Improvements in the registration of multimodal medical imaging : application to intensity inhomogeneity and partial volume corrections

Alignment or registration of medical images has a relevant role on clinical diagnostic and treatment decisions as well as in research settings. With the advent of new technologies for multimodal imaging, robust registration of functional and anatomical information is still a challenge, particular in small-animal imaging given the lesser structural content of certain anatomical parts, such as the brain, than in humans. Besides, patient-dependent and acquisition artefacts affecting the images information content further complicate registration, as is the case of intensity inhomogeneities (IIH) showing in MRI and the partial volume effect (PVE) attached to PET imaging. Reference methods exist for accurate image registration but their performance is severely deteriorated in situations involving little images Overlap. While several approaches to IIH and PVE correction exist these methods still do not guarantee or rely on robust registration. This Thesis focuses on overcoming current limitations af registration to enable novel IIH and PVE correction methods.El registre d'imatges mèdiques té un paper rellevant en les decisions de diagnòstic i tractament clíniques així com en la recerca. Amb el desenvolupament de noves tecnologies d'imatge multimodal, el registre robust d'informació funcional i anatòmica és encara avui un repte, en particular, en imatge de petit animal amb un menor contingut estructural que en humans de certes parts anatòmiques com el cervell. A més, els artefactes induïts pel propi pacient i per la tècnica d'adquisició que afecten el contingut d'informació de les imatges complica encara més el procés de registre. És el cas de les inhomogeneïtats d'intensitat (IIH) que apareixen a les RM i de l'efecte de volum parcial (PVE) característic en PET. Tot i que existeixen mètodes de referència pel registre acurat d'imatges la seva eficàcia es veu greument minvada en casos de poc solapament entre les imatges. De la mateixa manera, també existeixen mètodes per la correcció d'IIH i de PVE però que no garanteixen o que requereixen un registre robust. Aquesta tesi es centra en superar aquestes limitacions sobre el registre per habilitar nous mètodes per la correcció d'IIH i de PVE

Monte Carlo-based Noise Compensation in Coil Intensity Corrected Endorectal MRI

Background: Prostate cancer is one of the most common forms of cancer found in males making early diagnosis important. Magnetic resonance imaging (MRI) has been useful in visualizing and localizing tumor candidates and with the use of endorectal coils (ERC), the signal-to-noise ratio (SNR) can be improved. The coils introduce intensity inhomogeneities and the surface coil intensity correction built into MRI scanners is used to reduce these inhomogeneities. However, the correction typically performed at the MRI scanner level leads to noise amplification and noise level variations. Methods: In this study, we introduce a new Monte Carlo-based noise compensation approach for coil intensity corrected endorectal MRI which allows for effective noise compensation and preservation of details within the prostate. The approach accounts for the ERC SNR profile via a spatially-adaptive noise model for correcting non-stationary noise variations. Such a method is useful particularly for improving the image quality of coil intensity corrected endorectal MRI data performed at the MRI scanner level and when the original raw data is not available. Results: SNR and contrast-to-noise ratio (CNR) analysis in patient experiments demonstrate an average improvement of 11.7 dB and 11.2 dB respectively over uncorrected endorectal MRI, and provides strong performance when compared to existing approaches. Conclusions: A new noise compensation method was developed for the purpose of improving the quality of coil intensity corrected endorectal MRI data performed at the MRI scanner level. We illustrate that promising noise compensation performance can be achieved for the proposed approach, which is particularly important for processing coil intensity corrected endorectal MRI data performed at the MRI scanner level and when the original raw data is not available.Comment: 23 page

