Speckle Noise Reduction in Medical Ultrasound Images

Ultrasound imaging is an incontestable vital tool for diagnosis, it provides in non-invasive manner the internal structure of the body to detect eventually diseases or abnormalities tissues. Unfortunately, the presence of speckle noise in these images affects edges and fine details which limit the contrast resolution and make diagnostic more difficult. In this paper, we propose a denoising approach which combines logarithmic transformation and a non linear diffusion tensor. Since speckle noise is multiplicative and nonwhite process, the logarithmic transformation is a reasonable choice to convert signaldependent or pure multiplicative noise to an additive one. The key idea from using diffusion tensor is to adapt the flow diffusion towards the local orientation by applying anisotropic diffusion along the coherent structure direction of interesting features in the image. To illustrate the effective performance of our algorithm, we present some experimental results on synthetically and real echographic images

Biomimetic Design for Efficient Robotic Performance in Dynamic Aquatic Environments - Survey

This manuscript is a review over the published articles on edge detection. At first, it provides theoretical background, and then reviews wide range of methods of edge detection in different categorizes. The review also studies the relationship between categories, and presents evaluations regarding to their application, performance, and implementation. It was stated that the edge detection methods structurally are a combination of image smoothing and image differentiation plus a post-processing for edge labelling. The image smoothing involves filters that reduce the noise, regularize the numerical computation, and provide a parametric representation of the image that works as a mathematical microscope to analyze it in different scales and increase the accuracy and reliability of edge detection. The image differentiation provides information of intensity transition in the image that is necessary to represent the position and strength of the edges and their orientation. The edge labelling calls for post-processing to suppress the false edges, link the dispread ones, and produce a uniform contour of objects

HIGH-ENERGY CYCLOTRONS WITHOUT SPIRAL

Abstract This paper explores the possibility of reaching high energies in isochronous ring cyclotrons with radial sectors by using negative valley fields to increase the magnetic flutter. A simple model was used to generate field maps for 4-GeV and 13-GeV proton rings, whose orbit properties were then studied using the CYCLOPS equilibrium orbit code. Field maps were also generated for two FFAG designs (both non-scaling, one isochronous and one not) and their orbit properties evaluated with CYCLOPS -the first time that a cyclotron code has been used on FFAGs

Image analysis using multiscale boundary extraction algorithm

The complete analysis and interpretation of the information in image data is a complex process. This dissertation presents 3 major contributions to image analysis, namely, global multiscale detection, local scale analysis, and boundary extraction. Global scale analysis is related to identification of the various scales presented in the image. A new approach for global scale analysis is developed based on the differential power spectrum normalized variance ratio (DPSNVR). The DPSNVR is the ratio of the second order normalized central moment of the power spectrum of the image to that of the multiscale differential mask. Local maxima in DPSNVR graph directly indicate the global scales in the image. Local scale analysis performs a more detailed analysis of the edges to eliminate effects of blurring. A method based on mutilscale feature matching has been proposed. Details obtained at all scales are treated using a scale invariant normalization scheme. Besides local scale analysis, a multiscale data fusion algorithm has been implemented which leads to the new concept of multiple scale differential masks. The multiple scale differential mask generated using a range of scale values possesses the remarkable shape preservation property which makes it superior to traditional multiscale masks. Finally the complete sequential boundary extraction algorithm based on particle motion in a velocity field is presented. The boundary extraction algorithm incorporates edge localization, boundary representation, and automated selection of boundary extraction parameters. The global scale analysis techniques in conjunction with the boundary extraction algorithm provide a multiscale image segmentation algorithm

Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration

Future AI applications require performance, reliability and privacy that the existing, cloud-dependant system architectures cannot provide. In this article, we study orchestration in the device-edge-cloud continuum, and focus on AI for edge, that is, the AI methods used in resource orchestration. We claim that to support the constantly growing requirements of intelligent applications in the device-edge-cloud computing continuum, resource orchestration needs to embrace edge AI and emphasize local autonomy and intelligence. To justify the claim, we provide a general definition for continuum orchestration, and look at how current and emerging orchestration paradigms are suitable for the computing continuum. We describe certain major emerging research themes that may affect future orchestration, and provide an early vision of an orchestration paradigm that embraces those research themes. Finally, we survey current key edge AI methods and look at how they may contribute into fulfilling the vision of future continuum orchestration.Comment: 50 pages, 8 figures (Revised content in all sections, added figures and new section

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

Tools and Methods for the Registration and Fusion of Remotely Sensed Data

Tools and methods for image registration were reviewed. Methods for the registration of remotely sensed data at NASA were discussed. Image fusion techniques were reviewed. Challenges in registration of remotely sensed data were discussed. Examples of image registration and image fusion were given

Ultrasonic Pulse Wave Imaging for in vivo Assessment of Vascular Wall Dynamics and Characterization of Arterial Pathologies

Arterial diseases such as hypertension, carotid stenosis, and abdominal aortic aneurysm (AAA) may progress silently without symptoms and contribute to acute cardiovascular events such as heart attack, stroke, and aneurysm rupture, which are consistently among the leading causes of death worldwide. The arterial pulse wave, regarded as one of the fundamental vital signs of clinical medicine, originates from the heart and propagates throughout the arterial tree as a pressure, flow velocity, and wall displacement wave, giving rise to the natural pulsation of the arteries. The dynamic properties of the pulse wave are intimately related to the physical state of the cardiovascular system. Thus, the assessment of the arterial wall dynamics driven by the pulse wave may provide valuable insights into vascular mechanical properties for the early detection and characterization of arterial pathologies. The focus of this dissertation was to develop and clinically implement Pulse Wave Imaging (PWI), an ultrasound elasticity imaging-based method for the visualization and spatio-temporal mapping of the pulse wave propagation at any accessible arterial location. Motion estimation algorithms based on cross-correlation of the ultrasound radio-frequency (RF) signals were used to track the arterial walls and capture the pulse wave-induced displacements over the cardiac cycle. PWI facilitates the image-guided measurement of clinically relevant pulse wave features such as propagation speed (pulse wave velocity, or PWV), uniformity, and morphology as well as derivation of the pulse pressure waveform. A parametric study investigating the performance of PWI in two canine aortas ex vivo and 10 normal, healthy human arteries in vivo established the optimal image acquisition and signal processing parameters for reliable measurement of the PWV and wave propagation uniformity. Using this framework, three separate clinical feasibility studies were conducted in patients diagnosed with hypertension, AAA, and carotid stenosis. In a pilot study comparing hypertensive and aneurysmal abdominal aortas with normal controls, the AAA group exhibited significantly higher PWV and lower wave propagation uniformity. A “teetering” motion upon pulse wave arrival was detected in the smaller aneurysms ( 5.5 cm in diameter). While no significant difference in PWV or propagation uniformity was observed between normal and hypertensive aortas, qualitative differences in the pulse wave morphology along the imaged aortic segment may be an indicator of increased wave reflection caused by elevated blood pressure and/or arterial stiffness. Pulse Wave Ultrasound Manometry (PWUM) was introduced as an extension of the PWI method for the derivation of the pulse pressure (PP) waveform in large central arteries. A feasibility study in 5 normotensive, 9 pre-hypertensive, and 5 hypertensive subjects indicated that a significantly higher PP in the hypertensive group was detected in the abdominal aorta by PWUM but not in the peripheral arteries by alternative devices (i.e. a radial applanation tonometer and the brachial sphygmomanometer cuff). A relatively strong positive correlation between aortic PP and both radial and brachial PP was observed in the hypertensive group but not in the normal and pre-hypertensive groups, confirming the notion that PP variation throughout the arterial tree may not be uniform in relatively compliant arteries. The application of PWI in 10 stenotic carotid arteries identified phenomenon such as wave convergence, elevated PWV, and decreased cumulative displacement around and/or within regions of atherosclerotic plaque. Intra-plaque mapping of the PWV and cumulative strain demonstrated the potential to quantitatively differentiate stable (i.e. calcified) and vulnerable (i.e. lipid) plaque components. The lack of correlation between quantitative measurements (PWV, modulus, displacement, and strain) and expected plaque stiffness illuminates to need to consider several physiological and imaging-related factors such as turbulent flow, wave reflection, imaging location, and the applicability of established theoretical models in vivo. PWI presents a highly translational method for visualization of the arterial pulse wave and the image-guided measurement of several clinically relevant pulse wave features. The aforementioned findings collectively demonstrated the potential of PWI to detect, diagnose, and characterize vascular disease based on qualitative and quantitative information about arterial wall dynamics under pathological conditions

