<p>What is meant with print quality, what makes people perceive the quality of an image in a certain way? An inquiry was made about what the parameters are that strongly affect the perception of digital printed images. </p><p>A subjective test and some measurements make the basis for the thesis. The goal was to find a tool to predict perceived image quality when investigating the connections between the subjective test and the measurements. </p><p>Some suitable images were chosen, with a variety of motifs. A test panel consisting of people that are used to observe image quality answered questions about the perception of the quality. Measurements were made on a special test form to get information about the six different printers used in the investigation. </p><p>One of the discoveries was made when two images with the same colorful motif were compared. The first image got a much higher grade for general quality than the second image, even though the second image was printed with a printer that had a larger color gamut. The reason of this is that the first image consists of more saturated colors, and the second image has more details. The human eye perceives the more saturated image to be better than the image with more details. Another discovery was the correlation between the perceived general quality of a colored image and the perceived color gamut. One conclusion was that a great difference between two calculated color gamuts resulted in a large difference in perception of the color gamuts. A discovery of an image with very few colors and many glossy surfaces was that print mottle and sharpness are strictly connected to the general quality.</p>

The focus of this thesis is the improvement of acquisition and processing of Mueller matrix polarimetry using a confocal scanning laser ophthalmoscope (CSLO) and the application of Mueller matrix polarimetry to image the retina. Stepper motors were incorporated into a CSLO to semi-automate Mueller matrix polarimetry and were used in retinal image acquisition. Success rates of Fourier transform based edge detection filters, designed to improve the registration of retinal images, were compared. The acquired polarimetry images were used to reassess 2 image quality enhancement techniques, Mueller matrix reconstruction (MMR) and Stokes vector reconstruction (SVR), focusing on the role of auto-contrasting or normalization within the techniques and the degree to which auto-contrasting or normalization is responsible for image quality improvement of the resulting images. Mueller matrix polarimetry was also applied to find the retardance image of a malaria infected retinal blood vessel imaged in a confocal scanning laser microscope (CSLM) to visualize hemozoin within the vessel. Image quality enhancement techniques were also applied and image quality improvement was quantified for this blood vessel. The semi-automation of Mueller matrix polarimetry yielded a significant reduction in experimental acquisition time (80%) and a non-significant reduction in registration time (44%). A larger sample size would give higher power and this result might become significant. The reduction in registration time was most likely due to less movement of the eye, particularly in terms of decreased rotation seen between registered images. Fourier transform edge detection methods increased the success rate of registration from 73.9% to 92.3%. Assessment of the 2 MMR images (max entropy and max signal-to-noise ratio (SNR)) showed that comparison to the best CSLO images (not auto-contrasted) yielded significant average image quality improvements of 158% and 4% when quantified with entropy and SNR, respectively. When compared to best auto-contrasted CSLO images, significant image quality improvements were 11% and 5% for entropy and SNR, respectively. Images constructed from auto-contrasted input images were of significantly higher quality than images reconstructed from original images. Of the 2 other images assessed (modified degree of polarization (DOPM) and the first element of the Stokes vector (S0)), DOPM and S0 yielded significant average image quality improvements quantified by entropy except for the DOPM image of the RNFL. SNR was not improved significantly when either SVR image was compared to the best CSLO images. Compared to the best auto-contrasted CSLO images, neither DOPM nor S0 improved average image quality significantly. This result might change with a larger number of participants. When MMR were applied to images of malaria infected retinal slides, image quality was improved by 19.7% and 15.3% in terms of entropy and SNR, respectively, when compared to the best CSLO image. The DOPM image yielded image quality improvements of 8.6% and -24.3% and the S0 image gave improvements of 9.5% and 9.4% in entropy and SNR, respectively. Although percent increase in image quality was reduced when images were compared to initial auto-contrasted CSLO images, the final image quality was improved when auto-contrasting occurred prior to polarimetry calculations for max SNR and max entropy images. Quantitative values of retardance could not be found due to physical constraints in the CSLM that did not allow for characterization of its polarization properties and vibrational noise. Mueller matrix polarimetry used to find the retardance image of a malaria infected retina sample did yield visualization of hemozoin within the vessel but only qualitatively. In conclusion, improvements in the acquisition and registration of CSLO images were successful in leading to considerably shorter experimentation and processing times. In terms of polarimetric image quality improvement techniques, when compared to the best CSLO image. A large proportion of the improvement was in fact due to partially or completely stretching the pixel values across the dynamic range of the images within the algorithm of each technique. However, in general the image quality was still improved by the Mueller matrix reconstruction techniques using both entropy and SNR to generate the CSLO retinal images and the CSLM imaged malaria infected sample. In the malaria sample, retinal blood vessel visualization was also qualitatively improved. The images yielded from Mueller matrix polarimetry applied to a malaria infected retinal sample localized hemozoin within the blood vessel, but a quantitative image of the phase retardance could not be achieved.

