<p>In this thesis, methods to study gene and protein expression in cells and tissues were developed and utilized in combination with protein-specific antibodies, with the overall objective to attain greater understanding of protein function.</p><p>To analyze protein expression in <i>in vitro</i> cultured cell lines, a cell microarray (CMA) was developed, facilitating antibody-based protein profiling of cell lines using immunohistochemistry (IHC). Staining patterns in cell lines were analyzed using image analysis, developed to automatically identify cells and immunohistochemical staining, providing qualitative and quantitative measurements of protein expression. Quantitative IHC data from CMAs stained with nearly 3000 antibodies was used to evaluate the adequacy of using cell lines as models for cancer tissue. We found that cell lines are homogenous with respect to protein expression profiles, and generally more alike each other, than corresponding cancer cells <i>in vivo</i>. However, we found variability between cell lines in regards to the level of retained tumor phenotypic traits, and identified cell lines with a preserved link to corresponding cancer, suggesting that some cell lines are appropriate model systems for specific tumor types. </p><p>Specific gene expression patterns were analyzed in vitiligo vulgaris and malignant melanoma. Transcriptional profiling of vitiligo melanocytes revealed dysregulation of genes involved in melanin biosynthesis and melanosome function, thus highlighting some mechanisms possibly involved in the pathogenesis of vitiligo. Two new potential markers for infiltrating malignant melanoma, Syntaxin-7 and Discs large homolog 5, were identified using antibody-based protein profiling of melanoma in a tissue microarray format. Both proteins were expressed with high specificity in melanocytic lesions, and loss of Syntaxin-7 expression was associated with more high-grade malignant melanomas.</p><p>In conclusion, the combination of antibody-based proteomics and microarray technology provided valuable information of expression patterns in cells and tissues, which can be used to better understand associations between protein signatures and disease.</p>

<p>Many research questions in biological research lead to numerous microscope images that need to be evaluated. Here digital image cytometry, i.e., quantitative, automated or semi-automated analysis of the images is an important rapidly growing discipline. This thesis presents contributions to that field. The work has been carried out in close cooperation with biomedical research partners, successfully solving real world problems.</p><p>The world is 3D and modern imaging methods such as confocal microscopy provide 3D images. Hence, a large part of the work has dealt with the development of new and improved methods for quantitative analysis of 3D images, in particular fluorescently labeled skeletal muscle cells.</p><p>A geometrical model for robust segmentation of skeletal muscle fibers was developed. Images of the multinucleated muscle cells were pre-processed using a novel spatially modulated transform, producing images with reduced complexity and facilitating easy nuclei segmentation. Fibers from several mammalian species were modeled and features were computed based on cell nuclei positions. Features such as myonuclear domain size and nearest neighbor distance, were shown to correlate with body mass, and femur length. Human muscle fibers from young and old males, and females, were related to fiber type and extracted features, where myonuclear domain size variations were shown to increase with age irrespectively of fiber type and gender.</p><p>A segmentation method for severely clustered point-like signals was developed and applied to images of fluorescent probes, quantifying the amount and location of mitochondrial DNA within cells. A synthetic cell model was developed, to provide a controllable golden standard for performance evaluation of both expert manual and fully automated segmentations. The proposed method matches the correctness achieved by manual quantification. </p><p>An interactive segmentation procedure was successfully applied to treated testicle sections of boar, showing how a common industrial plastic softener significantly affects testosterone concentrations.</p>