SEGMENTATION AND INFORMATICS IN MULTIDIMENSIONAL FLUORESCENCE OPTICAL MICROSCOPY IMAGES

Recent advances in the field of optical microscopy have enabled scientists to observe and image complex biological processes across a wide range of spatial and temporal resolution, resulting in an exponential increase in optical microscopy data. Manual analysis of such large volumes of data is extremely time consuming and often impossible if the changes cannot be detected by the human eye. Naturally it is essential to design robust, accurate and high performance image processing and analysis tools to extract biologically significant results. Furthermore, the presentation of the results to the end-user, post analysis, is also an equally challenging issue, especially when the data (and/or the hypothesis) involves several spatial/hierarchical scales (e.g., tissues, cells, (sub)-nuclear components). This dissertation concentrates on a subset of such problems such as robust edge detection, automatic nuclear segmentation and selection in multi-dimensional tissue images, spatial analysis of gene localization within the cell nucleus, information visualization and the development of a computational framework for efficient and high-throughput processing of large datasets. Initially, we have developed 2D nuclear segmentation and selection algorithms which help in the development of an integrated approach for determining the preferential spatial localization of certain genes within the cell nuclei which is emerging as a promising technique for the diagnosis of breast cancer. Quantification requires accurate segmentation of 100 to 200 cell nuclei in each patient tissue sample in order to draw a statistically significant result. Thus, for large scale analysis involving hundreds of patients, manual processing is too time consuming and subjective. We have developed an integrated workflow that selects, following 2D automatic segmentation, a sub-population of accurately delineated nuclei for positioning of fluorescence in situ hybridization labeled genes of interest in tissue samples. Application of the method was demonstrated for discriminating normal and cancerous breast tissue sections based on the differential positioning of the HES5 gene. Automatic results agreed with manual analysis in 11 out of 14 cancers, all 4 normal cases and all 5 non-cancerous breast disease cases, thus showing the accuracy and robustness of the proposed approach. As a natural progression from the 2D analysis algorithms to 3D, we first developed a robust and accurate probabilistic edge detection method for 3D tissue samples since several down stream analysis procedures such as segmentation and tracking rely on the performance of edge detection. The method based on multiscale and multi-orientation steps surpasses several other conventional edge detectors in terms of its performance. Subsequently, given an appropriate edge measure, we developed an optimal graphcut-based 3D nuclear segmentation technique for samples where the cell nuclei are volume or surface labeled. It poses the problem as one of finding minimal closure in a directed graph and solves it efficiently using the maxflow-mincut algorithm. Both interactive and automatic versions of the algorithm are developed. The algorithm outperforms, in terms of three metrics that are commonly used to evaluate segmentation algorithms, a recently reported geodesic distance transform-based 3D nuclear segmentation method which in turns was reported to outperform several other popular tools that segment 3D nuclei in tissue samples. Finally, to apply some of the aforementioned methods to large microscopic datasets, we have developed a user friendly computing environment called MiPipeline which supports high throughput data analysis, data and process provenance, visual programming and seamlessly integrated information visualization of hierarchical biological data. The computational part of the environment is based on LONI Pipeline distributed computing server and the interactive information visualization makes use of several javascript based libraries to visualize an XML-based backbone file populated with essential meta-data and results