DIGITAL IMAGE PROCESSING IN ULTRASOUND IMAGES

Image processing plays a vital role in the development of medical industry especially ultrasound medical images. Image acquisition is carried out using ultrasound equipments like transducer, scanner, and CPU and display device. There are mainly four modes of ultrasound imaging A - mode, M - mode, B - mode and Doppler - mode. Ultrasound imaging plays crucial role in medical imaging due to its non - invasive nature, low cost and capability of forming real time imaging. Modern ultrasound systems are signal processing intensive. Advanced techniques of signal processing are used to provide better image quality and higher diagnostic valu

A multi-scale imaging approach to understand osteoarthritis development

X-ray phase-contrast imaging is an innovative and advanced imaging method. Contrary to conventional radiology, where the image contrast is primarily determined by X-ray attenuation, phase-contrast images contain additional information generated by the phase shifts or refraction of the X-rays passing through matter. The refractive effect on tissue samples is orders of magnitude higher than the absorption effect in the X-ray energy range used in biomedical imaging. This technique makes it possible to produce excellent and enhanced image contrast, particularly when examining soft biological tissues or features with similar X-ray attenuation properties. In combination with high spatial resolution detector technology and computer tomography, X-ray phase-contrast imaging has been proved to be a powerful method to examine tissue morphology and the evolution of pathologies three-dimensionally, with great detail and without the need of contrast agents. This Thesis work has focused on developing an accurate, multi-scale X-ray-based methodology for imaging and characterizing the early stages of osteoarthritis. X-ray phase-contrast images acquired at different spatial resolutions provide unprecedented insights into cartilage and the development of its degeneration, i.e., osteoarthritis. Other types of X-ray phase-contrast imaging techniques and setups using spatial resolutions ranging from micrometer down to nanometer were applied. Lower spatial resolutions allow large sample coverage and comprehensive representations, while the nanoscale analysis provides a precise depiction of anatomical details and pathological signs. X-ray phase-contrast results are correlated to data obtained, on the same specimens, by standard laboratory methods, such as histology and transmission electron microscopy. Furthermore, X-ray phase-contrast images of cartilage were acquired using different X-ray sources and results were compared in terms of image quality. It was shown that with the use of synchrotron radiation, more detailed images and much faster data acquisitions could be achieved. A second focus in this Thesis work has been the investigation of the reaction of healthy and degenerated cartilage under different physical pressures, simulating the different levels of stress to which the tissue is subject during daily movements. A specifically designed setup was used to dynamically study cartilage response to varying pressures with X-ray phase-contrast micro-computed tomography, and a fully volumetric and quantitative methodology to accurately describe the tissue morphological variations. This study revealed changes in the behavior of the cartilage cell structure, which differ between normal and osteoarthritic cartilage tissues. The third focus of this Thesis is the realization of an automated evaluation procedure for the discrimination of healthy and cartilage images with osteoarthritis. In recent years, developments in neural networks have shown that they are excellently suited for image classification tasks. The transfer learning method was applied, in which a pre-trained neural network with cartilage images is further trained and then used for classification. This enables a fast, robust and automated grouping of images with pathological findings. A neural network