Diagnosis of diabetes mellitus, presence of hyperglycemia, and/or insulin resistance confer cardiovascular risk, particularly for diastolic dysfunction. Diabetes is associated with elevated myocardial norepinephrine (NE) content, enhanced sympathetic nervous system (SNS) activity, altered resting heart rate, and depressed heart rate variability. Positron emission tomography (PET) using the NE analogue [11C]meta-hydroxyephedrine ([11C]HED) provides an index of myocardial sympathetic neuronal integrity at the NE reuptake transporter (NET). The hypothesis of this project is that (i) hyperglycemia imparts heightened sympathetic tone and NE release, leading to abnormal sympathetic neuronal function in the hearts of diabetic rats, and (ii) these abnormalities may be reversed or prevented by treatments to normalize glycemia. Sprague Dawley rats were rendered insulin resistant by high fat feeding and diabetic by a single dose of streptozotocin (STZ). Diabetic rats were treated for 8 weeks with insulin, metformin or rosiglitazone, starting from either 1 week (prevention) or 8 weeks (reversal) after STZ administration. Sympathetic neuronal integrity was evaluated longitudinally by [11C]HED PET. Echocardiography measures of systolic and diastolic function were completed at serial timepoints. Plasma NE levels were evaluated serially and expression of NET and β-adrenoceptors were tested at the terminal endpoints. Diabetic rats exhibited a 52-57% reduction of [11C]HED standardized uptake value (SUV) at 8 weeks after STZ, with a parallel 2.5-fold elevation of plasma NE and a 17-20% reduction in cardiac NET expression. These findings were confirmed by ex vivo biodistribution studies. Transmitral pulse wave Doppler echocardiography established an extension of mitral valve deceleration time and elevated early to atrial velocity ratio, suggesting diastolic dysfunction. Subsequent treatment with insulin but not metformin restored glycemia, reduced plasma NE by 50%, normalized NET expression, and recovered [11C]HED SUV towards non-diabetic age-matched control. Diastolic dysfunction in these rats persisted. By contrast, early treatment with insulin, metformin, or rosiglitazone delayed the progression of diastolic dysfunction, but had no effect on elevated NE and reduced [11C]HED SUV in diabetic rats, potentially owing to a latent decrease in blood glucose. In conclusion, diabetes is associated with heightened circulating and tissue NE levels which can be effectively reversed by lowering glycemia with insulin. Noninvasive interrogation of sympathetic neuronal integrity using [11C]HED PET may have added value in the stratification of cardiovascular risk among diabetic patients and in determining the myocardial effects of glycemic therapy.

This PhD work constitutes a series of inter-disciplinary studies that use biologically plausible computational techniques and experiments with human subjects in analyzing facial expressions. The performance of the computational models and human subjects in terms of accuracy and response time are analyzed. The computational models process images in three stages. This includes: Preprocessing, dimensionality reduction and Classification. The pre-processing of face expression images includes feature extraction and dimensionality reduction. Gabor filters are used for feature extraction as they are closest biologically plausible computational method. Various dimensionality reduction methods: Principal Component Analysis (PCA), Curvilinear Component Analysis (CCA) and Fisher Linear Discriminant (FLD) are used followed by the classification by Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA). Six basic prototypical facial expressions that are universally accepted are used for the analysis. They are: angry, happy, fear, sad, surprise and disgust. The performance of the computational models in classifying each expression category is compared with that of the human subjects. The Effect size and Encoding face enable the discrimination of the areas of the face specific for a particular expression. The Effect size in particular emphasizes the areas of the face that are involved during the production of an expression. This concept of using Effect size on faces has not been reported previously in the literature and has shown very interesting results. The detailed PCA analysis showed the significant PCA components specific for each of the six basic prototypical expressions. An important observation from this analysis was that with Gabor filtering followed by non linear CCA for dimensionality reduction, the dataset vector size may be reduced to a very small number, in most cases it was just 5 components. The hypothesis that the average response time (RT) for the human subjects in classifying the different expressions is analogous to the distance measure of the data points from the classification hyper-plane was verified. This means the harder a facial expression is to classify by human subjects, the closer to the classifying hyper-plane of the classifier it is. A bi-variate correlation analysis of the distance measure and the average RT suggested a significant anti-correlation. The signal detection theory (SDT) or the d-prime determined how well the model or the human subjects were in making the classification of an expressive face from a neutral one. On comparison, human subjects are better in classifying surprise, disgust, fear, and sad expressions. The RAW computational model is better able to distinguish angry and happy expressions. To summarize, there seems to some similarities between the computational models and human subjects in the classification process.

Breast cancer is the most common and the second most fatal cancer among women in the U.S. Current knowledge indicates that there is a relationship between high breast density (measured by mammography) and increased breast cancer risk. However, the biology behind this relationship is not well understood. This may be due to the limited information provided by mammography which only yields information on the relative amount of fibroglandular to adipose tissue in the breast. In our studies, breast density is assessed using quantitative MRI, in which MRI-based tissue-dependent parameters are derived voxel-wise by mathematically modeling the acquired MRI signals. Specifically, we use data from a radial gradient- and spin-echo imaging technique, previously developed in our group, to assess fat fraction and T₂ of the water component in relation to breast density. In addition, we use diffusion-weighted imaging to obtain another parameter, apparent diffusion coefficient (ADC) of the water component in the breast. Each parametric map provides a different type of information: fat fraction gives the amount of fat present in the voxel, the T₂ of water spin relaxation is sensitive to the water component in the tissue, and the ADC of water yields other type of information, such as tissue cellularity. The challenge in deriving these parameters from breast MRI data is the presence of abundant fat in the breast, which can cause artifacts in the images and can also affect the parameter estimation. We approached this problem by modifying the imaging sequence (as in the case of diffusion-weighted imaging) and by exploring new signal models that describe the MRI signal accounting for the presence of fat. In this work, we present the improvements made in the imaging sequence and in the parametric mapping algorithms using simulation and phantom experiments. We also present preliminary results in vivo in the context of breast density-related tissue characterization.

