Alzheimer’s disease (AD) is a neurodegenerative terminal disorder that accounts for nearly 70% of dementia cases worldwide. Global dementia incidence is projected to 75 million cases by 2030, with the majority of the affected individuals coming from low- and medium- income countries. Although there is no cure for AD, early diagnosis can improve the quality of life of AD patients and their caregivers. Currently, AD diagnosis is carried out using mental status examinations, expensive neuroimaging scans, and invasive laboratory tests, all of which render the diagnosis time-consuming and costly. Notwithstanding, over the last decade electroencephalography (EEG), specifically resting-state EEG (rsEEG), has emerged as an alternative technique for AD diagnosis with accuracies inline with those obtained with more expensive neuroimaging tools, such as magnetic resonance imaging (MRI), computed tomography (CT) and positron emission tomography (PET). However the use of rsEEG for ...

Intravascular ultrasound has, over the past decade, significantly changed the clinical diagnosis and therapeutic strategy of coronary and vascular disease assessment, as it not only allows visualisation of the vessel lumen, but gives a unique view of the pathophysiologic structure of the artery wall. This information is currently unavailable from the universally accepted instrument for artery assessment, angiography, which has on several occasions had its diagnostic accuracy questioned. With intravascular ultrasound, there is the potential to categorise diseased arterial tissue belonging to distinct pathological groups which can ultimately aid in the understanding of individual lesions as well as making a significant contribution to treatment choice and management of cardiac patients. The high resolution image information offered by intravascular ultrasound provides excellent crosssectional views of coronary artery disease at the level of the disease process itself. This information can be used ...

Accounting for uncertainty in three-dimensional (3D) shapes is important in a large number of scientific and engineering areas, such as biometrics, biomedical imaging, and data mining. It is well known that 3D polar shaped objects can be represented by Fourier descriptors such as spherical harmonics and double Fourier series. However, the statistics of these spectral shape models have not been widely explored. This thesis studies several areas involved in 3D shape modeling, including random field models for statistical shape modeling, optimal shape filtering, parametric active contours for object segmentation and surface reconstruction. It also investigates multi-modal image registration with respect to tumor activity quantification. Spherical harmonic expansions over the unit sphere not only provide a low dimensional polarimetric parameterization of stochastic shape, but also correspond to the Karhunen-Lo´eve (K-L) expansion of any isotropic random field on the unit sphere. Spherical ...

Computed tomography (CT) is a technique that uses computation to form an image of the inside of an object or person, by combining projections of that object or person. The word tomography is derived from the Greek word tomos, meaning slice. The basis for computed tomography was laid in 1917 by Johann Radon, an Austrian mathematician. Computed tomography has a broad range of applications, the best known being medical imaging (the CT scanner), where X-rays are used for making the projection images. The rst practical application of CT was, however, in astronomy, by Ronald Bracewell in 1956. He used CT to improve the resolution of radio-astronomical observations. The practical applications in this thesis are from electron tomography, where the images are made with an electron microscope, and from preclinical research, where the images are made with a CT scanner. There ...

In this PhD thesis the problem of detection and classification of stationary targets in Through-the-Wall Radar Imaging is considered. A multiple-view framework is used in which a 3D scene of interest is imaged from a set of vantage points. By doing so, clutter and noise is strongly suppressed and target detectability increased. In target detection, centralized as well as decentralized frameworks for simultaneous image fusion and detection are examined. The practical case when no prior knowledge on image statistics is available and all inference must be drawn from the data at hand is specifically considered. An adaptive detection scheme is proposed which iteratively adapts in a non-stationary environment. Optimal configurations for this scheme are derived based on morphological operations which allow for automatic and reliable target detection. In a decentralized framework, local decisions are transmitted to a fusion center to ...

It takes more time to think of a silent scene, action or event than finding one that emanates sound. Not only speaking or playing music but almost everything that happens is accompanied with or results in one or more sounds mixed together. This makes acoustic event detection (AED) one of the most researched topics in audio signal processing nowadays and it will probably not see a decline anywhere in the near future. This is due to the thirst for understanding and digitally abstracting more and more events in life via the enormous amount of recorded audio through thousands of applications in our daily routine. But it is also a result of two intrinsic properties of audio: it doesn’t need a direct sight to be perceived and is less intrusive to record when compared to image or video. Many applications such ...

A complex physical system like the human brain can only be comprehended by the use of a combination of various medical imaging techniques, each of which shed light on only a specific aspect of the neural processes that take place beneath the skull. Electroencephalography (EEG) and functional magnetic resonance (fMRI) are two such modalities, which enable the study of brain (dys)function. While the EEG is measured with a limited set of scalp electrodes which record rapid electrical changes resulting from neural activity, fMRI offers a superior spatial resolution at the expense of only picking up slow fluctuations of oxygen concentration that takes place near active brain cells. Hence, combining these very complementary modalities is an appealing, but complicated task due to their heterogeneous nature. In this thesis, we devise advanced signal processing techniques which integrate the multimodal data stemming from ...

Assertive diagnosis of cancer, Alzheimer’s disease, epilepsy and other metabolic diseases is essential to provide patients with the adequate treatment. Recently, different invasive and non-invasive techniques have been developed for this purpose, nevertheless, due to their harmless properties the non-invasive techniques have gained more value. Magnetic Resonance is a well-known non-invasive technique that provides spectra (metabolite peaks) and images (anatomical structures) of the examined tissue. In Magnetic Resonance Spectroscopy (MRS), molecules containing certain excitable nuclei, such as 1H, provide the metabolite information. As a consequence, the peaks in the MR spectra correspond to observable metabolites which are the biomarkers of diseases. Finally, metabolite concentrations are computed and compared against normal values in order to establish the diagnosis. The method to obtain such amplitudes is also called quantification and its accuracy is essential for diagnosis assessment. Quantification of MRS signals is ...