constructed in this way could be used as a supporting instrument in pathology. X-ray phase-contrast imaging computed tomography can provide a powerful tool for a fully 3D, highly accurate and quantitative depiction and characterization of healthy and early stage-osteoarthritic cartilage, supporting the understanding of the development of osteoarthritis.Röntgen-Phasenkontrast-Bildgebung ist eine innovative und weiterführende Bildgebungsmethode. Im Gegensatz zu herkömlichen Absorptions-Röntgenaufnahmen, wie sie in der Radiologie verwendet werden, wird der Kontrast bei dieser Methode aus dem Effekt der Phasenverschiebung oder auch Brechung der Röngtenstrahlen gebildet. Der Brechungseffekt bei Gewebeproben ist um ein Vielfaches höher als der Absorptionseffekt des elektromagnetischen Spektrums der Röntgenstrahlen. Diese Methode ermöglicht die Darstellung von großen Kontraste im Gewebe. Unter Verwendung eines hochauflösenden Detektors und in Kombination mit der Computer-Tomographie, ist Phasenkontrast-Bildgebung eine sehr gute Methode um Knorpelgewebe und Arthrose im Knorpel zu untersuchen. Diese Arbeit beschreibt primär ein Verfahren zur Darstellung arthrotischen Knorpels im Anfangsstadium. Die mit verschiedenen Auflösungen und 3D-Phasen-Kontrast-Methoden produzierten Aufnahmen ermöglichen einen noch nie dagewesenen Einblick in den Knorpel und die Entwicklung von Arthrose im Anfangsstadium. Hierbei kam die propagationsbasierte Phasenkontrastmethode mit einer Auflösung im mikrometer Bereich und die (Nano)-Holotomographie-Methode mit einer Auflösung im Submicrometer Bereich zum Einsatz. Durch Auflösung im mikrometer Bereich kann ein großes Volumen im Knorpel gescannt werden, während die Nano-Holotomographie Methode eine sehr große Detailauflösung aufweißt. Die Phasenkontrast-Aufnahmen werden mit zwei anderen wissenschaftlichen Methoden verglichen: mikroskopische Abbildungen histologisch aufgearbeiteter Knorpelproben und Aufnahmen eines Transmissionselektroskop zeigen sehr große Übereinstimmungen zur Röntgen-Phasenkontrast-Bildgebung. Desweiteren wurden Phasenkontrast-Aufnahmen von Knorpel aus unterschiedlichen Röntgenquellen verglichen. Hierbei zeigte sich, dass mit Hilfe des Teilchenbeschleunigers (Synchrotron) detailreichere und schnellere Aufnahmen erzielt werden können. Bilder aus Flüssig-Metall-Quellen zeigen sich durchaus von guter Qualität, erfordern jedoch sehr lange Aufnahmezeiten. In dieser Arbeit wird zudem das Verhalten von Knorpelgewebe, welches ein Anfangsstadium von Arthrose aufweist, unter physikalischem Druck untersucht. Hierfür wurden 3D-Computertomographie-Aufnahmen von komprimiertem Knorpelgewebe angefertig und mit Aufnahmen ohne Komprimierung verglichen. Ein quantitativer Vergleich machte Veränderungen des Verhaltens der Knorpelzellstruktur (Chondronen) sichtbar. Es konnte gezeigt werden, dass Chondrone bei arthrotischem Knorpel ein verändertes Kompressionsverhalten haben. Der dritte Fokus dieser Arbeit liegt auf der automatisierten Auswertung von Aufnahmen gesunden und arthrotischen Knorpelgewebes. Die Entwicklungen im Bereich der Neuronale Netze zeigten in den letzten Jahren, dass diese sich hervoragend für Bildklassifizierungsaufgaben eignen. Es wurde die Methode des transferierenden Lernens angewandt, bei der ein vortrainiertes Neuronales Netz mit Knorpelbildern weitertrainiert und anschließend zur Klassifizierung eingesetzt wird. Dadurch ist eine schnelle, robuste und automatisierte Gruppierung von Bildern mit pathologischen Befunden möglich. Ein derart konstruiertes Neuronales Netz könnte als unterstützendes Instrument in der Pathologie angewandt werden. Röntgen-Phasenkontrast-CT kann ein leistungsstarkes Werkzeug für eine umfassende, hochpräzise und quantitative 3D-Darstellung und Charakterisierung von gesundem Knorpel und athrotischem Knorpel im Frühstadium bieten, um das Verständnis der Entwicklung von Osteoarthritis zu erweitern

シュリーレン法による可聴音場可視化のための時空間フィルタリング

早大学位記番号:新7470早稲田大

Light field image denoising using a linear 4D frequency-hyperfan all-in-focus filter