Techniques from the theory of partial differential equations are often used to design filter methods that are locally adapted to the image structure. These techniques are usually used in the investigation of gray-value images. The extension to color images is non-trivial, where the choice of an appropriate color space is crucial. The RGB color space is often used although it is known that the space of human color perception is best described in terms of non-euclidean geometry, which is fundamentally different from the structure of the RGB space. Instead of the standard RGB space, we use a simple color transformation based on the theory of finite groups. It is shown that this transformation reduces the color artifacts originating from the diffusion processes on RGB images. The developed algorithm is evaluated on a set of real-world images, and it is shown that our approach exhibits fewer color artifacts compared to state-of-the-art techniques. Also, our approach preserves details in the image for a larger number of iterations. / Original Publication:Åström Freddie, Felsberg Michael and Lenz Reiner, Color Persistent Anisotropic Diffusion of Images, 2011, Image Analysis, SCIA conference, 23-27 May 2011, Ystad Sweden, 262-272.http://dx.doi.org/10.1007/978-3-642-21227-7_25Copyright: Springer

Measurement of visual quality is crucial for various image and video processing applications. It is widely applied in image acquisition, media transmission, video compression, image/video restoration, etc. The goal of image quality assessment (QA) is to develop a computable quality metric which is able to properly evaluate image quality. The primary criterion is better QA consistency with human judgment. Computational complexity and resource limitations are also concerns in a successful QA design. Many methods have been proposed up to now. At the beginning, quality measurements were directly taken from simple distance measurements, which refer to mathematically signal fidelity, such as mean squared error or Minkowsky distance. Lately, QA was extended to color space and the Fourier domain in which images are better represented. Some existing methods also consider the adaptive ability of human vision. Unfortunately, the Video Quality Experts Group indicated that none of the more sophisticated metrics showed any great advantage over other existing metrics. This thesis proposes a general approach to the QA problem by evaluating image information entropy. An information theoretic model for the human visual system is proposed and an information theoretic solution is presented to derive the proper settings. The quality metric is validated by five subjective databases from different research labs. The key points for a successful quality metric are investigated. During the testing, our quality metric exhibits excellent consistency with the human judgments and compatibility with different databases. Other than full reference quality assessment metric, blind quality assessment metrics are also proposed. In order to predict quality without a reference image, two concepts are introduced which quantitatively describe the inter-scale dependency under a multi-resolution framework. Based on the success of the full reference quality metric, several blind quality metrics are proposed for five different types of distortions in the subjective databases. Our blind metrics outperform all existing blind metrics and also are able to deal with some distortions which have not been investigated.

Sammanfattning Bakgrund Stråldosen vid en datortomografiundersökning (CT-undersökning) kan vara 100 gånger större än vid en konventionell röntgenundersökning [1]. På 10 år har antalet CT-undersökningar nästan fördubblats [2], vilket också innebär att stråldosen till patienterna ökar [1]. Bröstkörtlarna är ett av de mest strålkänsliga organen och är därför viktiga att skydda [3, 4]. Material som dämpar röntgenstrålning kraftigt och som lämpar sig för strålskydd är t.ex. bly och vismut [5]. Vismutskydd är ett tungt, metalliskt grundämne som absorberar röntgenstrålning och därmed reducerar stråldosen till vävnaderna under skyddet [6, 7]. Syfte Syftet med studien är att genom en litteraturstudie undersöka vismuts strålskyddande effekt för bröstvävnad och eventuell påverkan på bildkvalitén. Metod Artikelsökningarna gjordes i databasen PubMed med sökorden Radiation protection, Reduced radiation dosage, Radiography, Bismuth, Breasts, Bismuth shielding och image quality i olika kombinationer. Totalt valdes åtta artiklar ut. Resultat Samtliga studier har kommit fram till att vismutskyddet ensamt [8, 9, 10, 11] eller tillsammans med automatisk exponeringskontroll (AEC) reducerar stråldosen till brösten [7, 8, 12, 13, 14]. Fem studier hävdar också att bildkvalitén inte påverkas nämnvärt [7, 10, 12, 13, 14] medan två av studierna visar på en brusökning [8, 9]. Slutsats Vismutskyddet har en stråldosreducerande effekt men det bidrar även till sämre bildkvalité. Studien visar att skyddet ska användas med försiktighet och om inte tekniken utvecklas så att mAs-värdet (milliAmperesekund) lokalt över brösten kan reduceras, rekommenderas användandet av AEC istället. Nyckelord: Vismut, datortomografi, bröst, bildkvalité och stråldosreducering.

