Signals and Images in Sea Technologies

Life below water is the 14th Sustainable Development Goal (SDG) envisaged by the United Nations and is aimed at conserving and sustainably using the oceans, seas, and marine resources for sustainable development. It is not difficult to argue that signals and image technologies may play an essential role in achieving the foreseen targets linked to SDG 14. Besides increasing the general knowledge of ocean health by means of data analysis, methodologies based on signal and image processing can be helpful in environmental monitoring, in protecting and restoring ecosystems, in finding new sensor technologies for green routing and eco-friendly ships, in providing tools for implementing best practices for sustainable fishing, as well as in defining frameworks and intelligent systems for enforcing sea law and making the sea a safer and more secure place. Imaging is also a key element for the exploration of the underwater world for various scopes, ranging from the predictive maintenance of sub-sea pipelines and other infrastructure projects, to the discovery, documentation, and protection of sunken cultural heritage. The scope of this Special Issue encompasses investigations into techniques and ICT approaches and, in particular, the study and application of signal- and image-based methods and, in turn, exploration of the advantages of their application in the previously mentioned areas

Bioactivation of 3D Cell-Imprinted Polydimethylsiloxane Surfaces by Bone Protein Nanocoating for Bone Tissue Engineering

Physical and chemical parameters that mimic the physiological niche of the human body have an influence on stem cell fate by creating directional signals to cells. Micro/nano cell-patterned polydimethylsiloxane (PDMS) substrates, due to their ability to mimic the physiological niche, have been widely used in surface modification. Integration of other factors such as the biochemical coating on the surface can achieve more similar microenvironmental conditions and promote stem cell differentiation to the target cell line. Herein, we investigated the effect of physical topography, chemical functionalization by acid bone lysate (ABL) nanocoating, and the combined functionalization of the bone proteins' nanocoated surface and the topographically modified surface. We prepared four distinguishing surfaces: plain PDMS, physically modified PDMS by 3D cell topography patterning, chemically modified PDMS with bone protein nanocoating, and chemically modified nano 3D cell-imprinted PDMS by bone proteins (ABL). Characterization of extracted ABL was carried out by Bradford staining and sodium dodecyl sulfate polyacrylamide gel electrophoresis analysis, followed by the MTT assay for evaluation of cell viability on ABL-coated PDMS. Moreover, field emission scanning electron microscopy and profilometry were used for the determination of optimal coating thickness, and the appropriate coating concentration was identified and used in the study. The binding and retention of ABL to PDMS were confirmed by Fourier transform infrared spectroscopy and bicinchoninic acid assay. Sessile drop static water contact angle measurements on substrates showed that the combined chemical functionalization and nano 3D cell-imprinting on the PDMS surface improved surface wettability by 66% compared to plain PDMS. The results of ALP measurement, alizarin red S staining, immunofluorescence staining, and real-time PCR showed that the nano 3D cell-imprinted PDMS surface functionalized by extracted bone proteins, ABL, is able to guide the fate of adipose derived stem cellss toward osteogenic differentiation. Eventually, chemical modification of the cell-imprinted PDMS substrate by bone protein extraction not only improved the cell adhesion and proliferation but also contributed to the topographical effect itself and caused a significant synergistic influence on the process of osteogenic differentiation

