Medical diagnosis using NIR and THz tissue imaging and machine learning methods

The problem of extracting useful information for medical diagnosis from 2D and 3D optical imaging experimental data is of great importance. We are discussing challenges and perspectives of medical diagnosis using machine learning analysis of NIR and THz tissue imaging. The peculiarities of tissue optical clearing for tissue imaging in NIR and THz spectral ranges aiming the improvement of content data analysis, methods of extracting of informative features from experimental data and creating of prognostic models for medical diagnosis using machine learning methods are discussed

Pattern identification of biomedical images with time series: contrasting THz pulse imaging with DCE-MRIs

Objective We provide a survey of recent advances in biomedical image analysis and classification from emergent imaging modalities such as terahertz (THz) pulse imaging (TPI) and dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) and identification of their underlining commonalities. Methods Both time and frequency domain signal pre-processing techniques are considered: noise removal, spectral analysis, principal component analysis (PCA) and wavelet transforms. Feature extraction and classification methods based on feature vectors using the above processing techniques are reviewed. A tensorial signal processing de-noising framework suitable for spatiotemporal association between features in MRI is also discussed. Validation Examples where the proposed methodologies have been successful in classifying TPIs and DCE-MRIs are discussed. Results Identifying commonalities in the structure of such heterogeneous datasets potentially leads to a unified multi-channel signal processing framework for biomedical image analysis. Conclusion The proposed complex valued classification methodology enables fusion of entire datasets from a sequence of spatial images taken at different time stamps; this is of interest from the viewpoint of inferring disease proliferation. The approach is also of interest for other emergent multi-channel biomedical imaging modalities and of relevance across the biomedical signal processing community

Design and Analysis of Telescope Receiver Systems for Future Far-Infrared Missions

The main topic of this thesis is the design and analysis of the Cosmic ORigins Explorer (CORE) telescope, a proposed mission for the ESA M5 mission call. Its focus was the study of the Cosmic Microwave Background (CMB), particularly its polarisation. An ambitious space mission, it would endeavour to detect elusive primordial B-modes. B-modes are considered the key piece of evidence for inflation theory and require extraordinary sensitivity to detect. CORE would house up to 2100 detectors on its large, super-cooled focal plane; granting the high sensitivity and wide field of view (FOV) required for CMB study but leads to challenging optical design. Maynooth’s role was to examine telescope designs capable of delivering diffraction-limited quality field of view over this 50 cm focal plane area. Two telescope designs (Offset Gregorian and Offset Dragonian) were analysed. The import and export of the mirrors with correct surface definition and orientation form a central part of this work. Physical optics analysis program GRASP was used to simulate beams on the sky from various focal plane positions to verify the positioning of different frequency detectors over the focal plane. This work would form a part of the CORE proposal. In addition, analysis was carried out on the receiver of the Large Latin American Millimetre Array (LLAMA) telescope, currently under construction in Argentina. Based on existing Atacama Large Millimetre/submillimetre Array (ALMA) telescope designs, LLAMA is an independent instrument that will be able to study a large array of astronomical phenomenon at millimetre wavelengths. Eventually it plans to form the first South American Very Long Baseline Interferometer (VLBI) array alongside ALMA and the Atacama Pathfinder Experiment (APEX). The Department of Experimental Physics was asked to perform analysis using three frequency bands on the Nasmyth B receiver of the telescope and the author was given the task

Modern Applications in Optics and Photonics: From Sensing and Analytics to Communication

Optics and photonics are among the key technologies of the 21st century, and offer potential for novel applications in areas such as sensing and spectroscopy, analytics, monitoring, biomedical imaging/diagnostics, and optical communication technology. The high degree of control over light fields, together with the capabilities of modern processing and integration technology, enables new optical measurement systems with enhanced functionality and sensitivity. They are attractive for a range of applications that were previously inaccessible. This Special Issue aims to provide an overview of some of the most advanced application areas in optics and photonics and indicate the broad potential for the future

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

Enhanced algorithms for lesion detection and recognition in ultrasound breast images

Mammography is the gold standard for breast cancer detection. However, it has very high false positive rates and is based on ionizing radiation. This has led to interest in using multi-modal approaches. One modality is diagnostic ultrasound, which is based on non-ionizing radiation and picks up many of the cancers that are generally missed by mammography. However, the presence of speckle noise in ultrasound images has a negative effect on image interpretation. Noise reduction, inconsistencies in capture and segmentation of lesions still remain challenging open research problems in ultrasound images. The target of the proposed research is to enhance the state-of-art computer vision algorithms used in ultrasound imaging and to investigate the role of computer processed images in human diagnostic performance. [Continues.