Measurement of visual quality is crucial for various image and video processing applications. It is widely applied in image acquisition, media transmission, video compression, image/video restoration, etc. The goal of image quality assessment (QA) is to develop a computable quality metric which is able to properly evaluate image quality. The primary criterion is better QA consistency with human judgment. Computational complexity and resource limitations are also concerns in a successful QA design. Many methods have been proposed up to now. At the beginning, quality measurements were directly taken from simple distance measurements, which refer to mathematically signal fidelity, such as mean squared error or Minkowsky distance. Lately, QA was extended to color space and the Fourier domain in which images are better represented. Some existing methods also consider the adaptive ability of human vision. Unfortunately, the Video Quality Experts Group indicated that none of the more sophisticated metrics showed any great advantage over other existing metrics. This thesis proposes a general approach to the QA problem by evaluating image information entropy. An information theoretic model for the human visual system is proposed and an information theoretic solution is presented to derive the proper settings. The quality metric is validated by five subjective databases from different research labs. The key points for a successful quality metric are investigated. During the testing, our quality metric exhibits excellent consistency with the human judgments and compatibility with different databases. Other than full reference quality assessment metric, blind quality assessment metrics are also proposed. In order to predict quality without a reference image, two concepts are introduced which quantitatively describe the inter-scale dependency under a multi-resolution framework. Based on the success of the full reference quality metric, several blind quality metrics are proposed for five different types of distortions in the subjective databases. Our blind metrics outperform all existing blind metrics and also are able to deal with some distortions which have not been investigated.

Since its introduction in 2004, the Structural Similarity (SSIM) index has gained widespread popularity as an image quality assessment measure. SSIM is currently recognized to be one of the most powerful methods of assessing the visual closeness of images. That being said, the Mean Squared Error (MSE), which performs very poorly from a perceptual point of view, still remains the most common optimization criterion in image processing applications because of its relative simplicity along with a number of other properties that are deemed important. In this thesis, some necessary tools to assist in the design of SSIM-optimal algorithms are developed. This work combines theoretical developments with experimental research and practical algorithms. The description of the mathematical properties of the SSIM index represents the principal theoretical achievement in this thesis. Indeed, it is demonstrated how the SSIM index can be transformed into a distance metric. Local convexity, quasi-convexity, symmetries and invariance properties are also proved. The study of the SSIM index is also generalized to a family of metrics called normalized (or M-relative) metrics. Various analytical techniques for different kinds of SSIM-based optimization are then devised. For example, the best approximation according to the SSIM is described for orthogonal and redundant basis sets. SSIM-geodesic paths with arclength parameterization are also traced between images. Finally, formulas for SSIM-optimal point estimators are obtained. On the experimental side of the research, the structural self-similarity of images is studied. This leads to the confirmation of the hypothesis that the main source of self-similarity of images lies in their regions of low variance. On the practical side, an implementation of local statistical tests on the image residual is proposed for the assessment of denoised images. Also, heuristic estimations of the SSIM index and the MSE are developed. The research performed in this thesis should lead to the development of state-of-the-art image denoising algorithms. A better comprehension of the mathematical properties of the SSIM index represents another step toward the replacement of the MSE with SSIM in image processing applications.

Visual interpretation of noisy images is not an easy problem. This is certainly apparent with ultrasound images. Due to the noise inherent in the images, it is often the case that discrepancies as to location of object boundaries and detection of different tissues arise even among highly trained physicians. The relatively low cost and short image acquisition time, however, make ultrasound an attractive imaging modality. Currently, diagnostic evaluation of ultrasound images is performed on two-dimensional (2D) cross-sections of the object of interest. No depth information is available and there is no way of viewing the outer surface of the object. The only way for a physician to visualise the entire object is by mentally reconstructing the object based on a series of a 2D images as well as prior expectations of the morphology of the object. In the case of abnormal or diseased growth, the physician's expectations often do not correspond to the actual morphology of the object. However, the use of three-dimensional (3D) data acquisition and visualisation may be used to overcome these problems. The present work addresses a number of difficulties in processing 3D ultrasound data. This includes special treatment of the volumetric ultrasound data obtained from a 3D probe, determination of 3D features of the different tissue types present in the ultrasound data and identification and localisation of objects (segmentation) in the volumetric ultrasound data. Experimental results obtained from synthesised and real ultrasound data demonstrate that the present work contributes significantly to the use of ultrasound imaging as a diagnostic tool. As well, the present work can be applied to different imaging modalities or different applications areas and is thus beneficial to the area of biomedical image processing, in general.