Computational phase imaging for biomedical applications

When a sample is illuminated by an imaging field, its fingerprints are left on the amplitude and the phase of the emerging wave. Capturing the information of the wavefront grants us a deeper understanding of the optical properties of the sample, and of the light-matter interaction. While the amplitude information has been intensively studied, the use of the phase information has been less common. Because all detectors are sensitive to intensity, not phase, wavefront measurements are significantly more challenging. Deploying optical interferometry to measure phase through phase-intensity conversion, quantitative phase imaging (QPI) has recently gained tremendous success in material and life sciences. The first topic of this dissertation describes our effort to develop a new QPI setup, named transmission Spatial Light Interference Microscopy (tSLIM), that uses the twisted nematic liquid-crystal (TNLC) modulators. Compared to the established SLIM technique, tSLIM is much less expensive to build than its predecessor (SLIM) while maintaining significant performance. The tSLIM system uses parallel aligned liquid-crystal (PANLC) modulators, has a slightly smaller signal-to-noise Ratio (SNR), and a more complicated model for the image formation. However, such complexity is well addressed by computing. Most importantly, tSLIM uses TNLC modulators that are popular in display LCDs. Therefore, the total cost of the system is significantly reduced. Alongside developing new imaging modalities, we also improved current QPI imaging systems. In practice, an incident field to the sample is rarely perfectly spatially coherent, i.e., plane wave. It is generally partially coherent; i.e., it comprises of many incoherent plane waves coming from multiple directions. This illumination yields artifacts in the phase measurement results, e.g., halo and phase-underestimation. One solution is using a very bright source, e.g., a laser, which can be spatially filtered very well. However, the laser comes at the expense of speckles, which degrades image quality. Therefore, solutions purely based on physical modeling and computations to remove these artifacts, using white-light illumination, are highly desirable. Here, using physical optics, we develop a theoretical model that accurately explains the effects of partial coherence on image information and phase information. The model is further combined with numerical processing to suppress the artifacts, and recover the correct phase information. The third topic is devoted to applying QPI to clinical applications. Traditionally, stained tissues are used in prostate cancer diagnosis instead. The reason is that tissue samples used in diagnosis are nearly transparent under bright field inspection if unstained. Contrast-enhanced microscopy techniques, e.g., phase contrast microscopy (PC) and differential interference contrast microscopy (DIC), can render visibility of the untagged samples with high throughput. However, since these methods are intensity-based, the contrast of acquired images varies significantly from one imaging facility to another, preventing them from being used in diagnosis. Inheriting the merits of PC, SLIM produces phase maps, which measure the refractive index of label-free samples. However, the maps measured by SLIM are not affected by variation in imaging conditions, e.g., illumination, magnification, etc., allowing consistent imaging results when using SLIM across different clinical institutions. Here, we combine SLIM images with machine learning for automatic diagnosis results for prostate cancer. We focus on two diagnosis problems of automatic Gleason grading and cancer vs. non-cancer diagnosis. Finally, we introduce a new imaging modality, named Gradient Light Interference Microscopy (GLIM), which is able to image through optically thick samples using low spatial coherence illumination. The key benefit of GLIM comes from a large numerical aperture of the condenser, which is 0.55 NA, about five times higher than that in SLIM. GLIM has an excellent depth sectioning when recording three-dimensional information of the susceptibility of the sample. We also introduce a model for the image formation of GLIM with an implication that a simple filtering step in the transverse dimension can dramatically improve the sectioning in the axial dimension. With GLIM, one can measure accurately the surface area, volume, and dry mass of a variety of biological samples, ranging from cells that are about tens of microns thick to bovine embryos that are hundreds of microns thick

A three dimensional analysis of soft tissue and bone changes following orthognathic surgery

Introduction: This report investigates the ability of surgeons to achieve predicted surgical movements in five different groups of patients, and analyses both the predictions and the changes in two dimensions using scale space analyses (Campos 1991). The report then progresses to the three dimensional analysis of the bone, the soft tissues and the ratio of soft tissue to bone following surgery, using a colour coded techniques (Fright and Linney, 1991) to illustrate the changes. The average soft tissue scans from each group of patients were averaged and compared to a control group at the preoperative, three months and 1 year postoperative stages (Fright, 1991) Data Acquisition: Bone measurements were recorded from lateral skull radiographs preoperatively and 48 hrs postoperatively, and CT scans preoperatively and 1 year postoperatively. Soft tissue measurements from an optical scanner, preoperatively, three months and 1 year postoperatively. Patients 1) Control group: 30 females and 30 males 2) Skeletal class 2 patients: 15 Females and 2 Males 3) Skeletal class 3 patients: 9 Females and 7 Males 4) Cleft Palate Patients a) Unilateral cleft lip and palate: I 6 Females: 2 left and 4 right sided clefts 7 Males: 3 left and 4 right sided clefts b) Bilateral cleft lip and palate: 5 Males and 1 Female c) Clefts of the Hard and Soft palate: 5 Females. Results: Prediction: There was a surprisingly poor match between the predicted and achieved movements in both the horizontal and vertical direction in all patient groups. The scale space analysis provided an efficient method of illustrating profile changes. Soft tissue movements There were definite patterns of change and relapse in the patient groups. The relapse being most marked in the cleft palate patients. Bone movements and soft tissue to bone ratios Definite patterns of movement for the maxilla and the mandible became apparent for both the bone and soft tissue to bone ratio of movement in each group. For maxillary impactions in the skeletal 2 group there was a 1:1 ratio of movement of the soft tissue to bone in the midline increasing to 1.25:1 in the canine region and 1.5:1 in the paranasal region. Conclusions: There is a need to develop a technique to aid the the surgeons in carrying out planned surgical movements. The colour coded method was shown to be a simple, efficient and easily understandable way of analysing surgical change. Diagnosis of surgical requirements was aided by the ability to objectively compare the individual to a control group. The prediction of surgical change should be greatly aided by adapting the current database to include the distinct patterns of movement in the bone and ratio of movements of the soft tissues to the bone

