Medical Image Retrieval using Bag of Meaningful Visual Words: Unsupervised visual vocabulary pruning with PLSA

Content--based medical image retrieval has been proposed as a technique that allows not only for easy access to images from the relevant literature and electronic health records but also for training physicians, for research and clinical decision support. The bag-of-visual-words approach is a widely used technique that tries to shorten the semantic gap by learning meaningful features from the dataset and describing documents and images in terms of the histogram of these features. Visual vocabularies are often redundant, over--complete and noisy. Larger than required vocabularies lead to high--dimensional feature spaces, which present important disadvantages with the curse of dimensionality and computational cost being the most obvious ones. In this work a visual vocabulary pruning technique is presented. It enormously reduces the amount of required words to describe a medical image dataset with no significant effect on the accuracy. Results show that a reduction of up to 90% can be achieved without impact on the system performance. Obtaining a more compact representation of a document enables multimodal description as well as using classifiers requiring low--dimensional representations

Retrieval of high-dimensional visual data: current state, trends and challenges ahead

Information retrieval algorithms have changed the way we manage and use various data sources, such as images, music or multimedia collections. First, free text information of documents from varying sources became accessible in addition to structured data in databases, initially for exact search and then for more probabilistic models. Novel approaches enable content-based visual search of images using computerized image analysis making visual image content searchable without requiring high quality manual annotations. Other multimedia data followed such as video and music retrieval, sometimes based on techniques such as extracting objects and classifying genre. 3D (surface) objects and solid textures have also been produced in quickly increasing quantities, for example in medical tomographic imaging. For these two types of 3D information sources, systems have become available to characterize the objects or textures and search for similar visual content in large databases. With 3D moving sequences (i.e., 4D), in particular medical imaging, even higher-dimensional data have become available for analysis and retrieval and currently present many multimedia retrieval challenges. This article systematically reviews current techniques in various fields of 3D and 4D visual information retrieval and analyses the currently dominating application areas. The employed techniques are analysed and regrouped to highlight similarities and complementarities among them in order to guide the choice of optimal approaches for new 3D and 4D retrieval problems. Opportunities for future applications conclude the article. 3D or higher-dimensional visual information retrieval is expected to grow quickly in the coming years and in this respect this article can serve as a basis for designing new applications

Generational Portrait of Spanish Society in the Face of Climate Change. A Question to Consider for the Green Economy under the Well-Being Approach

Climate change is emerging as an issue of progressive attention, and therefore awareness, in societies. In this work, the problem is addressed from a generational perspective in Spanish society and is carried out from the approaches of awareness, human action, and self-responsibility. All this from the search of the subjective well-being and the citizens' happiness, as one of the bases of sustainable development initiatives. With data from the European Social Survey R8, from EUROSTAT, we work in two phases: (1) descriptive and inferential on possible associations of the items with the variable Age, and (2) calculation of probabilities between groups through logistic regression. The results confirm a general awareness, but with apparent statistical differences between age groups. In general, the youngest are the most aware, blame human activity most intensely, are the most concerned, and are the most willing to act. And it is the older people who are less aware of all these issues. Based on this finding, and from the approach mentioned above, it is recommended that leaders, both in the macroeconomic and microeconomic sectors, develop initiatives that sensitize and encourage older age groups

Semi–Supervised Learning for Image Modality Classification

Searching for medical image content is a regular task for many physicians, especially in radiology. Retrieval of medical images from the scientific literature can benefit from automatic modality classification to focus the search and filter out non–relevant items. Training datasets are often unevenly distributed regarding the classes resulting sometimes in a less than optimal classification performance. This article proposes a semi–supervised learning approach applied using a k–Nearest Neighbour (k–NN) classifier to exploit unlabelled data and to expand the training set. The algorithmic implementation is described and the method is evaluated on the ImageCLEFmed modality classification benchmark. Results show that this approach achieves an improved performance over supervised k–NN and Random Forest classifiers. Moreover, medical case–based retrieval benefits from the modality filter

Crowdsourcing for Medical Image Classification

To help manage the large amount of biomedical images produced, image information retrieval tools have been developed to help access the right information at the right moment. To provide a test bed for image retrieval evaluation, the ImageCLEFmed benchmark proposes a biomedical classification task that automatically focuses on determining the image modality of figures from biomedical journal articles. In the training data for this machine learning task, some classes have many more images than others and thus a few classes are not well represented, which is a challenge for automatic image classification. To address this problem, an automatic training set expansion was first proposed. To improve the accuracy of the automatic training set expansion, a manual verification of the training set is done using the crowdsourcing platform Crowdflower. This platform allows the use of external persons to pay for the crowdsourcing or to use personal contacts free of charge. Crowdsourcing requires strict quality control or using trusted persons but it can quickly give access to a large number of judges and thus improve many machine learning tasks. Results show that the manual annotation of a large amount of biomedical images carried out in this project can help with image classification

Sistema de Recuperación de Imágenes 3D para cirugía reparadora

Este trabajo desarrolla un método para la búsqueda y selección de un implante para la sustitución de una estructura anatómica dañada. Para ello se ha implementado un sistema de recuperación de imágenes en 3D basado en contenido, por el cual a partir del tratamiento de imágenes radiológicas tridimensionales del paciente, se obtiene una lista de los posibles casos de una base de datos disponible, ordenados por la similitud con el original. El sistema está basado en la extracción de características locales a lo largo de cada modelo, para su incorporación a una base de datos con un motor de búsqueda que selecciona los puntos de afinidad entre el modelo buscado y los propuestos. Posteriormente se realiza una selección de aquellos que comparten, además, características globales