<p>Cilj - Cilj istraživanja je razvoj modela, implementacija prototipa i verifikacija računarskog simulacionog sistema koji omogućuje rad sa nepreciznim geoprostornim podacima u realnim uslovima, i pro&scaron;irivanje postojećiim sistemima, razvoja novih algoritama za obradu, novih modela za reprezentaciju procesa i njihovu integraciju.<br />Metodologija - Za modeliranje neodređenosti i nepreciznosti u podacima i procesima kori&scaron;ćen je fazi pristup. Za razvoj softverskog sistema kori&scaron;ćen je objektni pristup (UML 2.0.), model vi&scaron;eslojne distribuirane softverske arhitekture, kombinacija vi&scaron;e objektno orijentisanih programskih jezika, i standardi iz oblasti prostornih podataka i procesa. Verifikacija je izvr&scaron;ena na primeru ekstrakcije prostornih osobina na<br />osnovu rasterskih mapa, i implementirana su dva prostorna procesa u skladu sa definisanim modelima.<br />Rezultati - Predloženi su modeli tačkastih (fazi tačka), pravolinijskih (fazi duž) i jednostavnih povr&scaron;inskih (fazi trougao, fazi krug) nepreciznih objekata pomoću fazi skupova. Date su definicije i osobine osnovnih prostornih operacija (prostorna merenja, prostorne funkcije i prostorne relacije). Dat je predlog za modeliranje geoprostornih procesa i algoritam za odreĎivanje fazi konveksnog omotača koji se odlikuje smanjenom računskom složeno&scaron;ću u odnosu na postojeće algoritme.<br />Ograničenja istraživanja/implikacije &ndash; Sistem ne obuhvata standardizovan format za razmenu nepreciznih prostornih podataka. To znači da se format razmene mora implementirati u softveru.<br />Praktične implikacije - Predloženi modeli mogu se, pre svega, koristiti u geografskim informacionim sistemima, ali i u analizi slike, te drugim zadacima koji zahtevaju modelovanje realnog prostora (robotika i slično).<br />Originalnost/vrednost &ndash; Originalni doprinosi su sledeći: novi modeli tačkastih (fazi tačka), pravolinijskih (fazi duž) i jednostavnih povr&scaron;inskih (fazi trougao, fazi krug) objekata, novi modeli osnovnih prostornih operacija (prostorna merenja, prostorne funkcije i prostorne relacije) i novi algoritam za odreĎivanje fazi konveksnog omotača koji se odlikuje smanjenom računskom složeno&scaron;ću u odnosu na postojeće algoritme.</p> / <p> Purpose- The purpose is model development, software prototype implementation and verification of the computer simulation system which provides support for imprecise data processing under real conditions, as well as extensions to existing systems, development of novel algorithms for data processing, new models for processes&rsquo; representation and their integration.<br /> Design/methodology/approach - Fuzzy approach is used for modelling uncertainties and imprecision. For the software system&rsquo;s development object approach (UML 2.0), multi-tiered distributed software architecture,<br /> combination of several object programming languages, and standards for geospatial data and processes are deployed. Verification is carried out by the example of spatial features extraction from raster maps which is applied to two spatial processes compliant to the proposed model.<br /> Findings- Models for point (fuzzy point), line (fuzzy line) and simple planar (fuzzy triangle, fuzzy circle) imprecise objects are proposed. The definitions and basic features of spatial operations (spatial measurements, spatial functions and spatial relations) are given. A proposal for modelling geospatial processes is given and the algorithm for fuzzy convex hull characterized by reduced computational complexity.<br /> Research limitations/implications - The system does not comprise standardized format for imprecise data interchange. This means that interchange format should be implemented within the software.<br /> Practical implications - The proposed models can be used, primarily for geographic information systems, but they can be also deployed in image analysis as well as tasks requiring modelling of the real space (robotics, etc.).<br /> Originality/Value - The main original contributions are: novel models for point (fuzzy point), line (fuzzy line) and simple planar (fuzzy triangle, fuzzy circle) imprecise objects, novel models for imprecise spatial operations (spatial measurements, spatial functions and spatial relations), and the algorithm for fuzzy convex hull characterized by reduced computational complexity.</p>

