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Technological advance and its influence on organisations, their performance and their responses to the stakeholders has been the subject of continuous research. It entirely affects such organisations' human capital, which will require being innovative and proactive. This can be achieved through happy human resources at work. Happiness and more efficient results constitute a well-argued binomial throughout the literature. Efficiency in organisations has been addressed, among other diverse approaches, by establishing specific standards that have ensured the overall quality of their production/performance system. These standards are reflected into different certifications, extended and internationally recognised as quality symbols (ISO, TQM, etc.). However, and although all of them include human resource management, there is still no regulation at that level dedicated to the quality of performances by achieving a happy staff on their tasks. This work aims to reduce this gap, proposing the Certification Happiness Management (CHM).

Segmentation of the various parts of the brain is a challenging area in medical imaging and it is a prerequisite for many image analysis tasks useful for clinical research. Advances have been made in generating brain image templates that can be registered to automatically segment regions of interest in the human brain. However, these methods may fail with some subjects if there is a significant shape distortion or difference from the proposed models. This is also the case of newborns, where the developing brain strongly differs from adult magnetic resonance imaging (MRI) templates. In this article, a texture-based cerebellum segmentation method is described. The algorithm presented does not use any prior spatial knowledge to segment the MRI images. Instead, the system learns the texture features by means of a multi-scale filtering and visual words feature aggregation. Visual words are a commonly used technique in image retrieval. Instead of using visual features directly, the features of specific regions are modeled (clustered) into groups of discriminative features. This means that the final feature space can be reduced in size and also that the visual words in local regions are really discriminative for the given data set. The system is currently trained and tested with a dataset of 18 adult brain MRIs. An extension to the use with newborn brain images is being foreseen as this could highlight the advantages of the proposed technique. Results show that the use of texture features can be valuable for the task described and can lead to good results. The use of visual words can potentially improve robustness of existing shape-based techniques for cases with significant shape distortion or other differences from the models. As the visual words based techniques are not assuming any prior knowledge such techniques could be used for other types of segmentations as well using a large variety of basic visual features.