Marcelo Gattass

A mass classification using spatial diversity approaches in mammography images for false positive reduction

Abstract Breast cancer is configured as a public health problem that affects mainly women population. One of the main ways of prevention is through screening mammography. The interpretation made by the physician is a repetitive task because of a low contrast image and the examination of several exams. So, computer systems have been proposed to aid detection step and helps physician, with the aim to increase sensitivity at the same time that reduces invasive procedures. Although these systems had improved the sensitivity of the original examination of mammography, they also generate a lot of false positives. This paper presents a methodology for reducing false positives by analyzing the diversity of approaches with improved spatial decomposition. After experiments the results reaches a high level of sensitivity at the same time promote a high rate of reduction of false positives.

ENVIRON: visualization of CAD models in a virtual reality environment

This paper presents ENVIRON (ENvironment for VIRtual Objects Navigation), an application that was developed motivated by the necessity of using Virtual Reality in large industrial engineering models coming from CAD (Computer Aided Design) tools. This work analyzes the main problems related to the production of a VR model, derived from the CAD model.

Finding solutions for effective collaboration in a heterogeneous industrial scenario

This work introduces a model that categorizes computer-supported collaboration in different levels, according to its support for communication, coordination, cooperation and interoperability. This model is useful for defining the level of collaboration needed in real industrial scenarios or the level that is possible to achieve with the available technology, resources and working culture. These ideas are illustrated by collaborative tools in the field of computer graphics being developed for a large oil and gas company.

Indoor localization using SLAM in parallel with a natural marker detector

Indoor localization poses is a challenge to computer vision research, since one may not make use of GPS-based devices. A classic approach commonly used in museums, research institutes, etc, is the use of fiducial marker to track the users position. However, this approach is intrusive into the ambient and not always possible. A possible solution would be natural marker detection, but algorithms for this, such as SURF, have not yet achieved real-time performance. A promising approach is a Visual Simultaneous Localization and Mapping (VSLAM) algorithm, which, starting from a known position, is capable of generating a map of the surrounding environment in portable systems. The problem of SLAM algorithms is theirs error accumulation that builds up during the movement. This work presents an algorithm to locate 3D positions in non-instrumented indoor environments using a web camera. We define a hybrid approach, using a pattern-recognition method to reinitialize whenever possible a VSLAM algorithm. An implementation of the proposed algorithm use well-known computer vision algorithms, such as SURF and Davisons SLAM. In addition, tests were made on datasets from walks inside a room. Results indicate that our approach is better than a fiducial marker tracking and pure SLAM tracking in our test environment.

Breast cancer detection in mammography using spatial diversity, geostatistics, and concave geometry

Breast cancer is a global health problem which mainly affects the female population. It is known that early detection increases the chances of effective treatment, improving the disease prognosis. It remains a challenge to detect the lesion with high detection rate and ensure, at the same time, low rates of false positives . Aiming this objective, this work proposes an efficient method for detection of mass regions on digitized mammograms though diversity analysis, geostatistical and concave geometry (Alpha Shapes). We evaluate the detection rate for each feature extraction using Support Vector Machine in MIAS and DDSM database, with 74 and 621 mammograms, respectively, all containing at least one mass region. The obtained results are promising, reaching 97.30% of detection rate and 0.89 false positive per image for MIAS database and also 91.63% of detection rate and 0.86 false positive per image for DDSM database. Specifically, in DDSM obtaining high detection rate and low rate of false positives when using concave geometry to extract features in a large database.

Informatics and Computerized Tomography Aiding Detection and Diagnosis of Solitary Lung Cancer

Aristofanes Correa Silva1, Anselmo Cardoso Paiva2, Rodolfo Acatauassu Nunes3 and Marcelo Gattass4 1,2Federal University of Maranhao, Applied Computing Group NCA/UFMA, Av. dos Portugueses, S/N, Campus do Bacanga, Bacanga, CEP 65085-580, Sao Luis MA 3State University of Rio de Janeiro UERJ, Sao Francisco de Xavier, 524, Maracana, CEP 20550-900, Rio de Janeiro, RJ 4Pontiphical Catholic University of Rio de Janeiro PUC-Rio, R. Sao Vicente, 225, Gavea, CEP 22453-900, Rio de Janeiro, RJ Brazil