Ultrasound-based fetal biometry is used to derive important clinical information for identifying IUGR (intra-uterine growth restriction) and managing risk in pregnancy. Accurate and reproducible biometric measurement relies heavily on a good standard image plane. However, qualitative visual assessment, which includes the visual identification of certain anatomical landmarks in the image is prone to inter- and intra-reviewer variability and is also time-consuming to perform. Automated anatomical structure detection is the first step towards the development of a fast and reproducible quality assessment of fetal biometry images. This thesis deals specifically with abdominal scans in the development and evaluation of methods to automatically detect the stomach and the umbilical vein within them. First, an original method for detecting the stomach and the umbilical vein in fetal abdominal scans was developed using a machine learning framework. A classifier solution was designed with AdaBoost learning algorithm with Haar features extracted from the intensity image. The performance of the new method was compared on different clinically relevant gestational age groups. Speckle and the low contrast nature of ultrasound images motivated the idea of introducing features extracted from local phase images. Local phase is contrast invariant and has proven to be useful in other ultrasound image analysis application compared with intensity. Nevertheless, it has never been implemented in a machine learning environment before. In our second experiment, local phase features were proven to have higher discriminative power than intensity features which enabled them to be selected as the first weak classifiers with large classifier weight. Third, a novel approach to improving the speed of the detection was developed using a global feature symmetry map based on local phase to select the candidate locations for the stomach and the umbilical vein. It was coupled with a local intensity-based classifier to form a “hybrid” detector. A nine-fold increase in the average computational speed was recorded along with higher accuracy in the detection of both the anatomical structures. Quantitative and qualitative evaluations of all the algorithms were presented using 2384 fetal abdominal images retrieved from the image database study of the Oxford Ultrasound Quality Control Unit of the INTERGROWTH-21st project. Finally, the “hybrid” detection method was evaluated in two potential application scenarios. The first application was clinical scoring in which both the computer algorithm and four experts were asked to record presence or absence of the stomach and the umbilical vein in 400 ultrasound images. The computer-experts agreement was found to be comparable with the inter-expert agreement. The second application concerned selecting the standard image plane from 3D abdominal ultrasound volume. The algorithm was successful in selecting 93.36% of the images plane defined by the expert in 30 ultrasound volumes.

Vitamin D is very important for endochondral ossification and it is commonly insufficient during pregnancy (Javaid et al., 2006). Insufficiency of vitamin D during pregnancy predicts bone mass and hence predicts adult osteoporosis (Javaid et al., 2006). The relationship between maternal vitamin D and manually measured fetal biometry has been studied (Mahon et al., 2009). However, manual fetal biometry especially volumetric measurements are subjective, time-consuming and possibly irreproducible. Computerised measurements can overcome or at least reduce such problems. This thesis concerns the development and evaluation of novel methods to do this. This thesis makes three contributions. Firstly, we have developed a novel technique based on the Random Forests (RF) classifier to segment and measure several fetal femoral characteristics from 3D ultrasound volumes automatically. We propose a feature selection step in the training stage to eliminate irrelevant features and utilise the "good" ones. We also develop a weighted voting mechanism to weight tree probabilistic decisions in the RF classifier. We show that the new RF classifier is more accurate than the classic method (Yaqub et al., 2010b, Yaqub et al., 2011b). We achieved 83% segmentation precision using the proposed technique compared to manually segmented volumes. The proposed segmentation technique was also validated on segmenting adult brain structures in MR images and it showed excellent accuracy. The second contribution is a wavelet-based image fusion technique to enhance the quality of the fetal femur and to compensate for missing information in one volume due to signal attenuation and acoustic shadowing. We show that using image fusion to increase the image quality of ultrasound images of bony structures leads to a more accurate and reproducible assessment and measurement qualitatively and quantitatively (Yaqub et al., 2010a, Yaqub et al., 2011a). The third contribution concerns the analysis of data from a cohort study of 450 fetal femoral ultrasound volumes (18-21 week gestation). The femur length, cross-sectional areas, volume, splaying indices and angles were automatically measured using the RF method. The relationship between these measurements and the fetal gestational age and maternal vitamin D was investigated. Segmentation of a fetal femur is fast (2.3s/volume), thanks to the parallel implementation. The femur volume, length, splaying index were found to significantly correlate with fetal gestational age. Furthermore, significant correlations between the automatic measurements and 10 nmol increment in maternal 25OHD during second trimester were found.