Imaging in low light is problematic as sensor noise can dominate imagery, and increasing illumination or aperture size is not always effective or practical. Computational photography offers a promising solution in the form of the light field camera, which by capturing redundant information offers an opportunity for elegant noise rejection. We show that the light field of a Lambertian scene has a 4D hyperfan-shaped frequency-domain region of support at the intersection of a dual-fan and a hypercone. By designing and implementing a filter with appropriately shaped passband we accomplish denoising with a single all-in-focus linear filter. Drawing examples from the Stanford Light Field Archive and images captured using a commercially available lenselet-based plenoptic camera, we demonstrate that the hyperfan outperforms competing methods including synthetic focus, fan-shaped antialiasing filters, and a range of modern nonlinear image and video denoising techniques. We show the hyperfan preserves depth of field, making it a single-step all-in-focus denoising filter suitable for general-purpose light field rendering. We include results for different noise types and levels, over a variety of metrics, and in real-world scenarios. Finally, we show that the hyperfan’s performance scales with aperture count. 1

Plenoptic Signal Processing for Robust Vision in Field Robotics

This thesis proposes the use of plenoptic cameras for improving the robustness and simplicity of machine vision in field robotics applications. Dust, rain, fog, snow, murky water and insufficient light can cause even the most sophisticated vision systems to fail. Plenoptic cameras offer an appealing alternative to conventional imagery by gathering significantly more light over a wider depth of field, and capturing a rich 4D light field structure that encodes textural and geometric information. The key contributions of this work lie in exploring the properties of plenoptic signals and developing algorithms for exploiting them. It lays the groundwork for the deployment of plenoptic cameras in field robotics by establishing a decoding, calibration and rectification scheme appropriate to compact, lenslet-based devices. Next, the frequency-domain shape of plenoptic signals is elaborated and exploited by constructing a filter which focuses over a wide depth of field rather than at a single depth. This filter is shown to reject noise, improving contrast in low light and through attenuating media, while mitigating occluders such as snow, rain and underwater particulate matter. Next, a closed-form generalization of optical flow is presented which directly estimates camera motion from first-order derivatives. An elegant adaptation of this "plenoptic flow" to lenslet-based imagery is demonstrated, as well as a simple, additive method for rendering novel views. Finally, the isolation of dynamic elements from a static background is considered, a task complicated by the non-uniform apparent motion caused by a mobile camera. Two elegant closed-form solutions are presented dealing with monocular time-series and light field image pairs. This work emphasizes non-iterative, noise-tolerant, closed-form, linear methods with predictable and constant runtimes, making them suitable for real-time embedded implementation in field robotics applications

Plenoptic Signal Processing for Robust Vision in Field Robotics

This thesis proposes the use of plenoptic cameras for improving the robustness and simplicity of machine vision in field robotics applications. Dust, rain, fog, snow, murky water and insufficient light can cause even the most sophisticated vision systems to fail. Plenoptic cameras offer an appealing alternative to conventional imagery by gathering significantly more light over a wider depth of field, and capturing a rich 4D light field structure that encodes textural and geometric information. The key contributions of this work lie in exploring the properties of plenoptic signals and developing algorithms for exploiting them. It lays the groundwork for the deployment of plenoptic cameras in field robotics by establishing a decoding, calibration and rectification scheme appropriate to compact, lenslet-based devices. Next, the frequency-domain shape of plenoptic signals is elaborated and exploited by constructing a filter which focuses over a wide depth of field rather than at a single depth. This filter is shown to reject noise, improving contrast in low light and through attenuating media, while mitigating occluders such as snow, rain and underwater particulate matter. Next, a closed-form generalization of optical flow is presented which directly estimates camera motion from first-order derivatives. An elegant adaptation of this "plenoptic flow" to lenslet-based imagery is demonstrated, as well as a simple, additive method for rendering novel views. Finally, the isolation of dynamic elements from a static background is considered, a task complicated by the non-uniform apparent motion caused by a mobile camera. Two elegant closed-form solutions are presented dealing with monocular time-series and light field image pairs. This work emphasizes non-iterative, noise-tolerant, closed-form, linear methods with predictable and constant runtimes, making them suitable for real-time embedded implementation in field robotics applications

Underwater image restoration: super-resolution and deblurring via sparse representation and denoising by means of marine snow removal

