A Chronology of Interpolation: From Ancient Astronomy to Modern Signal and Image Processing

This paper presents a chronological overview of the developments in interpolation theory, from the earliest times to the present date. It brings out the connections between the results obtained in different ages, thereby putting the techniques currently used in signal and image processing into historical perspective. A summary of the insights and recommendations that follow from relatively recent theoretical as well as experimental studies concludes the presentation

Registration of pre-operative lung cancer PET/CT scans with post-operative histopathology images

Non-invasive imaging modalities used in the diagnosis of lung cancer, such as Positron Emission Tomography (PET) or Computed Tomography (CT), currently provide insuffcient information about the cellular make-up of the lesion microenvironment, unless they are compared against the gold standard of histopathology.The aim of this retrospective study was to build a robust imaging framework for registering in vivo and post-operative scans from lung cancer patients, in order to have a global, pathology-validated multimodality map of the tumour and its surroundings.;Initial experiments were performed on tissue-mimicking phantoms, to test different shape reconstruction methods. The choice of interpolator and slice thickness were found to affect the algorithm's output, in terms of overall volume and local feature recovery. In the second phase of the study, nine lung cancer patients referred for radical lobectomy were recruited. Resected specimens were inflated with agar, sliced at 5 mm intervals, and each cross-section was photographed. The tumour area was delineated on the block-face pathology images and on the preoperative PET/CT scans.;Airway segments were also added to the reconstructed models, to act as anatomical fiducials. Binary shapes were pre-registered by aligning their minimal bounding box axes, and subsequently transformed using rigid registration. In addition, histopathology slides were matched to the block-face photographs using moving least squares algorithm.;A two-step validation process was used to evaluate the performance of the proposed method against manual registration carried out by experienced consultants. In two out of three cases, experts rated the results generated by the algorithm as the best output, suggesting that the developed framework outperforms the current standard practice.Non-invasive imaging modalities used in the diagnosis of lung cancer, such as Positron Emission Tomography (PET) or Computed Tomography (CT), currently provide insuffcient information about the cellular make-up of the lesion microenvironment, unless they are compared against the gold standard of histopathology.The aim of this retrospective study was to build a robust imaging framework for registering in vivo and post-operative scans from lung cancer patients, in order to have a global, pathology-validated multimodality map of the tumour and its surroundings.;Initial experiments were performed on tissue-mimicking phantoms, to test different shape reconstruction methods. The choice of interpolator and slice thickness were found to affect the algorithm's output, in terms of overall volume and local feature recovery. In the second phase of the study, nine lung cancer patients referred for radical lobectomy were recruited. Resected specimens were inflated with agar, sliced at 5 mm intervals, and each cross-section was photographed. The tumour area was delineated on the block-face pathology images and on the preoperative PET/CT scans.;Airway segments were also added to the reconstructed models, to act as anatomical fiducials. Binary shapes were pre-registered by aligning their minimal bounding box axes, and subsequently transformed using rigid registration. In addition, histopathology slides were matched to the block-face photographs using moving least squares algorithm.;A two-step validation process was used to evaluate the performance of the proposed method against manual registration carried out by experienced consultants. In two out of three cases, experts rated the results generated by the algorithm as the best output, suggesting that the developed framework outperforms the current standard practice

Interpolation of Low Resolution Images for Improved Accuracy in Human Face Recognition

In a wide range of face recognition applications, such as the surveillance camera in law enforcement, it is cannot provide enough resolution of face for recognition. The first part of this research demonstrates the impact of the image resolution on the performance of the face recognition system. The performance of several holistic face recognition algorithms is evaluated for low-resolution face images. For the classification, this research uses k-nearest neighbor (k-NN) and Extreme Learning Machine-based neural network (ELM). The recognition rate of these systems is a function in the image resolution. In the second part of this research, nearest neighbor, bilinear, and bicubic interpolation techniques are applies as a preprocessing step to increase the resolution of the input image to obtain better results. The results show that increasing the image resolution using the mentioned interpolation methods improves the performance of the recognition systems considerably

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Applications in Electronics Pervading Industry, Environment and Society