Uncertainty in the sizing of breast cancer masses is a major issue in breast screening programs, as there is a tendency to severely underestimate the sizing of malignant masses, especially with ultrasound imaging as part of the standard triple assessment. Due to this issue about 20% of all surgically treated women have to undergo a second resection, therefore the aim of this thesis is to address this issue by developing novel image analysis methods. Ultrasound elasticity imaging has been proven to have a better ability to differentiate soft tissues compared to standard B-mode. Thus a novel segmentation algorithm is presented, employing elasticity imaging to improve the sizing of malignant breast masses in ultrasound. The main contributions of this work are the introduction of a novel filtering technique to significantly improve the quality of the B-mode image, the development of a segmentation algorithm and their application to an ongoing clinical trial. Due to the limitations of the employed ultrasound device, the development of a method to improve the contrast and signal to noise ratio of B-mode images was required. Thus, an autoregressive model based filter on the radio-frequency signal is presented which is able to reduce the misclassification error on a phantom by up to 90% compared to the employed device, achieving similar results to a state-of-the art ultrasound system. By combining the output of this filter with elasticity data into a region based segmentation framework, a computationally highly efficient segmentation algorithm using Graph-cuts is presented. This method is shown to successfully and reliably segment objects on which previous highly cited methods have failed. Employing this method on 18 cases from a clinical trial, it is shown that the mean absolute error is reduced by 2 mm, and the bias of the B-Mode sizing to underestimate the size was overcome. Furthermore, the ability to detect widespread DCIS is demonstrated.

In this thesis, we develop a motion correction method to overcome the degradation of image quality introduced by respiratory motion in positron emission tomography (PET), so that diagnostic performance for lung cancer can be improved. Lung cancer is currently the most common cause of cancer death both in the UK and in the world. PET/CT, which is a combination of PET and CT, providing clinicians with both functional and anatomical information, is routinely used as a non-invasive imaging technique to diagnose and stage lung cancer. However, since a PET scan normally takes 15-30 minutes, respiration is inevitable in data acquisition. As a result, thoracic PET images are substantially degraded by respiratory motion, not only by being blurred, but also by being inaccurately attenuation corrected due to the mismatch between PET and CT. If these challenges are not addressed, the diagnosis of lung cancer may be misled. The main contribution of this thesis is to propose a novel process for respiratory motion correction, in which non-attenuation corrected PET images (PET-NAC) are registered to a reference position for motion correction and then multiplied by a voxel-wise attenuation correction factor (ACF) image for attenuation correction. The ACF image is derived from a CT image which matches the reference position, so that no attenuation correction artefacts would occur. In experiments, the motion corrected PET images show significant improvements over the uncorrected images, which represent the acquisitions typical of current clinical practice. The enhanced image quality means that our method has the potential to improve diagnostic performance for lung cancer. We also develop an automatic lesion detection method based on motion corrected images. A small lung lesion is only 2 or 3 voxels in diameter and of marginal contrast. It could easily be missed by human observers. Our method aims to provide radiologists with a map of potential lesions for decision so that diagnostic efficiency can be improved. It utilises both PET and CT images. The CT image provides a lung mask, to which lesion detection is confined, whereas the PET image provides distribution of glucose metabolism, according to which lung lesions are detected. Experimental results show that respiratory motion correction significantly increases the success of lesion detection, especially for small lesions, and most of the lung lesions can be detected by our method. The method can serve as a useful computer-aided image analysing tool to help radiologists read images and find malignant lung lesions. Finally, we explore the possibility of incorporating temporal information into respiratory motion correction. Conventionally, respiratory gated PET images are individually registered to the reference position. Temporal continuity across the respiratory period is not considered. We propose a spatio-temporal registration algorithm, which models temporally smooth deformation in order to improve the registration performance. However, we discover that the improvement introduced by temporal information is relatively small at the cost of a much longer computation time. Spatial registration with regularisation yields similar results but is superior in speed. Therefore, it is preferable for respiratory motion correction.