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PurposeThe aim of the research project which resulted in this work is to achieve a cost‐effective approach for instantaneous hyperspectral imaging.Design/methodology/approachThis paper presents a simulation study and an experimental evaluation of a novel imaging spectroscopy technique, where multi‐channel image data are acquired instantaneously and transformed into spectra by using a statistical modelling approach. A digital colour camera equipped with an additional colour filter array was used to acquire an instantaneous single image that was demosaicked to generate a multi‐channel image. A statistical transformation approach was employed to convert this image into a hyperspectral one.FindingsThe feasibility of this method was investigated through extensive simulation and experimental tasks where promising results were obtained.Practical implicationsThe small size of the initially acquired single instantaneous image makes this approach useful for applications where video‐rate hyperspectral imaging is required.Originality/valueFor the first time, a simplified prototype of this novel imaging spectroscopy technique was built and evaluated experimentally. And the results were compared with those of a more ideal simulation study. Recommendations for how to improve the prototype were also suggested as a result of the comparison between the simulation and the prototype evaluation results.

A challenging problem in dental implant surgery is to evaluate the stability of the implant. In this simulation study, an experimental phantom is used to represent a jawbone with a dental implant. It is made of a little pool filled with soft-tissue equivalent material and a disc of fresh Oakwood with a metal screw. Varying levels of contact between screw and wood are simulated by screwing in or out the screw. Initially, the screw is screwed in and fixed firmly in wood. Thereafter, the screw is screwed out, a half turn each time, to increase the gap gradually between wood and screw. Pulse-echo ultrasound is used and the power spectra of the received echo-signals are computed. These spectra are normalized then analyzed by using the partial least squares method to estimate the corresponding implant stiffness grade in terms of number of turns when beginning from the initial tight-screw state then screwing out the screw. A coefficient of determination R2 of 96.4% and a mean absolute error of ±0.23 turns are achieved when comparing real and estimated values of stiffness grades, indicating the efficiency of this approach.

New advances within the recently rediscovered field of Compressed Sensing (CS) have opened for a great variety of new possibilities in the field of image reconstruction and more specifically in medical image reconstruction. In this work, a new approach using a CS-based algorithm is proposed and used in order to solve limited-angle problems (LAPs), like the ones that typically occur in computed tomography or electron microscope. This approach is based on a variant of the Robbins-Monro stochastic approximation procedure, developed by Egaziarian, using regularization by a spatially adaptive filter. This proposal consists on filling the gaps of missing or unobserved data with random noise and enabling a spatially adaptive denoising filter to regularize the data and reveal the underlying topology. This method was tested on different 3D transmission electron microscope datasets that presented different missing data artifacts (e.g, wedge or cone shape). The test results show a great potential for solving LAPs using the proposed technique.

Myoelectric (EMG) and mechano-myographic (MMG) signals recorded during muscular activities are useful not only for monitoring and assessing these activities but can also help in providing effective rehabilitation for disabled patients as well as constructing and controlling sophisticated prosthesis for various amputees. While the existing training methods using EMG and MMG signal data have compelling benefits, many engineering challenges still remain with regard to the sensory control system. Studies show that prosthesis’ users support arguments considering the continued development of comfortable, reliable and better functionality of the sensors of the available applications. The aim of this paper is to study muscular activity by using an armband with embedded OptoMyoGraphy (OMG) sensors arranged as a one dimensional sensor array. The armband and the signals’ acquisition system are designed and developed at the beginning of the research project. The used sensors are capable of measuring lateral dimensional changes, on the landscape formed by the skin surface, caused due to underlying muscles’ activities around the forearm region. As a novel contribution, the paper discusses a possible methodology to control portable upper limb prosthetic devices using the OMG technique. Furthermore, the developed armband can be used as a biofeedback system for rehabilitation purposes in upper-limb amputees (ULAs) cases with below-the-forearm amputation and help them gain control over their remaining sensory-motor system and get rid of phantom and/or residual limb pain (PLP, RLP).

