requires and the application; the lack and cost of appropriately labeled reference images; and, finally, the information loss problem that arises in many algorithms when high dimensional images are projected onto lower dimensional spaces before starting the segmentation process. To address these issues, the Multi-Gradient based Cellular Automaton (MGCA) structure is proposed to segment multidimensional images without projecting them to lower dimensional spaces. The MGCA structure is coupled with an evolutionary algorithm (ECAS-II) in order to produce the transition rule sets required by MGCA segmenters. These sets are customized to specific segmentation needs as a function of a set of low dimensional training images in which the user expresses his segmentation requirements. Constructing high dimensional image segmenters from low dimensional training sets alleviates the problem of lack of labeled training images. These can be generated online based on a parametrization of the desired segmentation extracted from a set of examples. The strategy has been tested in experiments carried out using synthetic and real hyperspectral images, and it has been compared to state-of-the-art segmentation approaches over benchmark images in the area of remote sensing hyperspectral imaging.Ministerio de Economía y competitividad; TIN2015-63646-C5-1-RMinisterio de Economía y competitividad; RTI2018-101114-B-I00Xunta de Galicia: ED431C 2017/1

Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis

Cancer is one of the most detrimental diseases globally. Accordingly, the prognosis prediction of cancer patients has become a field of interest. In this review, we have gathered 43 stateof- the-art scientific papers published in the last 6 years that built cancer prognosis predictive models using multimodal data. We have defined the multimodality of data as four main types: clinical, anatomopathological, molecular, and medical imaging; and we have expanded on the information that each modality provides. The 43 studies were divided into three categories based on the modelling approach taken, and their characteristics were further discussed together with current issues and future trends. Research in this area has evolved from survival analysis through statistical modelling using mainly clinical and anatomopathological data to the prediction of cancer prognosis through a multi-faceted data-driven approach by the integration of complex, multimodal, and high-dimensional data containing multi-omics and medical imaging information and by applying Machine Learning and, more recently, Deep Learning techniques. This review concludes that cancer prognosis predictive multimodal models are capable of better stratifying patients, which can improve clinical management and contribute to the implementation of personalised medicine as well as provide new and valuable knowledge on cancer biology and its progression

Portable Oxygen Therapy: Is the 6-Minute Walking Test Overestimating the Actual Oxygen Needs?

The appropriate titration for the personalized oxygen needs of patients with chronic obstructive pulmonary disease (COPD) and severe hypoxemia is a determining factor in the success of long-term oxygen therapy. There are no standardized procedures to assist in determining the patient's needs during the physical activities of daily life. Despite that effort tests are a wide broad approach, further research concerning the development of protocols to titrate O-2 therapy is needed. The main objective of this study was to assess whether the level of oxygen titrated through the 6-minute walking test (6MWT) for patients with COPD and exertional hypoxemia is adequate to meet the patients' demand during their activities of daily living. Physiological and subjective variables were estimated for a study population during two walking tests: a 6MWT and a 20-minute walking circuit (20MWC), designed ad-hoc to reproduce daily physical activities more truthfully. The results indicate that in a significant proportion of patients, the 6MWT might not accurately predict their oxygen needs at a domiciliary environment. Therefore, the titration of the portable O-2 therapy could not be optimal in these cases, with the detrimental impact on the patient's health (hyperoxia episodes), the autonomy of the oxygen device, and the decrease of time out of the home