Underwater imaging has been widely used as a tool in many fields, however, a major issue is the quality of the resulting images/videos. Due to the light's interaction with water and its constituents, the acquired underwater images/videos often suffer from a significant amount of scatter (blur, haze) and noise. In the light of these issues, this thesis considers problems of low-resolution, blurred and noisy underwater images and proposes several approaches to improve the quality of such images/video frames. Quantitative and qualitative experiments validate the success of proposed algorithms

A new technique for 3D modeling of water surfaces using a Geiger mode receiver

It is difficult to accurately and quickly (i.e in real time) create a digital surface model of a water surface using bathymetric lidar when presented with water turbidity. Accurate and consistent modeling of the water surface is required in order to correct for the surface’s volatility when imaging the sea floor. However, this becomes exceedingly difficult due to the limited data return from water, the uneven surface, and noise. For this purpose, we use a highly sensitive Geiger Mode Avalanche PhotoDiode camera to collect this feint data and attempt to create an accurate digital surface model for water which can be utilized in future imaging computations.M.S

Clinical application of low-dose phase contrast breast CT: methods for the optimization of the reconstruction workflow

Results are presented of a feasibility study of three-dimensional X-ray tomographic mammography utilising in-line phase contrast. Experiments were performed at SYRMEP beamline of Elettra synchrotron. A specially designed plastic phantom and a mastectomy sample containing a malignant lesion were used to study the reconstructed image quality as a function of different image processing operations. Detailed evaluation and optimization of image reconstruction workflows have been carried out using combinations of several advanced computed tomography algorithms with different pre-processing and post-processing steps. Special attention was paid to the effect of phase retrieval on the diagnostic value of the reconstructed images. A number of objective image quality indices have been applied for quantitative evaluation of the results, and these were compared with subjective assessments of the same images by three experienced radiologists and one pathologist. The outcomes of this study provide practical guidelines for the optimization of image processing workflows in synchrotron-based phase-contrast mammo-tomography

Widening the view angle of auto-multiscopic display, denoising low brightness light field data and 3D reconstruction with delicate details

This doctoral thesis will present the results of my work into widening the viewing angle of the auto-multiscopic display, denoising light filed data the enhancement of captured light filed data captured in low light circumstance, and the attempts on reconstructing the subject surface with delicate details from microscopy image sets. The automultiscopic displays carefully control the distribution of emitted light over space, direction (angle) and time so that even a static image displayed can encode parallax across viewing directions (light field). This allows simultaneous observation by multiple viewers, each perceiving 3D from their own (correct) perspective. Currently, the illusion can only be effectively maintained over a narrow range of viewing angles. We propose and analyze a simple solution to widen the range of viewing angles for automultiscopic displays that use parallax barriers. We insert a refractive medium, with a high refractive index, between the display and parallax barriers. The inserted medium warps the exitant lightfield in a way that increases the potential viewing angle. We analyze the consequences of this warp and build a prototype with a 93% increase in the effective viewing angle. Additionally, we developed an integral images synthesis method that can address the refraction introduced by the inserted medium efficiently without the use of ray tracing. Capturing light field image with a short exposure time is preferable for eliminating the motion blur but it also leads to low brightness in a low light environment, which results in a low signal noise ratio. Most light field denoising methods apply regular 2D image denoising method to the sub-aperture images of a 4D light field directly, but it is not suitable for focused light field data whose sub-aperture image resolution is too low to be applied regular denoising methods. Therefore, we propose a deep learning denoising method based on micro lens images of focused light field to denoise the depth map and the original micro lens image set simultaneously, and achieved high quality total focused images from the low focused light field data. In areas like digital museum, remote researching, 3D reconstruction with delicate details of subjects is desired and technology like 3D reconstruction based on macro photography has been used successfully for various purposes. We intend to push it further by using microscope rather than macro lens, which is supposed to be able to capture the microscopy level details of the subject. We design and implement a scanning method which is able to capture microscopy image set from a curve surface based on robotic arm, and the 3D reconstruction method suitable for the microscopy image set