Abstract
Reading room conditions such as illumination, ambient light, human factors and display luminance, play an important role on how radiologists analyze and interpret images. Indeed, serious diagnostic errors can appear when observing images through everyday monitors. Typically, these occur whenever professionals are ill-positioned with respect to the display or visualize images under improper light and luminance conditions. In this work, we show that virtual reality can assist radiodiagnostics by considerably diminishing or cancel out the effects of unsuitable ambient conditions. Our approach combines immersive head-mounted displays with interactive surfaces to support professional radiologists in analyzing medical images and formulating diagnostics. We evaluated our prototype with two senior medical doctors and four seasoned radiology fellows. Results indicate that our approach constitutes a viable, flexible, portable and cost-efficient option to traditional radiology reading rooms.

Abstract

Abstract

Abstract
In this article, a D-shaped photonic crystal fiber based surface plasmon resonance sensor is proposed for refractive index sensing. Surface plasmon resonance effect between surface plasmon polariton modes and fiber core modes of the designed D-shaped photonic crystal fiber is used to measure the refractive index of the analyte. By using finite element method, the sensing properties of the proposed sensor are investigated, and a very high average sensitivity of 7700 nm/RIU with the resolution of 1.30 × 10-5 RIU is obtained for the analyte of different refractive indices varies from 1.43 to 1.46. In the proposed sensor, the analyte and coating of gold are placed on the plane surface of the photonic crystal fiber, hence there is no necessity of the filling of voids, thus it is gentle to apply and easy to use. © 2016, Springer Science+Business Media New York.

Abstract

Abstract
Clinical decisions are sometimes based on a variety of patient's information such as: age, weight or information extracted from image exams, among others. Depending on the nature of the disease or anatomy, clinicians can base their decisions on different image exams like mammographies, positron emission tomography scans or magnetic resonance images. However, the analysis of those exams is far from a trivial task. Over the years, the use of image descriptors-computational algorithms that present a summarized description of image regions-became an important tool to assist the clinician in such tasks. This paper presents an overview of the use of image descriptors in healthcare contexts, attending to different image exams. In the making of this review, we analyzed over 70 studies related to the application of image descriptors of different natures-e.g., intensity, texture, shape-in medical image analysis. Four imaging modalities are featured: mammography, PET, CT and MRI. Pathologies typically covered by these modalities are addressed: breast masses and microcalcifications in mammograms, head and neck cancer and Alzheimer's disease in the case of PET images, lung nodules regarding CTs and multiple sclerosis and brain tumors in the MRI section.