<p>Positron emission tomography (PET) is a nuclear medicine diagnostic imaging exam of metabolic processes in the body. Radiotracers, which consist of positron emitting radioisotopes and a molecular probe, are introduced into the body, emitted radiation is detected, and tomographic images are reconstructed. The primary clinical PET application is in oncology using a glucose analogue radiotracer, which is avidly taken up by some cancers.</p><p>It is well known that PET performance and image quality degrade as body size increases, and epidemiological studies over the past two decades show that the adult US population's body size has increased dramatically and continues to increase. Larger patients have more attenuating material that increases the number of emitted photons that are scattered or absorbed within the body. Thus, for a fixed amount of injected radioactivity and acquisition duration, the number of measured true coincidence events will decrease, and the background fractions will increase. Another size-related factor, independent of attenuation, is the volume throughout which the measured coincidence counts are distributed: for a fixed acquisition duration, as the body size increases, the counts are distributed over a larger area. This is true for both a fixed amount of radioactivity, where the concentration decreases as size increases, and a fixed concentration, where the amount radioactivity increases with size.</p><p>Time-of-flight (TOF) PET is a recently commercialized technology that allows the localization, with a certain degree of error, of a positron annihilation using timing differences in the detection of coincidence photons. Both heuristic and analytical evaluations predict that TOF PET will have improved performance and image quality compared to non-TOF PET, and this improvement increases as body size increases. The goal of this dissertation is to parameterize the image quality improvement of TOF PET compared to non-TOF PET as a function of body size. Currently, no standard for comparison exists.</p><p>Previous evaluations of TOF PET's improvement have been made with either computer-simulated data or acquired data using a few discrete phantom sizes. A phantom that represents a range of attenuating dimensions, that can have a varying radioactivity distribution, and that can have radioactive inserts positioned throughout its volume would facilitate characterizing PET system performance and image quality as a function of body size. A fillable, tapered phantom, was designed, simulated, and constructed. The phantom has an oval cross-section ranging from 38.5 &times; 49.5 cm to 6.8 &times; 17.8 cm, a length of 51.1 cm, a mass of 6 kg (empty), a mass of 42 kg (water filled), and 1.25-cm acrylic walls.</p><p>For this dissertation research, PET image quality was measured using multiple, small spheres with diameters near the spatial resolution of clinical whole-body PET systems. Measurements made on a small sphere, which typically include a small number of image voxels, are susceptible to fluctuations over the few voxels, so using multiple spheres improves the statistical power of the measurements that, in turn, reduces the influence of these fluctuations. These spheres were arranged in an array and mounted throughout the tapered phantom's volume to objectively measure image quality as a function of body size. Image quality is measured by placing regions of interest on images and calculating contrast recovery, background variability, and signal to noise ratio.</p><p>Image quality as a function of body size was parameterized for TOF compared to non-TOF PET using 46 1.0-cm spheres positioned in six different body sizes in a fillable, tapered phantom. When the TOF and non-TOF PET images were reconstructed for matched contrast, the square of the ratio of the images' signal-to-noise ratios for TOF to non-TOF PET was plotted as a function, <italic>f</italic>(<italic>D</italic>), of the radioactivity distribution size, <italic>D</italic>, in cm. A linear regression was fit to the data: <italic>f</italic>(<italic>D</italic>) = 0.108<italic>D</italic> - 1.36. This was compared to the ratio of <italic>D</italic> and the localization error, <italic>&sigma;_d</italic>, based on the system timing resolution, which is approximately 650 ps for the TOF PET system used for this research. With the image quality metrics used in this work, the ratio of TOF to non-TOF PET fits well to a linear relationship and is parallel to <italic>D/&sigma;_d</italic>. For <italic>D</italic> < 20 cm, there is no image quality improvement, but for radioactivity distributions <italic>D</italic> > 20 cm, TOF PET improves image quality over non-TOF PET. PET imaging's clinical use has increased over the past decade, and TOF PET's image quality improvement for large patients makes TOF an important new technology because the occurrence of obesity in the US adult population continues to increase.</p> / Dissertation

MRI as a non-invasive method for studying the internal structure and function of the human body was developed over the past three decades. In MRI, radiofrequency (RF) field inhomogeneity is an unavoidable problem in practice and becomes severe at high magnetic fields due to the dependence of B1 on the sample. It leads to nonuniformities in image intensity and contrast, causing difficulties in quantitative interpretation and image segmentation. In this thesis, we report an interesting observation that the fast low-angle shot (FLASH) sequence, which is often used for anatomic imaging and morphometric studies, can be insensitive to RF inhomogeneity when the same coil is used for both transmission and reception and a proper nominal flip angle is employed. Recommendations also are given for optimum processing procedures for FLASH imaging. This observation can be useful in understanding the signal behavior of FLASH in the presence of RF inhomogeneity and provides a guide for selecting parameters in FLASH imaging.