Inspecting X-ray images is an essential aspect of medical diagnosis. However, due to an X-ray's low contrast and low dynamic range, important aspects such as organs, bones, and nodules become difficult to identify. Hence, contrast adjustment is critical, especially because of its ability to enhance the details in both bright and dark regions. For X-ray image enhancement, we therefore propose a new concept based on component attenuation. Notably, we assumed an X-ray image could be decomposed into tissue components and important details. Since tissues may not be the major primary focus of an X-ray, we proposed enhancing the visual contrast by adaptive tissue attenuation and dynamic range stretching. Via component decomposition and tissue attenuation, a parametric adjustment model was deduced to generate many enhanced images at once. Finally, an ensemble framework was proposed for fusing these enhanced images and producing a high-contrast output in both bright and dark regions. We have used measurement metrics to evaluate our system and achieved promising scores in each. An online testing system was also built for subjective evaluation. Moreover, we applied our system to an X-ray data set provided by the Japanese Society of Radiological Technology to help with nodule detection. The experimental results of which demonstrated the effectiveness of our method.

In this paper, we discuss a similarity inconsistency phenomenon where the radio signal strength (RSS) signatures of two neighboring positions are dissimilar due to the RSS variation. While matching an observed RSS throughout the radio map, the phenomenon would lead to a jagged similarity distribution. This may break the similarity assumption of the previous works. To address the problem, we proposed a multi-dimensional kernel density estimation (MDKDE) method. By introducing the spatial kernel, the method could adopt neighboring information to enrich the fingerprint. The model can also help to generate a smooth and consistent similarity distribution. Moreover, we formulated the searching of the target location over the continuous domain as an optimization problem. Instead of estimating the optimal location numerically, we also came up with an efficient tracking method, weighted average tracker (WAT). Upon the MDKDE model, WAT can track the target in a simple weighted average method. The experimental results have demonstrated that the proposed system could well model the RSS variation and provide robust positioning performance in an efficient manner.

Conventional Wi-Fi-based indoor localization methods rely on training a RSS fingerprint model to predict user locations. Most fingerprinting models only consider the distribution of RSS (radio signal strength) at a location and ignore the relationship between adjacent locations. Another challenging issue is the RSS inconsistency problem where the RSSs of neighboring locations are not as similar as the ideal expectation. To address these problems, we suggest well utilizing the richer regional features rather than the raw RSSs. Thereby, we proposed a deep learning network which integrates three components: the One-Dimension-Convolutional Neural Network to extract regional RSS features, the Siamese architecture to handle the similarity inconsistency problem, and the Regression network for user positioning. Our experiments present promising results compared with the state-of-art methods.

Pulmonary edema, i.e. excess of extravascular fluid in lungs, is a common manifestation of various clinical conditions. Although the etiology of the pulmonary edema is deduced with the help of history, physical examination and various biochemical and radiological investigations, computer aided evaluation of pulmonary edema will be helpful for physicians in determining the course of management for the condition. In this study we present texture analysis of chest x-ray, using Gabor filter and one of the machine learning techniques, Support Vector Machine (SVM), to distinguish the normal chest x-ray from the chest x-ray of pulmonary edema. This is an initial step towards computer aided quantitative assessment of the pulmonary edema using chest x-ray.