This work presents a systematic review of techniques for the 3D automatic detection of pulmonary nodules in computerized-tomography (CT) images. Its main goals are to analyze the latest technology being used for the development of computational diagnostic tools to assist in the acquisition, storage and, mainly, processing and analysis of the biomedical data. Also, this work identifies the progress made, so far, evaluates the challenges to be overcome and provides an analysis of future prospects. As far as the authors know, this is the first time that a review is devoted exclusively to automated 3D techniques for the detection of pulmonary nodules from lung CT images, which makes this work of noteworthy value. The research covered the published works in the Web of Science, PubMed, Science Direct and IEEEXplore up to December 2014. Each work found that referred to automated 3D segmentation of the lungs was individually analyzed to identify its objective, methodology and results. Based on the analysis of the selected works, several studies were seen to be useful for the construction of medical diagnostic aid tools. However, there are certain aspects that still require attention such as increasing algorithm sensitivity, reducing the number of false positives, improving and optimizing the algorithm detection of different kinds of nodules with different sizes and shapes and, finally, the ability to integrate with the Electronic Medical Record Systems and Picture Archiving and Communication Systems. Based on this analysis, we can say that further research is needed to develop current techniques and that new algorithms are needed to overcome the identified drawbacks.

Some tools for detecting similarity, such as Sherlock, compare textual documents of any nature, but have limitations to compare source code files. The presence or absence of blank spaces between structure elements, variable names, among other actions interfere with the similarity index found. This paper evidences that the preprocessing of the source code improves Sherlock' performance. The results are based on experiments conducted with 66 source code previously plagiarized, and a base formed by 2160 codes created by students of engineering courses in programming classes. In this last set, the situation of similarity was not previously known, so a method was created to calculate precision and recall, in a relative way, based on a set of reference tools, as a kind of oracle. Our approach, called Sherlock N-overlap obtained, in most of the cases tested, similarity indexes superior to other complex tools such as MOSS, JPlag and SIM.

This paper proposes using virtual reality to enhance the perception of actions by distant users on a shared application. Here, distance may refer either to space (e.g. in a remote synchronous collaboration) or time (e.g. during playback of recorded actions). Our approach consists in immersing the application in a virtual inhabited 3D space and mimicking user actions by animating avatars. We illustrate this approach with two applications, the one for remote collaboration on a shared application and the other to playback recorded sequences of user actions. We suggest this could be a low cost enhancement for telepresence.

In 3D reconstruction applications, an important issue is the matching of point clouds corresponding to different perspectives of a particular object or scene, which is addressed by the use of variants of the Iterative Closest Point (ICP) algorithm. In this work, we introduce a cloud-partitioning strategy for improved registration and compare it to other relevant approaches by using both time and quality of pose correction. Quality is assessed from a rotation metric and also by the root mean square error (RMSE) computed over the points of the source cloud and the corresponding closest ones in the corrected target point cloud. A wide and plural set of experimentation scenarios was used to test the algorithm and assess its generalization, revealing that our cloud-partitioning approach can provide a very good match in both indoor and outdoor scenes, even when the data suffer from noisy measurements or when the data size of the source and target models differ significantly. Furthermore, in most of the scenarios analyzed, registration with the proposed technique was achieved in shorter time than those from the literature.