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In the medical field, new technologies are incorporated for the sole purpose of enhancing the quality of life for the patients and even for the normal healthy people. Infrared technology is one of the technologies that have some applications in both the medical and biological fields. In this work, the thermal infrared (IR) measurement is used to investigate the potential of skin cancer detection. IR enjoys non-invasive and non-contact advantages as well as favorable cost, apparently. It is also very well developed regarding the technological and methodological aspects. IR per se is an electro-metric radiation that all objects emit when their temperature is above the absolute zero. And the human body is not different in this regard. The IR range extends, ideally, to cover wavelengths from 800 nanometer to few hundred micrometer. Cancer, in modern life, has grown tangibly due to many factors, such as life expectancies increase, personal habits and ultraviolet radiation exposures among others. Moreover, the significant enhancement of technologies has helped identifying more types of cancers than before. The sole purpose of this work is to investigate further IR technology methods and applications not yet matured in skin cancer detection to enhance the detection ability with higher safety level.

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.

Objective: SPECT data from myocardial perfusion imaging (MPI) are normally displayed as a set of three slices orthogonal to the left ventricular (LV) long axis. For data presentation, the images are orientated about the LV long axis. Therefore, radial slices provide a suitable alternative to standard orthogonal slices, with the advantage of requiring fewer slices to adequately represent the data. In this study, a semi-automatic method is developed for displaying MPI SPECT data as a set of radial slices orientated about the LV axis. The aim is to reduce the number of slices viewed without loss of information and independently from the heart size. Method: Standard short axis slices, orientated perpendicular to the LV axis, are utilized.The skeleton of the segmented myocardium is found and the true LV axis is determined in each central long slice. The LV axis of the whole volume is determined by aligning the axes of all slices. Result: Radial slices centered about this axis were generated by integration over a sector equal to the resolution of the imaging system which was of the order of 1.2 cm. Therefore, assuming a mean LV diameter of 8 cm, 20 slices were sufficient to represent a non-gated study. Gated information could be adequately displayed with 4 slices integrated over an angle of 45. Conclusion: A semi-automatic method for generating radial slices from SPECT MPI short axis slices has been developed.

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.

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.