In Positron Emission Tomography (PET), the coincident emission of gamma photon pairs constitutes the useful signals that should be detected and processed to reconstruct the desired PET images of the studied objects. However, along with the useful signal, noise is also generated and added to the detected signals that are sorted with respect to their line-ofresponse and arranged as a sinogram for each two-dimensional slice. In this paper, the type and properties of noise in PET sinogram data will be evaluated. Furthermore, the effect of the used linear and non-linear image denoising and reconstruction procedures on the type of noise will be analyzed. For this purpose, the Gaussian filter, the Median filter, the Patch Confidence k-Nearest Neighbor filter (PCkNN) and the Block Matching 3D filter (BM3D) were used to denoise PET image data, as well as the maximum likelihood expectation maximization algorithm (MLEM) and the Filtered Back Projection algorithm (FBP) to reconstruct the PET images.

In Positron Emission Tomography (PET), image quality is highly degraded by noise. Therefore, two main PETimage denoising approaches can be used: pre- and postreconstruction denoising. In the pre-reconstruction approach the PET sinogram is denoised before forwarding it to the image reconstruction algorithm. On the other hand, the reconstructed PET-image is denoised in the post-reconstruction approach. In this study, comparison of image quality of the resulting images of the pre- and post-reconstruction approaches is performed. In both types of approaches, the Gaussian filter, the Non-Local Means filter (NLM), the Block-Matching and 3D filter (BM3D), the K-Nearest Neighbors Filter (KNN) and the Patch Confidence K-Nearest Neighbors Filter (PCkNN) are utilized. These approaches are evaluated on a simulated PET-phantom dataset, a real-life physical thorax-phantom PET dataset as well as a reallife MicroPET-scan dataset of a mouse. The performance is measured using the Signal-to-Noise Ratio (SNR) in addition to the Contrast-to-Noise Ratio (CNR) in the resulting images.

This paper presents a sensitivity analysis of using instantaneous multichannel two-dimensional (2D) imaging to achieve instantaneous 2D imaging spectroscopy. A simulated multiple-filter mosaic was introduced and used to acquire multichannel data which were transformed into spectra. The feasibility of two different transformation approaches (the concrete pseudoinverse approach and a statistical approach) was investigated through extensive experimental tasks. A promising statistical method was identified to be used for accurate estimation of spectra from multichannel data. Comparison between estimated and measured spectra shows that higher estimation accuracy can be achieved when using a larger number of usable multiple-filter combinations in the mosaic.

Many studies have shown the usefulness of hyperspectral crop reflectance data for detecting plant pathological stress. However, there is still a need to identify unique signatures for specific stresses amidst the constantly changing background associated with normal crop growth and development. Comparing spatial and temporal patterns in crop spectra can provide such signatures. This work was concerned with characterising and estimating fungal disease severity in a spring wheat crop. This goal can be accomplished by using a reference data set consisting of hyperspectral crop reflectance data vectors and the corresponding disease severity field assessments. The hyperspectral vectors were first normalised into zero-mean and unit-variance vectors by performing various combinations of spectral- and band-wise normalisations. Then, after applying the same normalisation procedures to the new hyperspectral data, a nearest-neighbour classifier was used to classify the new data against the reference data. Finally, the corresponding stress signatures were computed using a linear transformation model. High correlation was obtained between the classification results and the corresponding field assessments of fungal disease severity, confirming the usefulness and efficiency of this approach. The effects of increased disease severity could be characterised by analysing the resulting disease signatures obtained when applying the different normalisation procedures. The low computational load of this approach makes it suitable for real-time on-vehicle applications.

A new more efficient variant of a recently developed algorithm for unsupervised fuzzy clustering is introduced. A Weighted Incremental Neural Network (WINN) is introduced and used for this purpose. The new approach is called FC-WINN (Fuzzy Clustering using WINN). The WINN algorithm produces a net of nodes connected by edges, which reflects and preserves the topology of the input data set. Additional weights, which are proportional to the local densities in input space, are associated with the resulting nodes and edges to store useful information about the topological relations in the given input data set. A fuzziness factor, proportional to the connectedness of the net, is introduced in the system. A watershed-like procedure is used to cluster the resulting net. The number of the resulting clusters is determined by this procedure. Only two parameters must be chosen by the user for the FC-WINN algorithm to determine the resolution and the connectedness of the net. Other parameters that must be specified are those which are necessary for the used incremental neural network, which is a modified version of the Growing Neural Gas algorithm (GNG). The FC-WINN algorithm is computationally efficient when compared to other approaches for clustering large high-dimensional data sets.