Spread spectrum-based video watermarking algorithms for copyright protection

Merged with duplicate record 10026.1/2263 on 14.03.2017 by CS (TIS)Digital technologies know an unprecedented expansion in the last years. The consumer can now benefit from hardware and software which was considered state-of-the-art several years ago. The advantages offered by the digital technologies are major but the same digital technology opens the door for unlimited piracy. Copying an analogue VCR tape was certainly possible and relatively easy, in spite of various forms of protection, but due to the analogue environment, the subsequent copies had an inherent loss in quality. This was a natural way of limiting the multiple copying of a video material. With digital technology, this barrier disappears, being possible to make as many copies as desired, without any loss in quality whatsoever. Digital watermarking is one of the best available tools for fighting this threat. The aim of the present work was to develop a digital watermarking system compliant with the recommendations drawn by the EBU, for video broadcast monitoring. Since the watermark can be inserted in either spatial domain or transform domain, this aspect was investigated and led to the conclusion that wavelet transform is one of the best solutions available. Since watermarking is not an easy task, especially considering the robustness under various attacks several techniques were employed in order to increase the capacity/robustness of the system: spread-spectrum and modulation techniques to cast the watermark, powerful error correction to protect the mark, human visual models to insert a robust mark and to ensure its invisibility. The combination of these methods led to a major improvement, but yet the system wasn't robust to several important geometrical attacks. In order to achieve this last milestone, the system uses two distinct watermarks: a spatial domain reference watermark and the main watermark embedded in the wavelet domain. By using this reference watermark and techniques specific to image registration, the system is able to determine the parameters of the attack and revert it. Once the attack was reverted, the main watermark is recovered. The final result is a high capacity, blind DWr-based video watermarking system, robust to a wide range of attacks.BBC Research & Developmen

Development and evaluation of image registration and segmentation algorithms for long wavelength infrared and visible wavelength images

In this thesis, algorithms for image registration and segmentation are developed to locate and identify DU penetrators and associated metal projectile debris on or near the surface at the US DoD firing ranges and proving grounds. The proposed registration algorithm supports fusing the LWIR and visible images. Control points are indentified by area-base detection and followed by eliminating outliers. Associated with bilinear interpolation, the gravity centers of control points are used to estimate the transformation parameters. The segmentation with a statistical detector is developed to improve the fusion result. The power spectrum density is invoked to extract and identify the image properties, and the probability of each pixel classified as target further the decision. The final result is consistent with the true vision and carries distinguished target information. The combination of registration and segmentation approaches can effectively orientate and investigate the target area

Development and evaluation of image registration and segmentation algorithms for long wavelength infrared and visible wavelength images

In this thesis, algorithms for image registration and segmentation are developed to locate and identify DU penetrators and associated metal projectile debris on or near the surface at the US DoD firing ranges and proving grounds. The proposed registration algorithm supports fusing the LWIR and visible images. Control points are indentified by area-base detection and followed by eliminating outliers. Associated with bilinear interpolation, the gravity centers of control points are used to estimate the transformation parameters. The segmentation with a statistical detector is developed to improve the fusion result. The power spectrum density is invoked to extract and identify the image properties, and the probability of each pixel classified as target further the decision. The final result is consistent with the true vision and carries distinguished target information. The combination of registration and segmentation approaches can effectively orientate and investigate the target area

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Face Recognition with Degraded Images

After more than two decades of research on the topic, automatic face recognition is finding its applications in our daily life; banks, governments, airports and many other institutions and organizations are showing interest in employing such systems for security purposes. However, there are so many unanswered questions remaining and challenges not yet been tackled. Despite its common occurrence in images, blur is one of the topics that has not been studied until recently. There are generally two types of approached for dealing with blur in images: (1) identifying the blur system in order to restore the image, (2) extracting features that are blur invariant. The first category requires extra computation that makes it expensive for large scale pattern recognition applications. The second category, however, does not suffer from this drawback. This class of features were proposed for the first time in 1995, and has attracted more attention in the last few years. The proposed invariants are mostly developed in the spatial domain and the Fourier domain. The spatial domain blur invariants are developed based on moments, while those in the Fourier domain are defined based on the phase\u27 properties. In this dissertation, wavelet domain blur invariants are proposed for the first time, and their performance is evaluated in different experiments. It is also shown that the spatial domain blur invariants are a special case of the proposed invariants. The second contribution of this dissertation is blur invariant descriptors that are developed based on an alternative definition for ordinary moments that is proposed in this dissertation for the first time. These descriptors are used forface recognition with blurred images, where excellent results are achieved. Also, in a comparison with the state-of-art, the superiority of the proposed technique is demonstrated