Heavy demands on the development of medical imaging modalities for breast cancer detection have been witnessed in the last three decades in an attempt to reduce the mortality associated with the disease. Recently, Digital Breast Tomosynthesis (DBT) shows its promising in the early diagnosis when lesions are small. In particular, it offers potential benefits over X-ray mammography - the current modality of choice for breast screening - of increased sensitivity and specificity for comparable X-ray dose, speed, and cost. An important feature of DBT is that it provides a pseudo-3D image of the breast. This is of particular relevance for heterogeneous dense breasts of young women, which can inhibit detection of cancer using conventional mammography. In the same way that it is difficult to see a bird from the edge of the forest, detecting cancer in a conventional 2D mammogram ...

Multi-input multi-output (MIMO) wireless networks and distributed MIMO relaying wireless networks have attracted significant attention in current generation of wireless communication networks, and will play a key role in the next generation of wireless net- works. The improvement of network capacity, data rate and reliability can be achieved at the cost of increasing computational complexity of employing space-time coding (STC) and distributed STC (DSTC) in MIMO and distributed MIMO relaying networks, respectively. Efficient designs and algorithms to achieve high diversity and coding gains with low computational complexity in encoding and decoding of STC and DSTC schemes are essential. In this thesis, DSTC designs with high diversity and coding gains and efficient detection and code matrices optimization algorithms in cooperative MIMO networks are proposed. Firstly, adaptive power allocation (PA) algorithms with different criteria for a coop- erative MIMO network equipped with ...

Computed tomography (CT) is a non-invasive imaging technique that allows to reveal the inner structure of an object by combining a series of projection images that were acquired from dierent directions. CT nowadays has a broad range of applications, including those in medicine, preclinical research, nondestructive testing, materials science, etc. One common feature of the tomographic setups used in most applications is the requirement to put an object into a scanner. The rst major disadvantage of such a requirement is the constraint imposed on the size of the object that can be scanned. The second one is the need to move the object which might be di cult or might cause undesirable changes in the object. A possibility to perform in loco, i. e. on site, tomography will open up numerous applications for tomography in nondestructive testing, security, medicine, archaeology ...

Multiple sclerosis is a progressive autoimmune disease that a˙ects young adults. Magnetic resonance (MR) imaging has become an integral part in monitoring multiple sclerosis disease. Conventional MR imaging sequences such as fluid attenuated inversion recovery imaging have high spatial resolution, and can visualise the presence of focal white matter brain lesions in multiple sclerosis disease. Manual delineation of these lesions on conventional MR images is time consuming and su˙ers from intra and inter-rater variability. Among the advanced MR imaging techniques, MR spectroscopic imaging can o˙er complementary information on lesion characterisation compared to conventional MR images. However, MR spectroscopic images have low spatial resolution. Therefore, the aim of this thesis is to automatically segment multiple sclerosis lesions on conventional MR images and use the information from high-resolution conventional MR images to enhance the resolution of MR spectroscopic images. Automatic single time ...

In this thesis, we present novel methods to enhance speech corrupted by noise. All methods are based on the processing of complex-valued spectral data. First, kernel principal component analysis (PCA) for speech enhancement is proposed. Subsequently, a simplification of kernel PCA, called pre-image iterations (PI), is derived. This method computes enhanced feature vectors iteratively by linear combination of noisy feature vectors. The weighting for the linear combination is found by a kernel function that measures the similarity between the feature vectors. The kernel variance is a key parameter for the degree of de-noising and has to be set according to the signal-to-noise ratio (SNR). Initially, PI were proposed for speech corrupted by additive white Gaussian noise. To be independent of knowledge about the SNR and to generalize to other stationary noise types, PI are extended by automatic determination of the ...

Nowadays, diagnosis and treatment of brain tumors is based on clinical symptoms, radiological appearance, and often histopathology. Magnetic resonance imaging (MRI) is a major noninvasive tool for the anatomical assessment of tumors in the brain. However, several diagnostic questions, such as the type and grade of the tumor, are difficult to address using MRI. The histopathology of a tissue specimen remains the gold standard, despite the associated risks of surgery to obtain a biopsy. In recent years, the use of magnetic resonance spectroscopy (MRS), which provides a metabolic profile, has gained a lot of interest for a more detailed and specific noninvasive evaluation of brain tumors. In particular, magnetic resonance spectroscopic imaging (MRSI) is attractive as this may also enable to visualize the heterogeneous spatial extent of tumors, both inside and outside the MRI detectable lesion. As manual, individual, viewing ...

Advances in compression techniques, decreasing cost of storage, and high-speed transmission have facilitated the way videos are created, stored and distributed. As a consequence, videos are now being used in many applications areas. The increase in the amount of video data deployed and used in today's applications reveals not only the importance as multimedia data type, but also led to the requirement of efficient management of video data. This management paved the way for new research areas, such as indexing and retrieval of video with respect to their spatio-temporal, visual and semantic contents. This thesis presents work towards a unified framework for semi-automated video indexing and interactive retrieval. To create an efficient index, a set of representative key frames are selected which capture and encapsulate the entire video content. This is achieved by, firstly, segmenting the video into its constituent ...