This book features the manuscripts accepted for the Special Issue “Applications in Electronics Pervading Industry, Environment and Society—Sensing Systems and Pervasive Intelligence” of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the “Applications in Electronics Pervading Industry, Environment and Society” (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs

Segmentation and quantification of spinal cord gray matter–white matter structures in magnetic resonance images

This thesis focuses on finding ways to differentiate the gray matter (GM) and white matter (WM) in magnetic resonance (MR) images of the human spinal cord (SC). The aim of this project is to quantify tissue loss in these compartments to study their implications on the progression of multiple sclerosis (MS). To this end, we propose segmentation algorithms that we evaluated on MR images of healthy volunteers. Segmentation of GM and WM in MR images can be done manually by human experts, but manual segmentation is tedious and prone to intra- and inter-rater variability. Therefore, a deterministic automation of this task is necessary. On axial 2D images acquired with a recently proposed MR sequence, called AMIRA, we experiment with various automatic segmentation algorithms. We first use variational model-based segmentation approaches combined with appearance models and later directly apply supervised deep learning to train segmentation networks. Evaluation of the proposed methods shows accurate and precise results, which are on par with manual segmentations. We test the developed deep learning approach on images of conventional MR sequences in the context of a GM segmentation challenge, resulting in superior performance compared to the other competing methods. To further assess the quality of the AMIRA sequence, we apply an already published GM segmentation algorithm to our data, yielding higher accuracy than the same algorithm achieves on images of conventional MR sequences. On a different topic, but related to segmentation, we develop a high-order slice interpolation method to address the large slice distances of images acquired with the AMIRA protocol at different vertebral levels, enabling us to resample our data to intermediate slice positions. From the methodical point of view, this work provides an introduction to computer vision, a mathematically focused perspective on variational segmentation approaches and supervised deep learning, as well as a brief overview of the underlying project's anatomical and medical background

Robust methods for medical image registration with application in clinical diagnosis

Automated analysis of medical imaging data allows both researchers and clinicians to develop more accurate and faster diagnoses. Image registration plays an essential role in both longitudinal studies and group analysis, allowing the combination of different imaging modalities, and automatic parcellation of regions of interest. Despite its wide use, image registration is still challenging with many issues such as artefacts, scarcity of correspondences, multi-modality, and computational complexity. Additionally, due to the lack of highly annotated datasets, the validation of image registration, and specifically non-linear registration, is also problematic. In this thesis several of these issues are addressed by introducing: a framework to validate non-linear registration methods; a robust and fast algorithm for non-linear registration; and validating the proposed methods in a conventional analysis. Current techniques used for non-linear image registration validation are explored, and it is shown that techniques based on label overlap are both not theoretically valid while also having poor accuracy. This analysis further leads to the development of a multiscale metric to minimize these problems. Also, a method based on the Demons Framework is proposed to improve the convergence speed of non-linear registration algorithms, and further extended to be robust in the presence of intensity inhomogeneities and contrast variations. The proposed methods are validated in a synthetic simulation platform with a known ground truth, compared with a manually traced region-of-interest, and tested in a voxel-based morphometry analysis of real data. It is shown that the proposed methods outperform other leading registration methods in both the synthetic simulation study and the manually traced data, and present reliable results in the voxel-based morphometry analysis. Furthermore, the impact of different registration algorithms is explored through the voxel-based morphometry study, and shown to affect the final results and their interpretation.Open Acces

Advanced Applications of Rapid Prototyping Technology in Modern Engineering

Rapid prototyping (RP) technology has been widely known and appreciated due to its flexible and customized manufacturing capabilities. The widely studied RP techniques include stereolithography apparatus (SLA), selective laser sintering (SLS), three-dimensional printing (3DP), fused deposition modeling (FDM), 3D plotting, solid ground curing (SGC), multiphase jet solidification (MJS), laminated object manufacturing (LOM). Different techniques are associated with different materials and/or processing principles and thus are devoted to specific applications. RP technology has no longer been only for prototype building rather has been extended for real industrial manufacturing solutions. Today, the RP technology has contributed to almost all engineering areas that include mechanical, materials, industrial, aerospace, electrical and most recently biomedical engineering. This book aims to present the advanced development of RP technologies in various engineering areas as the solutions to the real world engineering problems