Biomedical Engineering: Applications, Basis and Communications

A new iris location method is presented in this paper. When locating the iris inner boundary, a method for locating the iris inner boundary based on the variation coefficient and multiple disk-structural elements is proposed. The variation coefficient of the iris image is first calculated to be used for the image gray stretch, then the mathematical morphology operations with multiple disk-structural elements are applied to tackle the binary iris image, finally the connected region in line with geometric features of the circle is picked up and its center and radius are calculated, namely for obtaining the iris inner boundary. When positioning the iris outer boundary, a method for locating the iris outer boundary based on the variation coefficient and the gradient amplitude is presented. First, the Gaussian function is employed to filter the iris image using the variation coefficient as its standard deviation, then for each pixel on a circle which is centered at each neighborhood pixel near the circle enter of the iris inner boundary and has a radius with greater than that of the inner boundary, the radial gradient amplitude and tangential gradient amplitude of are computed, finally the iris outer boundary is determined on the maximum of the tangential gradient amplitude. The experiments reveal that this proposed method is effective, and has a fairly simple implementation, a low computational load, a fast location and good accuracy.

In the field of epidemiology, not only the disease and the carriers, but also the surrounding environment and the associated stresses play a vital role. Environmental stresses in a novel habitat may facilitate adaptive shifts. Organisms living under environmental stresses often experience higher mutation rates and display greater phenotypic and genetic variation. There is controversial evidence available in the literature about the impact of environmental stresses on the organisms and the resulting variation in mutation rates and the immune responses. In nature, “selection” and the high energetic costs of stress usually reduce this variation. The prior knowledge of the interaction between the stress and disease epidemics may help to control the disease spread at an early stage. A mathematical model of epidemiology, specifically focusing on the vector borne diseases, with environmental stress is reported in this paper. The model is validated with the aid of stability analysis. During this research, a set of parametric values is obtained using reverse engineering. For this purpose, the parametric evaluation is reported with the help of Monte Carlo Markov Chain (MCMC) reverse engineering. Among other factors, the environmental stresses are also responsible for different dynamics of the same disease, in different continents of the world. The proposed research methodology will help in forecasting the epidemiological problems such as the current threat of coronavirus.

Blood glucose monitoring systems (BGMSs) play a crucial role in health care applications. Invasive measurements are more accurate while non-invasive BGMS encourage self monitoring and reduce the cost of health care. Though multiple sensor data acquisition and suitable processing improve accuracy, self-monitoring becomes difficult in such non-invasive systems due to multiple signal acquisition. This paper investigates a non-invasive BGMS prototype that renders accurate measurements by statistically processing a single sensor data. The developed prototype is based on near-infrared (NIR) spectroscopy, which provides an electronic voltage that gets mapped to corresponding blood glucose level. This mapping is proposed using two different statistical regression approaches, parametric Bayesian Regression (BR) approach and the non-parametric Gaussian Process Regression (GPR) approach. Dataset is acquired from 33 subjects who visited Ramaiah Medical College Hospital, India. On each subject, voltage from the BGMS prototype and corresponding invasively obtained blood glucose level have been recorded. The BR and GPR approaches are trained with 75% of the data while the remaining 25% is used for testing. Test results show that BR approach renders root mean square error (RMSE) of 3.7mg/dL, while the mean absolute percentage error (MAPE) is around 2.5. The GPR with different radial basis function kernels revealed that a multiquadric kernel provides a lowest RMSE of 3.28mg/dL and lowest MAPE of 2.2, thus outperforming the parametric BR approach. Investigations also show that for a training data of less than 15 entries, BR renders better accuracy than the GPR approach.

In the present era, hip joint replacement is a well-known medical issue to which the male patients are mostly prone to. There are different designs available for the hip replacement process; however, each design has its benefits as well as its limitations. This study provides the optimum design solution for the hip joint replacement by changing the dimensional parameters of the hip joint. The three geometrical parameters, ball diameter, Neck length and stem length of the hip joint, are selected for the base design. Furthermore, parameters i.e. ball material and stem material are also considered in this study to reduce the weight. The above-mentioned factors have four levels, which help to generate experimental design by using design of experiment methodology. The titanium alloy, Co–Cr-alloy, stainless steel and ultra-high molecule weight polyethylene (UHMWPE) have been selected as working material for modeling of the hip joint. The L${}_{16}$ orthogonal array is simulated in ANSYS FEM solver (Version-14.5). The FEM simulation techniques have been applied, and modeling steps are also developed. Two responses i.e. Von mises stress and body weight are selected to optimize the base design for further study. The signal-to-noise ratio analysis for stress shows that the most significant factor is ball diameter and least significant factor is neck length of the hip joint. For reducing the weight of the entire model, material of stem body is the most significant factor whereas the least significant factor is stem length.

