Determinants of environmental disclosure in the annual reports of large companies operating in Portugal

Similarly to what has happened in other countries, since the early 1990s Portuguese companies have developed corporate environmental reporting practices in response to internal and external factors. This paper is based on empirical research directed to both the study of environmental reporting practices developed by Portuguese companies and the identification of the factors that explain the extent to which these companies disclose environmental information. This study focuses on the environmental disclosures made in the annual reports by a sample of 109 large firms operating in Portugal during the period 2002-04. Using the content analysis technique we have developed an index in order to assess the presence of the environmental disclosures in companies’ annual reports and their breadth. Based on the extant literature, several characteristics relating to firms’ attributes were selected and their influence on the level of environmental disclosure was tested empirically. The selected explanatory variables were firm size, industry membership, profitability, foreign ownership, quotation on the stock market and environmental certification. The results reveal that, in spite of the fact that the level of environmental information disclosed during the period 2002-04 is low, the extent of environmental disclosure has increased as well as the number of Portuguese companies that disclose environmental information. Moreover, the firm size and the fact that a company is listed on the stock market are positively related to the extent of environmental disclosure. This study adds to the international research on environmental disclosure by providing empirical data from a country, Portugal, where empirical evidence is still relatively unknown, extending the scope of the current understanding of the environmental reporting practices.

Semi-automatic 3D Segmentation Of Costal Cartilage In CT Data From Pectus Excavatum Patients

One of the current frontiers in the clinical management of Pectus Excavatum (PE) patients is the prediction of the surgical outcome prior to the intervention. This can be done through computerized simulation of the Nuss procedure, which requires an anatomically correct representation of the costal cartilage. To this end, we take advantage of the costal cartilage tubular structure to detect it through multi-scale vesselness filtering. This information is then used in an interactive 2D initialization procedure which uses anatomical maximum intensity projections of 3D vesselness feature images to efficiently initialize the 3D segmentation process. We identify the cartilage tissue centerlines in these projected 2D images using a livewire approach. We finally refine the 3D cartilage surface through region-based sparse field level-sets. We have tested the proposed algorithm in 6 noncontrast CT datasets from PE patients. A good segmentation performance was found against reference manual contouring, with an average Dice coefficient of 0.75±0.04 and an average mean surface distance of 1.69±0.30mm. The proposed method requires roughly 1 minute for the interactive initialization step, which can positively contribute to an extended use of this tool in clinical practice, since current manual delineation of the costal cartilage can take up to an hour.

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.