Experimental and Model-based Terahertz Imaging and Spectroscopy for Mice, Human, and Phantom Breast Cancer Tissues

The goal of this work is to investigate terahertz technology for assessing the surgical margins of breast tumors through electromagnetic modeling and terahertz experiments. The measurements were conducted using a pulsed terahertz system that provides time and frequency domain signals. Three types of breast tissues were investigated in this work. The first was formalin-fixed, paraffin-embedded tissues from human infiltrating ductal and lobular carcinomas. The second was human tumors excised within 24-hours of lumpectomy or mastectomy surgeries. The third was xenograft and transgenic mice breast cancer tumors grown in a controlled laboratory environment to achieve more data for statistical analysis. Experimental pulsed terahertz imaging first used thin sections (10-30 μm thick) of fixed breast cancer tissue on slides. Electromagnetic inverse scattering models, in transmission and reflection modes, were developed to retrieve the tissue refractive index and absorption coefficient. Terahertz spectroscopy was utilized to experimentally collect data from breast tissues for these models. The results demonstrated that transmission mode is suitable for lossless materials while the reflection model is more suitable for biological materials where the skin depth of terahertz waves does not exceed 100 µm. The reflection model was implemented to estimate the polarization of the incident terahertz signal of the system, which was shown to be a hybridization of TE and TM modes. Terahertz imaging of three-dimensional human breast cancer blocks of tissue embedded in paraffin was achieved through the reflection model. The terahertz beam can be focused at depths inside the block to produce images in the x-y planes (z-scan). The time-of-flight analysis was applied to terahertz signals reflected at each depth demonstrating the margins of cancerous regions inside the block as validated with pathology images at each depth. In addition, phantom tissues that mimic freshly excised infiltrating ductal carcinoma human tumors were developed with and without embedded carbon nanometer-scale onion-like carbon particles. These particles exhibited a strong terahertz signal interaction with tissue demonstrating a potential to greatly improve the image contrast. The results presented in this work showed, in most cases, a significant differentiation in terahertz images between cancer and healthy tissue as validated with histopathology images

An electromagnetic imaging system for metallic object detection and classification

PhD ThesisElectromagnetic imaging currently plays a vital role in various disciplines, from engineering to medical applications and is based upon the characteristics of electromagnetic fields and their interaction with the properties of materials. The detection and characterisation of metallic objects which pose a threat to safety is of great interest in relation to public and homeland security worldwide. Inspections are conducted under the prerequisite that is divested of all metallic objects. These inspection conditions are problematic in terms of the disruption of the movement of people and produce a soft target for terrorist attack. Thus, there is a need for a new generation of detection systems and information technologies which can provide an enhanced characterisation and discrimination capabilities. This thesis proposes an automatic metallic object detection and classification system. Two related topics have been addressed: to design and implement a new metallic object detection system; and to develop an appropriate signal processing algorithm to classify the targeted signatures. The new detection system uses an array of sensors in conjunction with pulsed excitation. The contributions of this research can be summarised as follows: (1) investigating the possibility of using magneto-resistance sensors for metallic object detection; (2) evaluating the proposed system by generating a database consisting of 12 real handguns with more than 20 objects used in daily life; (3) extracted features from the system outcomes using four feature categories referring to the objects’ shape, material composition, time-frequency signal analysis and transient pulse response; and (4) applying two classification methods to classify the objects into threats and non-threats, giving a successful classification rate of more than 92% using the feature combination and classification framework of the new system. The study concludes that novel magnetic field imaging system and their signal outputs can be used to detect, identify and classify metallic objects. In comparison with conventional induction-based walk-through metal detectors, the magneto-resistance sensor array-based system shows great potential for object identification and discrimination. This novel system design and signal processing achievement may be able to produce significant improvements in automatic threat object detection and classification applications.Iraqi Cultural Attaché, Londo

Superresolution Enhancement with Active Convolved Illumination

The first two decades of the 21st century witnessed the emergence of “metamaterials”. The prospect of unrestricted control over light-matter interactions was a major contributing factor leading to the realization of new technologies and advancement of existing ones. While the field certainly does not lack innovative applications, widespread commercial deployment may still be several decades away. Fabrication of sophisticated 3d micro and nano structures, specially for telecommunications and optical frequencies will require a significant advancement of current technologies. More importantly, the effects of absorption and scattering losses will require a robust solution since this renders any conceivable application of metamaterials impracticable. In this dissertation, a new approach, called Active Convolved Illumination (ACI), is formulated to address the problem of optical losses in metamaterials and plasmonics. An active implementation of ACI’s predecessor the Π scheme formulated to provide compensation for arbitrary spatial frequencies. The concept of “selective amplification” of spatial frequencies is introduced as a method of providing signal amplification with suppressed noise amplification. Pendry’s non-ideal negative index flat lens is intentionally chosen as an example of a stringent and conservative test candidate. A physical implementation of ACI is presented with a plasmonic imaging system. The superlens integrated with a tunable near-field spatial filter designed with a layered metal-dielectric system exhibiting hyperbolic dispersion. A study of the physical generation of the auxiliary shows how selective amplification via convolution, is implemented by a lossy metamaterial functioning as a near-field spatial filter. Additionally the preservation of the mathematical formalism of ACI is presented by integrating the hyperbolic metamaterial with the previously used plasmonic imaging system. A comprehensive mathematical exposition of ACI is developed for coherent light. This provides a rigorous understanding of the role of selective spectral amplification and correlations during the loss compensation process. The spectral variance of noise is derived to prove how an auxiliary source, which is firstly correlated with the object field, secondly is defined over a finite spectral bandwidth and thirdly, provides amplification over the selected bandwidth can significantly improve the spectral signal-to-noise ratio and consequently the resolution limit of a generic lossy plasmonic superlens