Utilização do mapa espacial de radiação e dose acumulada como ferramenta para otimização de doses em pacientes e trabalhadores nas clínicas de medicina nuclear

This study has as general aim to propose a spatial map of doses as an auxiliary tool in assessing the need for optimization of the workplace in nuclear medicine services. As specific aims, we assessed the workers individual dosimetry; we analyzed the facilities of the nuclear medicine services; and we evaluated environment exposure rates. The research is characterized as a case study, with an exploratory and explanatory nature. It was conducted in three Nuclear Medicine Services, all established in the Northwest of the Paraná State. Results indicated that the evaluated dose rates and workers dosimetry, in all the dependencies of the surveyed services, are within the limits of annual doses. However some exceeded the limits recommended in the standard CNEN-NN 3:01 (2014). It was concluded that the spatial map dose is an important tool for nuclear medicine services because it facilitates the visualization of areas with highest concentration of radiation, and also helps in the constant review of these measures and resources, aiding in the identification of any failures and shortcomings, providing resources to correct any issues and prevent their repetition. The spatial map dose is also important for the regular inspection, evaluating if the radiation protection objectives are being met.

A contribution to semantic description of images and videos: an application of soft biometrics

Humans have a high ability to extract visual data information acquired by sight. Trought a learning process, which starts at birth and continues throughout life, image interpretation becomes almost instinctively. At a glance, one can easily describe a scene with reasonable precision, naming its main components. Usually, this is done by extracting low-level features such as edges, shapes and textures, and associanting them to high level meanings. In this way, a semantic description of the scene is done. An example of this, is the human capacity to recognize and describe other people physical and behavioral characteristics, or biometrics. Soft-biometrics also represents inherent characteristics of human body and behaviour, but do not allow unique person identification. Computer vision area aims to develop methods capable of performing visual interpretation with performance similar to humans. This thesis aims to propose computer vison methods which allows high level information extraction from images in the form of soft biometrics. This problem is approached in two ways, unsupervised and supervised learning methods. The first seeks to group images via an automatic feature extraction learning , using both convolution techniques, evolutionary computing and clustering. In this approach employed images contains faces and people. Second approach employs convolutional neural networks, which have the ability to operate on raw images, learning both feature extraction and classification processes. Here, images are classified according to gender and clothes, divided into upper and lower parts of human body. First approach, when tested with different image datasets obtained an accuracy of approximately 80% for faces and non-faces and 70% for people and non-person. The second tested using images and videos, obtained an accuracy of about 70% for gender, 80% to the upper clothes and 90% to lower clothes. The results of these case studies, show that proposed methods are promising, allowing the realization of automatic high level information image annotation. This opens possibilities for development of applications in diverse areas such as content-based image and video search and automatica video survaillance, reducing human effort in the task of manual annotation and monitoring.