%0 Journal Article
%T Implementation of a Low-Cost Mobile Devices to Support Medical Diagnosis
%A Carlos Garc¨ªa S¨¢nchez
%A Guillermo Botella Juan
%A Ferm¨ªn Ayuso M¨¢rquez
%A Diego Gonz¨¢lez Rodr¨ªguez
%A Manuel Prieto-Mat¨ªas
%A Francisco Tirado Fern¨¢ndez
%J Computational and Mathematical Methods in Medicine
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/287089
%X Medical imaging has become an absolutely essential diagnostic tool for clinical practices; at present, pathologies can be detected with an earliness never before known. Its use has not only been relegated to the field of radiology but also, increasingly, to computer-based imaging processes prior to surgery. Motion analysis, in particular, plays an important role in analyzing activities or behaviors of live objects in medicine. This short paper presents several low-cost hardware implementation approaches for the new generation of tablets and/or smartphones for estimating motion compensation and segmentation in medical images. These systems have been optimized for breast cancer diagnosis using magnetic resonance imaging technology with several advantages over traditional X-ray mammography, for example, obtaining patient information during a short period. This paper also addresses the challenge of offering a medical tool that runs on widespread portable devices, both on tablets and/or smartphones to aid in patient diagnostics. 1. Introduction Medical imaging [1] as a diagnostic technique in medicine requires complex image analysis of image sequences obtained by a plethora of variety, such as ECG, X-ray, MRI, ultrasound, CT, and so forth. Magnetic resonance imaging (MRI) [2] technology is one of the most promising tools over other methods, like conventional X-ray mammography, regarding breast cancer diagnosis. Nowadays, X-ray images still have a higher spatial resolution than MR images, but this technique has the advantages of producing natural tridimensional images and being able to noninvasively monitor the contrast agent concentration in the depicted tissue over time. On other hand, motion estimation is still an open problem with important applications to medical imaging. Attending to the estimation of a pixel motion inside the image sequence, there are many models and algorithms that could be classified as belonging to the matching domain approximations [3], energy models [4], and gradient models [5]. Related to this last family, different studies [6¨C8] show that this represents an admissible choice for keeping a tolerable trade-off between accuracy and computing resources. For designing systems operating efficiently, many challenges must be dealt with, such as robustness, static patterns, illumination changes, different kinds of noise, contrast invariance, and so forth. Regarding the hardware platform used, the portable industry in recent years has dramatically increased the importance of the processing hardware elements. The iPhone 3GS offered more
%U http://www.hindawi.com/journals/cmmm/2013/287089/