This study introduces MCOF, a multi-camera, computer vision-based system designed to assist visually impaired individuals with mobility on university campuses. The system operates locally and achieves the following performance metrics: (i) detecting body points within 1.2. 10–2seconds, (ii) identifying people, objects, and animals in 2.4. 10–2seconds, and (iii) detecting movements in 5.1. 10−2seconds. These results were obtained using a GTX 1,660 GPU with up to 6 cameras (or a 6,112MB stream) running concurrently. According to the MCOF architecture, events trigger tickets that are sent to an external information system, which can then implement its own safety and personnel protocols. Additionally, MCOF includes modules to handle electrical and network failures and features an obstruction detection routine for the cameras.

As recent research indicates that virtual games are beneficial to the player's brain health, games have been used to aid aging-related disease treatment. There are also studies that imply that sensory effects improve user's Quality of Experience. Combining both concepts, this paper proposes the Stroop Game, a cognitive game inspired by the Stroop test, which uses light sensory effects. The Stroop Game was developed for the Brazilian Digital TV System using the Ginga-NCL middleware, which implements the ITU H.761 standard. Evaluating the Stroop Game implementation with elderly users, it indicates that the game proposed in this paper was well received by the users and also that the light sensory effect makes it more attractive to them.

Breast cancer is the second most common cancer in the world. Early diagnosis and treatment increase the patient's chances of healing. The temperature of cancerous tissues is generally higher than that of healthy neighbouring tissues, making thermography an option to be considered in screening strategies for this type of cancer. In this paper, we propose a computational method for breast Dynamic Infrared Thermography images analysis for screening patients with abnormalities in the breast, using supervised and unsupervised machine learning techniques. An abnormality may be a benign tumor or a malignant tumor (cancer). As performance measure, we use the area under ROC curve, sensitivity, specificity and accuracy. The best results are achieved by K-Star classifier, obtaining an accuracy equal to 98.57%. The results confirm the potential of the proposed method for screening patients with abnormalities in the breast.

Medical images are important elements for the diagnosis of diseases. Computer Aided Diagnostic has evolved in recent years along with the processing capacity of computers as well as the emergence of new computational techniques. Segmentation is a valuable approach for identifying a specific area in human body images, such as the lungs and heart. This work proposes an algorithm to segment anatomical structures using parallel 3D region growing. Experiments using different Computer Tomography scans show that the proposed approach can run 150 times faster than the typical sequential region growing algorithm while providing good results in the identification of the target region.

Electronic medical records (EMRs) are highly sensitive information shared among peers to keep up-to-date patient history. Providing security, privacy, and availability to these sensitive data is a challenge because, typically, after data publication the patient loses control over them. In this paper, we propose a blockchain-based approach to secure EMR for healthcare applications, where access control is patient-centric. Our proposal keeps encrypted EMRs in the blockchain, and the patient shares the decryption key only with healthcare professionals in which he/she trusts. Blockchain allows untrusted node, in a distributed peer-to-peer network to correctly and verifiably interact with each other, without any reliable intermediary. We investigate the scalability of our approach through simulations. Results show that it scales well since increasing the number of nodes in the network implies a linear increase in the size of the stored chain. Results also reveal that the time for inserting a new EMR in the blockchain remains low even when the number of nodes in the network increases.

Aging is a worldwide phenomenon and represents a growing concern for public health systems. In this context, neurodegenerative diseases like Alzheimer's Disease (AD) have a high prevalence among the elderly. Early diagnosis of AD allows early treatment and improves patient's quality of life. In this paper, we propose a clinical decision support system (CDSS) to aid physicians in diagnosis of AD and related disorders: Dementia (D) and Mild Cognitive Impairment (MCI). For each case, the system exhibits the most probable diagnosis, the most relevant health records and unobserved health records that could confirm such diagnosis. Moreover, the system has the ability to refine its decision model using the final diagnosis reported by physicians. The system can offer to physicians a friendly user interface designed for smartphones. The proposed decision support system is flexible and adaptable to different contexts, since it allows new neuropsychological tests to be included and its decision model to be adapted automatically. Clinical cases from Center for Alzheimer's Disease (CAD) of Federal University of Rio de Janeiro (UFRJ) were used as the training dataset for the proposed supervised learning method. Preliminary tests using clinical cases from Antônio Pedro University Hospital (HUAP) of Fluminense Federal University (UFF) showed that the proposed CDSS decision model achieves good performance (accuracy of 0.94 for D and 0.85 for MCI) by a computational method that evaluates several classifiers and selects the best for each mental disorder.

The worldwide aging phenomenon is a growing concern. Alzheimer's disease (AD) has a high prevalence in the elderly. In this paper, we present a clinical decision support system for aiding the diagnosis of AD and related disorders. We describe system's main components and architecture, which is based on a mobile web-based platform. Its predictive model is based on Bayesian networks designed considering AD diagnosis criteria, trained and tested with the patient database of the Center for Alzheimer's Disease and Related Disorder at the Institute of Psychiatry of the Federal University of Rio de Janeiro, Brazil. Patient database attributes are composed by predisposal factors, demographic data, assessment scales, symptoms and signs. When the system indicates a patient diagnosis, it provides: the most probable diagnosis, health data that lead to such diagnosis and, in case of low certainty factor, unobserved health data that should be collected to confirm or refuse the initial diagnostic hypothesis. Preliminary usability tests indicate potential use of the system in clinical practice.

This paper addresses the image registration problem applying genetic algorithms. The image registration's objective is the definition of a mapping that best match two set of points or images. In this work the point matching problem was addressed employing a method based on nearest-neighbor. The mapping was handled by affine transformations. Experiments were conducted using three 2D synthetic point-sets with different affine transformations and noise. The results were compared against other optimization techniques. The similarity of two point-sets is measured using the Euclidean distance between matched points.

The non-invasive in vivo nature of magnetic resonance imaging (MRI) makes it the modality of choice of many neuroanatomical imaging studies. This paper discusses automatic brain structure segmentation based on anatomic atlas. Our goal is to use image-processing algorithms and previous knowledge statistical models for segmentation and labeling of brain regions in order to support radiologists to make clinical diagnosis. Practical experiments show the results of brain tissue classification process and automatic region labeling in order to segment accurately the hippocampus and measure its volume. Hippocampus volumetric information can be useful to evaluate patients with Alzheimer's disease. The final goal of this work is computer-aided diagnosis for brain diseases.