Continuous improvement of image quality is one of the priorities in medical imaging. Therefore, development of a simulation tool allowing to generate realistic images would be of great value to understand better the impact of the components on the image quality metrics and to choose imaging set-ups or new design features to optimize output of existing systems and to prototype new ones and to formalize the link between objective and subjective image quality metrics. Therefore, the purpose of this project, was to contribute to adaptation and validation of an existing simulator for simulation of C-arm X-ray imaging. Firstly, the study of the existing simulation tool was performed to choose further development axes. Afterwards, preliminary estimations of simulation complexity by evaluating the number of photons for a given imaging examination were performed. Previous studies[1] showed the determining impact of focal spot on imaging performance (reducing the limiting spatial frequency in common examination conditions) of X-ray interventional imaging systems.  Therefore, the work focused on the improvements of source model, in particular realistic focal spot was defined and simulations of images with close-to-real sharpness were performed and compared to experimentally acquired images. Finally, a part of this project was dedicated to scatter study. An experimental set-up and "scatter map" analysis were designed to determine the scatter evolution as function of imaging field-of-view.  First simulations were also performed. [1] Samei, E., Ranger, N., MacKenzie, A., Honey, I., Dobbins, J. and Ravin, C. (2008). Detector or System? Extending the Concept of Detective Quantum Efficiency to Characterize the Performance of Digital Radiographic Imaging Systems 1. Radiology, 249(3), pp.926-937.

Microcirculation is defined as the blood flow in the smallest blood vessels. Laser speckle contrast imaging (LSCI) is a full field imaging technique that provides instantaneous 2-D perfusion maps of illuminated tissues based on speckle contrast. Perimed’s Perfusion Speckle Imager (PSI) is a medical device developed at Perimed AB that exploits LSCI to measure tissue blood perfusion. In this thesis work, a robust Autofocus (AF) algorithm for PSI was implemented. AF is a procedure to drive PSI camera to reach the depth of focus and acquire sharp images, that relies only on signal processing. Therefore, several Blind image sharpness assessment (BISA) methods, to judge the degree of image sharpness, were compared to choose which BISA method to incorporate in the algorithm. An optimized focus scanning technique was implemented to more efficiently find the depth of focus. When working with LSCI, speckle is a source of noise that destroys image content. Experiments were performed to study laser speckle filtration: digital filters were employed to attenuate the speckle noise that corrupted details in the acquired images. Finally, two methods to perform AF were provided. These procedures were proven practically with LED images. However, with laser source image information is corrupted by speckle despite the application of digital filters and AF remains a real challenge. Moreover, important hardware limitations require to be overcome to make the technique real-time. Focus motor speed should be higher to acquire images at different focus positions faster which could benefit the speed of the AF procedure and speckle filtration.

The ability of humans to perceive significant pattern and structure of an image is something which humans take for granted. We can recognize objects and patterns independent of changes in image contrast and illumination. In the past decades, it has been widely recognized in both biology and computer vision that phase contains critical information in characterizing the structures in images. Despite the importance of local phase information and its significant success in many computer vision and image processing applications, the coherence behavior of local phases at scale-space is not well understood. This thesis concentrates on developing an invariant image representation method based on local phase information. In particular, considerable effort is devoted to study the coherence relationship between local phases at different scales in the vicinity of image features and to develop robust methods to measure the strength of this relationship. A computational framework that computes local phase coherence (LPC) intensity with arbitrary selections in the number of coefficients, scales, as well as the scale ratios between them has been developed. Particularly, we formulate local phase prediction as an optimization problem, where the objective function computes the closeness between true local phase and the predicted phase by LPC. The proposed framework not only facilitates flexible and reliable computation of LPC, but also broadens the potentials of LPC in many applications. We demonstrate the potentials of LPC in a number of image processing applications. Firstly, we have developed a novel sharpness assessment algorithm, identified as LPC-Sharpness Index (LPC-SI), without referencing the original image. LPC-SI is tested using four subject-rated publicly-available image databases, which demonstrates competitive performance when compared with state-of-the-art algorithms. Secondly, a new fusion quality assessment algorithm has been developed to objectively assess the performance of existing fusion algorithms. Validations over our subject-rated multi-exposure multi-focus image database show good correlations between subjective ranking score and the proposed image fusion quality index. Thirdly, the invariant properties of LPC measure have been employed to solve image registration problem where inconsistency in intensity or contrast patterns are the major challenges. LPC map has been utilized to estimate image plane transformation by maximizing weighted mutual information objective function over a range of possible transformations. Finally, the disruption of phase coherence due to blurring process is employed in a multi-focus image fusion algorithm. The algorithm utilizes two activity measures, LPC as sharpness activity measure along with local energy as contrast activity measure. We show that combining these two activity measures result in notable performance improvement in achieving both maximal contrast and maximal sharpness simultaneously at each spatial location.

The ability of humans to perceive significant pattern and structure of an image is something which humans take for granted. We can recognize objects and patterns independent of changes in image contrast and illumination. In the past decades, it has been widely recognized in both biology and computer vision that phase contains critical information in characterizing the structures in images. Despite the importance of local phase information and its significant success in many computer vision and image processing applications, the coherence behavior of local phases at scale-space is not well understood. This thesis concentrates on developing an invariant image representation method based on local phase information. In particular, considerable effort is devoted to study the coherence relationship between local phases at different scales in the vicinity of image features and to develop robust methods to measure the strength of this relationship. A computational framework that computes local phase coherence (LPC) intensity with arbitrary selections in the number of coefficients, scales, as well as the scale ratios between them has been developed. Particularly, we formulate local phase prediction as an optimization problem, where the objective function computes the closeness between true local phase and the predicted phase by LPC. The proposed framework not only facilitates flexible and reliable computation of LPC, but also broadens the potentials of LPC in many applications. We demonstrate the potentials of LPC in a number of image processing applications. Firstly, we have developed a novel sharpness assessment algorithm, identified as LPC-Sharpness Index (LPC-SI), without referencing the original image. LPC-SI is tested using four subject-rated publicly-available image databases, which demonstrates competitive performance when compared with state-of-the-art algorithms. Secondly, a new fusion quality assessment algorithm has been developed to objectively assess the performance of existing fusion algorithms. Validations over our subject-rated multi-exposure multi-focus image database show good correlations between subjective ranking score and the proposed image fusion quality index. Thirdly, the invariant properties of LPC measure have been employed to solve image registration problem where inconsistency in intensity or contrast patterns are the major challenges. LPC map has been utilized to estimate image plane transformation by maximizing weighted mutual information objective function over a range of possible transformations. Finally, the disruption of phase coherence due to blurring process is employed in a multi-focus image fusion algorithm. The algorithm utilizes two activity measures, LPC as sharpness activity measure along with local energy as contrast activity measure. We show that combining these two activity measures result in notable performance improvement in achieving both maximal contrast and maximal sharpness simultaneously at each spatial location.