Responsabilidade Financeira e Criminal, o livro (Recensão)

“Responsabilidade Financeira e Criminal § Direitos Constitucionais Sociais, Dinheiros Públicos e Recuperação de Activos”, com Prefácio do Sr. Prof. Associado com Agregação, da Faculdade de Direito da Universidade de Coimbra, Doutor Jónatas E.M. Machado, é o título e tema do nosso último livro jurídico dado à estampa pela chancela da Editora Juruá, Curitiba e Lisboa, 2015. Livro o qual está disponível em Portugal nas livrarias Almedina ou em inúmeras livrarias do Brasil. Ou ainda através da loja virtual daquela que é uma das maiores editoras do mundo em língua portuguesa, a Editora Juruá que tem mais de 46 anos de vida: www.jurua.com.br § "Financial Responsibility and Criminal § Constitutional Social Rights, Public Moneys and Assets Recovery" with Prof. Mr. Preface Associated with aggregation, the Law Faculty of the University of Coimbra, Doctor Jónatas E.M. Machado is the title and theme of our last legal book given to the press by the seal of Editora Jurua, Curitiba and Lisbon, 2015. Book which is available in Portugal Almedina in bookstores or in several bookstores in Brazil. Or through the online store of what is one of the largest publishers in the world in English, Editora Jurua which has more than 46 years: www.jurua.com.br

Computer-aided recognition of dental implants in X-ray images

Dental implant recognition in patients without available records is a time-consuming and not straightforward task. The traditional method is a complete user-dependent process, where the expert compares a 2D X-ray image of the dental implant with a generic database. Due to the high number of implants available and the similarity between them, automatic/semi-automatic frameworks to aide implant model detection are essential. In this study, a novel computer-aided framework for dental implant recognition is suggested. The proposed method relies on image processing concepts, namely: (i) a segmentation strategy for semi-automatic implant delineation; and (ii) a machine learning approach for implant model recognition. Although the segmentation technique is the main focus of the current study, preliminary details of the machine learning approach are also reported. Two different scenarios are used to validate the framework: (1) comparison of the semi-automatic contours against implant’s manual contours of 125 X-ray images; and (2) classification of 11 known implants using a large reference database of 601 implants. Regarding experiment 1, 0.97±0.01, 2.24±0.85 pixels and 11.12±6 pixels of dice metric, mean absolute distance and Hausdorff distance were obtained, respectively. In experiment 2, 91% of the implants were successfully recognized while reducing the reference database to 5% of its original size. Overall, the segmentation technique achieved accurate implant contours. Although the preliminary classification results prove the concept of the current work, more features and an extended database should be used in a future work.