A novel algorithm for unsupervised fuzzy clustering is introduced. The algorithm uses a so-called Weighted Fixed Neural Network (WFNN) to store important and useful information about the topological relations in a given data set. The algorithm produces a weighted connected net, of weighted nodes connected by weighted edges, which reflects and preserves the topology of the input data set. The weights of the nodes and the edges in the resulting net are proportional to the local densities of data samples in input space. The connectedness of the net can be changed, and the higher the connectedness of the net is chosen, the fuzzier the system becomes. The new algorithm is computationally efficient when compared to other existing methods for clustering multi-dimensional data, such as color images.

Radiological techniques based on X-rays are well established in medical diagnostics and there are known risks associated with the use of ionizing radiation like X-rays. That explains why the X-ray technology is constantly under development in the pursuit of new technologies that can contribute to reduce radiation dose to patients. Since the reduction of a radiation dose generally results in a poorer image quality, we have investigated whether the use of digital image processing can provide panoramic radiographs with enhanced image quality. An automated image processing algorithm was proposed and employed for this purpose. Panoramic X-ray examination is an important and common tool in dental radiology, used especially for children and teenagers. The technique is used to create an overview of a patient's jaw.

In Positron Emission Tomography (PET), the resulted images are highly deteriorated by noise. In this study, we propose a new denoising framework using the Patch Confidence K-Nearest Neighbors Filter (PCKNN) to reduce noise in the sinogram before forwarding it to the reconstruction procedure. This method has been evaluated on a simulated PET image of a phantom, and the performance has been compared with several conventional methods in the literature. The results have shown that the PET image quality can be substantially improved in term of increased signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR).

A novel method for characterizing and visualizing the progression of waves along the walls of the carotid artery is presented. The new approach is noninvasive and able to simultaneously capture the spatial and the temporal propagation of wavy patterns along the walls of the carotid artery in a completely automated manner. Spatiotemporal and spatiospectral 2D maps describing these patterns (in both the spatial and the frequency domains, resp.) were generated and analyzed by visual inspection as well as automatic feature extraction and classification. Three categories of cases were considered: pathological elderly, healthy elderly, and healthy young cases. Automatic differentiation, between cases of these three categories, was achieved with a sensitivity of 97.1% and a specificity of 74.5%. Two features were proposed and computed to measure the homogeneity of the spatiospectral 2D map which presents the spectral characteristics of the carotid artery wall's wavy motion pattern which are related to the physical, mechanical (e.g., elasticity), and physiological properties and conditions along the artery. These results are promising and confirm the potential of the proposed method in providing useful information which can help in revealing the physiological condition of the cardiovascular system. Correction in: International Journal of Biomedical Imaging, Volume 2017 (2017), Article ID 4237858. https://doi.org/10.1155/2017/4237858

An automated method for the segmentation and tracking of moving vessel walls in 2D ultrasound image sequences is introduced. The method was tested on simulated and real ultrasound image sequences of the carotid artery. Tracking was achieved via a self organizing neural network known as Growing Neural Gas. This topology-preserving algorithm assigns a net of nodes connected by edges that distributes itself within the vessel walls and adapts to changes in topology with time. The movement of the nodes was analyzed to uncover the dynamics of the vessel wall. By this way, radial and longitudinal strain and strain rates have been estimated. Finally, wave intensity signals were computed from these measurements. The method proposed improves upon wave intensity wall analysis, WIWA, and opens up a possibility for easy and efficient analysis and diagnosis of vascular disease through noninvasive ultrasonic examination.

Segmenting hyperspectral images is an important task for simplifying the analysis of the data by focusing on a certain part of the data set or on data samples of the same or at least "nearby" spectral properties. A new class of neuro-fuzzy systems, based on so-called weighted incremental neural networks (WINN), is briefly introduced, exemplified and finally used for unsupervised segmentation of hyperspectral images. The WINN algorithm produces a net of nodes connected by edges, which reflects and preserves the topology of the input data set. Additional weights, which are proportional to the local data densities in the input space, are associated with the resulting nodes and edges to store useful information about the topological relations in the given input data set. A fuzziness factor, related to the connectedness of the net, is introduced in the system. A watershed-like procedure is used to cluster the resulting net. The number of the resulting clusters is determined by this procedure. Experimental results underline the usefulness and efficiency of the proposed neuro-fuzzy system for multi-dimensional data clustering and image segmentation, especially hyperspectral images.