Owing to its cost effective and non-ionizing procedure, ultrasound imaging has become a primary tool for detection and diagnosis of breast tumors compared to mammography or biopsy. But the precise interpretation of the images obtained is limited due to the intensity inhomogeneties, speckle noise, low contrast, etc. present in the observed images. Also, tumors can appear in different echogenic patterns including visually challenging to detect isoechoic and slightly hypoechoic tumors, and distinguishing the tumor by means of intensity variations only will not suffice. Therefore it is important to preprocess the image to remove unwanted noise and variations, and thereby provide adequate enhancement to highlight the region of interest. In this paper, a segmentation method is proposed where the query images are clustered using fuzzy c-means clustering (FCM) after neutrosophic filtering and enhancement followed by extraction of textural and edge features through the use of Gabor and compass filters. In this work, Prewitt compass filters were implemented to capture the maximum region difference between the tumor and background. By using filters of different orientations, sufficient texture and edge information were able to be captured by using the combination of Gabor and compass filters, which can then aid in the accurate detection of isoechoic and hypoechoic tumors. The neutrosophic preprocessing implemented in this work models the ultrasound noise as combination of Gaussian and Rayleigh distributions, and shows better signal-to-noise ratio than traditionally modeling speckle as Gaussian alone. The proposed method gave an increase of 42.25%, 40.15%, 81.32% and 50.15% and a decrease of 95.83% compared to standard Otsu thresholding, an increase of 23.34%, 19.57%, 24.53% and 24.71% and a decrease of 91.81% compared to simple FCM and an increase of 37.89%, 44.64%, 35.86% and 48.11% and a decrease of 93.68% compared to active contour segmentation in structural similarity index, Jaccard index, Dice coefficient and accuracy and mean square error, respectively. On contrasting with recent learning-based methodologies for tumor segmentation, the proposed method gave comparable accuracy without the need for large training database and expensive graphical processing unit (GPU) computing capabilities.

Biometric identification of the human face is a pervasive subject which deals with a wide range of disciplines such as image processing, computer vision, pattern recognition, artificial intelligence, and cognitive psychology. Extracting key face points for developing software and commercial devices of face surgery analysis is one of the most challenging fields in computer image and vision processing. Many studies have developed a variety of techniques to extract facial features from color and gray images. In recent years, using depth information has opened up new approaches to researchers in the field of image processing. Hence, in this study, a statistical method is proposed to extract key nose points from color-depth images (RGB-D) of the face front view. In this study, the Microsoft Kinect sensor is used to produce the face RGB-D images. To assess the capability of the proposed method, this algorithm is applied to 20 RGB-D face images from the database collected in the ICT lab of Sahand University of Technology and promising results are achieved for extracting key points of the face. The results of this study indicated that using the available information in two different color-depth bands could make key points of the face more easily accessible and bring better results and we can conclude that the proposed algorithm provided a promising outcome for extracting the positions of key points.

Alzheimer’s disease (AD) is a type of neuronal brain disorder that is degenerative and results in memory loss, skills and cognitive changes. The primary diagnostic tests for the disorder are defined to be total brain atrophy and hippocampal atrophy. Early diagnosis is significant and the design of automatic systems is necessary for this disorder. A potential biomarker for AD is described using a hippocampal magnetic resonance imaging volumetry system that possesses certain limitations. This paper aims to analyze the transition of stages from normal cognition to different forms that ultimately leads to Alzheimer’s disease. The magnetic resonance imaging (MRI) images of different stages are derived from the standard database for the segregation of hippocampal region. Later, the morphological and radiomic features are extracted from the hippocampal regions of different stages, since the hippocampus plays a major role in memory. Classification of extracted features was performed using machine learning algorithms like ensemble tree classifiers. The classification results based on performance parameters specify that the bagged tree classifier is more efficient. The 4-way classification has an accuracy of 95.6% indicating certain misclassification between the two classes MCI and PMCI. To categorize these two classes, a 2-way classification is described that has an accuracy of 98.6%. With these results, an effective method is defined for the analysis and identification of the different progressive stages of Alzheimer’s disease.

Vascular complications are responsible for most of the morbidity and mortality in diabetic patients. Effective strategies to improve circulation appear to be another important issue in addition to the interventions of blood glucose control. Previous studies have shown that the biological effects on humans after using the materials containing ceramic particles emits far infrared radiation (FIR). The present study is to investigate the warming effect of the fabrics containing specified metals in diabetic patients. A total of 28 diabetic patients were blinded and randomly assigned to treatment group ($N=18$) and control group ($N=10$), respectively. The subjects of treatment group were ministered with the blankets with fibers containing the specified metals, while the subjects of control group were provided with blankets of ordinary material. The skin temperature and microcirculatory perfusion were monitored before and after 20-min use of the blankets at shoulder and bilateral calves. After warming with blankets, the treatment group revealed a higher increased ratio of skin perfusion than control ($p<0.05$), while there was no prominent variation on the wavelet spectrum of the perfusion signal. Although it is already a known fact that passive FIR warming has the advantages of safety and convenience, our results suggest that warming by wearing the textiles containing the specified metals with high FIR emissivity is a solution for daily skin care of diabetic patients.