Vision Sensors and Edge Detection

Vision Sensors and Edge Detection book reflects a selection of recent developments within the area of vision sensors and edge detection. There are two sections in this book. The first section presents vision sensors with applications to panoramic vision sensors, wireless vision sensors, and automated vision sensor inspection, and the second one shows image processing techniques, such as, image measurements, image transformations, filtering, and parallel computing

Bioengineering Stents for Proactive Biocompatibility: From Biomaterials to Stents

The thesis describes methods to characterize modified biomaterial surfaces in vitro, and investigate its short term implications at the artery interface in vivo. Plasma activated coating (PAC) technology has been previously deposited on a stainless steel biomaterial (316LSS), investigated in the stent form in vivo. Initial histopathology characterizations conducted with resin-artery-stents evaluate artery-stent interface interactions in vivo. The 7 day pilot study was followed by detailed material characterization and biofunctionalization on a modified cobalt chromium metal alloy L605, for the first time herein. The outcome of this study, is to transfer optimized plasma technology to new generation cobalt chromium stents (Multi Link 8, Abbott Vascular); currently in use to treat coronary artery disease (CAD). Plasma technology is unique for its ability to not delaminate from a biomaterial, while providing surface hemocompatibility, cytocompatibility, and controlled covalent attachment of protein tropoelastin (TE), in its native conformation. The present study addressed three key questions: 1. Do PAC 316LSS stents engineered with TE improve in vivo biocompatibility at 7 days? 2. How does PAC adhere to cobalt chromium alloy L605 (novel biomaterial) to prevent delamination under stress? 3. How does PAC-L605 maintain superior hemocompatibility and promote homogenous cell culture compared to alloy L605

Imaging Sensors and Applications

In past decades, various sensor technologies have been used in all areas of our lives, thus improving our quality of life. In particular, imaging sensors have been widely applied in the development of various imaging approaches such as optical imaging, ultrasound imaging, X-ray imaging, and nuclear imaging, and contributed to achieve high sensitivity, miniaturization, and real-time imaging. These advanced image sensing technologies play an important role not only in the medical field but also in the industrial field. This Special Issue covers broad topics on imaging sensors and applications. The scope range of imaging sensors can be extended to novel imaging sensors and diverse imaging systems, including hardware and software advancements. Additionally, biomedical and nondestructive sensing applications are welcome

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise

Automated shape analysis and visualization of the human back.

Spinal and back deformities can lead to pain and discomfort, disrupting productivity, and may require prolonged treatment. The conventional method of assessing and monitoring tile de-formity using radiographs has known radiation hazards. An alternative approach for monitoring the deformity is to base the assessment on the shape of back surface. Though three-dimensional data acquisition methods exist, techniques to extract relevant information for clinical use have not been widely developed. Thi's thesis presentsthe content and progression of research into automated analysis and visu-alization of three-dimensional laser scans of the human back. Using mathematical shape analysis, methods have been developed to compute stable curvature of the back surface and to detect the anatomic landmarks from the curvature maps. Compared with manual palpation, the landmarks have been detected to within accuracy of 1.15mm and precision of 0.8111m.Based on the detected spinous process landmarks, the back midline which is the closest surface approximation of the spine, has been derived using constrained polynomial fitting and statistical techniques. Three-dimensional geometric measurementsbasedon the midline were then corn-puted to quantify the deformity. Visualization plays a crucial role in back shape analysis since it enables the exploration of back deformities without the need for physical manipulation of the subject. In the third phase,various visualization techniques have been developed, namely, continuous and discrete colour maps, contour maps and three-dimensional views. In the last phase of the research,a software system has been developed for automating the tasks involved in analysing, visualizing and quantifying of the back shape. The novel aspectsof this research lie in the development of effective noise smoothing methods for stable curvature computation; improved shape analysis and landmark detection algorithm; effective techniques for visualizing the shape of the back; derivation of the back midline using constrained polynomials and computation of three dimensional surface measurements.