Breast cancer is the most prevalent cancer in the world and the fifth-leading cause of cancer-related death. Treatment is effective in the early stages. Thus, a need to screen considerable portions of the population is crucial. When the screening procedure uncovers a suspect lesion, a biopsy is performed to assess its potential for malignancy. This procedure is usually performed using real-time Ultrasound (US) imaging. This work proposes a visualization system for US breast biopsy. It consists of an application running on AR glasses that interact with a computer application. The AR glasses track the position of QR codes mounted on an US probe and a biopsy needle. US images are shown in the user’s field of view with enhanced lesion visualization and needle trajectory. To validate the system, latency of the transmission of US images was evaluated. Usability assessment compared our proposed prototype with a traditional approach with different users. It showed that needle alignment was more precise, with 92.67 ± 2.32° in our prototype versus 89.99 ± 37.49° in a traditional system. The users also reached the lesion more accurately. Overall, the proposed solution presents promising results, and the use of AR glasses as a tracking and visualization device exhibited good performance.

Chest X-ray (CXR) images help specialists worldwide to diagnose lung diseases, such as tuberculosis and COVID-19. A primary step in an image-based diagnostic tool is to segment the region of interest. That facilitates the disease classification problem by reducing the amount of information to be processed. However, due to the noisy nature of CXRs, identifying the lung region can be a challenging task. This paper addresses the lung segmentation problem using a less costable computational process based on image analysis and mathematical morphology techniques. The proposed method achieved a specificity of 92.92%, a Jaccard index of 77.77%, and a Dice index of 87.37% on average. All images that comprehend the dataset used and their respective ground truths are available for download at https://github.com/mnzluiza/Lung-Segmentation.

Radiografias são ferramentas indispensáveis para auxílio ao diagnóstico médico. Ainda que outras técnicas estejam disponíveis, a radiografia é um exame acessível, de rápida aquisição e utilizado em larga escala. Este trabalho apresenta um método de segmentação automatizada das áreas pulmonares em radiografias torácicas e para tal, faz uso de técnicas de processamento de imagens simples. Disponibilizamos publicamente as segmentações desenvolvidas; resultados são comparados aos de outras técnicas disponíveis na literatura e podem ser utilizados como entrada em sistemas de auxílio ao diagnóstico.