Radiologists' workload has been steadily increasing for decades. As digital technology matures it improves the workflow for radiology departments and decreases the time necessary to examine patients. Computer systems are widely used in health care and are for example used to view radiology images. To simplify this, display protocols based on examination data are used to automatically create a layout and hang images for the user. To cover a wide variety of examinations hundreds of protocols must be created, which is a time-consuming task and the system can still fail to hang series if strict requirements on the protocols are not met. To remove the need for this manual step we propose to use machine learning based on past manually corrected presentations. The classifiers are trained on the metadata in the examination and how the radiologist preferred to hang the series. The chosen approach was to create classifiers for different layout rules and then use these predictions in an algorithm for assigning series types to individual image slots according to categories based on metadata, similar to how display protocol works. The resulting presentations shows that the system is able to learn, but must increase its prediction accuracy if it is to be used commercially. Analyses of the different parts show that increased accuracy in early steps should improve overall success. / Röntgenläkares arbetsbörda har under flera årtionden ökat. Den digitala sjukvårdsteknologin utvecklas ständigt vilket bidrar till ett förbättrat arbetsflöde och kortare undersökningstider i radiologiavdelningar. Datorsystem används idag överallt inom sjukvården och används bland annat för att visa bilder åt röntgenläkare. För att underlätta visningen används display protocol som automatiskt skapar layouts och hänger bilder åt användaren. För att täcka ett stort antal olika undersökningstyper krävs att användaren skapar hundratals protokoll vilket är en tidskrävande uppgift, och systemet kan ändå misslyckas med att hänga upp bilder om de strikta kraven protokollen ställer inte uppfylls. För att ta bort detta manuella steg föreslår vi att man använder maskininlärning baserat på tidigare sparade presentationer.  Klassificerarna tränas på undersökningens metadata och radiologens preferenser på hängning av serier. Den valda metoden går ut på att skapa klassificerare för olika layout-regler och att sedan använda deras output i en algoritm som placerar ut series-typer till individuella bildplatser enligt kategorier baserade på metadata. Denna metod liknar den process de nuvarande display protokollen utför. De presentationer som skapats visar att systemet kan läras upp, men kräver högre precision om det ska användas kommersiellt. Analys av de olika delarna tyder på att ökad precision tidigt i systemet skulle öka den totala precision.

The ubiquity and speed of Internet access led over the past decade to an exponential increase in the use of thin clients and cloud computing, both taking advantage of the ability to remotely provide computing resources. The work investigates the role of remote display Protocol in behavioral biometric systems based on free-text keystroke dynamics. Authentication based on keystroke dynamics is easy in use, cheap, invisible for user and does not require any additional sensor.I n this project I will investigate how network characteristics affect the keystroke dynamics pattern in remote desktop scenario. Objectives: The aim of this project is to investigate the role of remote display Protocol in behavioral biometric system based on free-text keystroke dynamics, by measuring how network characteristics influence the computation of keystroke pattern in Virtual Desktop Infrastructure (VDI). Method: This thesis will answer all of its research question with the help of a Systematic Literature Review (SLR) and an Experiment. Literature review was conducted to gather information about the keystroke dynamics analysis, the applied algorithms and their performance; and to clarify the controlled changes of networking performance in VDI based scenario. Using the acquired knowledge, implemented keystroke dynamics pattern algorithm based on Euclidian distance statistical method, designed an experiment and performed a series of tests, in order to identify the influence of remote display protocol to keystroke pattern. Results: Through the SLR, keystroke dynamics analysis working structure is identified and illustrated, essential elements are summarized, and a statistical approach based on Euclidian distance is described; a technique to simulate and measure networklatency in VDI scenario is described including essential elements and parameters of VDI testbed. Keystroke analysis algorithm, dataset replication code and VDItestbed are implemented. The controlled experiment provided measurements of the metrics of the algorithm and network performance mentioned in objectives. Conclusions: During experimentation, I found that timing pattern in the keystroke dynamics data is affected by VDI in normal network conditions by 12% in average. Higher latency standard deviation, jitter, packet loss as well as remote display protocol overheads have a significant combined impact onto keystroke pattern. Moreover I found what maximum possible delay values does not affect keystroke pattern in a larger extent.