Image Colourisation for Early Infarct Detection

Although early detection of infarct sign can provide early treatment to patients, early infarct detection on brain images is well-known to be difficult due to their subtle signs. A method is proposed to aid the detection of infarct through image colourisation based on Hounsfield units. A test was conducted in a private university to evaluate the algorithm, and the method appeared helpful and robust. The limited results showed that students with and without medical background had improved their ability of detecting early sign by 5.5% with the help of image colourisation

Signal -To-Noise Ratio Estimation In Scanning Electron Microscope Imaging System

Two new methods, the Autoregressive(AR) model and the Mixed Lagrange Time Delay Estimation Autoregressive (MLTDEAR) model, are developed to estimate the Signal-to-Noise Ratio (SNR) from a single image for the Scanning Electron Microscope (SEM) Imaging System application

Autoregressive linear least square single scanning electron microscope image signal-to-noise ratio estimation

A technique based on linear Least Squares Regression (LSR) model is applied to estimate signal-to-noise ratio (SNR) of scanning electron microscope (SEM) images. In order to test the accuracy of this technique on SNR estimation, a number of SEM images are initially corrupted with white noise. The autocorrelation function (ACF) of the original and the corrupted SEM images are formed to serve as the reference point to estimate the SNR value of the corrupted image. The LSR technique is then compared with the previous three existing techniques known as nearest neighbourhood, first-order interpolation, and the combination of both nearest neighborhood and first-order interpolation. The actual and the estimated SNR values of all these techniques are then calculated for comparison purpose. It is shown that the LSR technique is able to attain the highest accuracy compared to the other three existing techniques as the absolute difference between the actual and the estimated SNR value is relatively small

Nonlinear least squares regression for single image scanning electron microscope signal-to-noise ratio estimation

A new method based on nonlinear least squares regression (NLLSR) is formulated to estimate signal-to-noise ratio (SNR) of scanning electron microscope (SEM) images. The estimation of SNR value based on NLLSR method is compared with the three existing methods of nearest neighbourhood, first-order interpolation and the combination of both nearest neighbourhood and first-order interpolation. Samples of SEM images with different textures, contrasts and edges were used to test the performance of NLLSR method in estimating the SNR values of the SEM images. It is shown that the NLLSR method is able to produce better estimation accuracy as compared to the other three existing methods. According to the SNR results obtained from the experiment, the NLLSR method is able to produce approximately less than 1% of SNR error difference as compared to the other three existing methods

Deep convolutional networks for magnification of DICOM Brain Images

Convolutional neural networks have recently achieved great success in Single Image Super-Resolution (SISR). SISR is the action of reconstructing a high-quality image from a low-resolution one. In this paper, we propose a deep Convolutional Neural Network (CNN) for the enhancement of Digital Imaging and ommunications in Medicine (DICOM) brain images. The network learns an end-to-end mapping between the low and high resolution images. We first extract features from the image, where each new layer is connected to all previous layers. We then adopt residual learning and the mixture of convolutions to reconstruct the image. Our network is designed to work with grayscale images, since brain images are originally in grayscale. We further compare our method with previous works, trained on the same brain images, and show that our method outperforms them

Adaptive Tuning Noise Estimation for Medical Images Using Maximum Element Convolution Laplacian

Noise in medical images can adversely affect the outcome of clinical diagnosis. In analyzing medical images, noise estimation is necessary to ensure consistency and performance quality ofimage processing techniques. In this study, we present a noise estimation method, namely Adaptive Tuning Noise Estimation (ATNE) that implements convolution Laplacian noise estimation. ATNE is based on subtraction of Gabor wavelet detected edges of images, and involves the relation element based on the parameters of the input image. This method allows a fast estimation of the image noise variance without a heavy computational cost. To assess the effectiveness of ATNE, 1000 mammograms are used. We pre-process these images to be Rician distributed with various noise variances. ATNE is used to estimate the noise level of the resulting images. We compare ATNE with other noise estimation methods, and the results show that ATNE outperforms other related methods with a lower percentage of error for noise variance estimation

Effect of Cu and PdCu wire bonding on bond pad splash

Cu wire bonding research has exploded exponentially in the past few years. Many studies have been carried out to understand the different behaviours of Cu wire and Au wire. One of the observations on Cu wire bonding is the excessive formation of aluminium (Al) splash on the bond pad due to a higher bond force. This leads to pad peeling and bond failure resulting in poor reliability performance of Cu and PdCu wire semiconductor devices. It is known that the Al splash is influenced by the front-end pad metal process and back-end wire bond process. Reported is the design of an experiment carried out to study a few factors that could influence the Al splash. The characterisation work is implemented to understand the bond pad structure using the focused ion beam (FIB) followed by a hardness test of bond pad metallisation. Then the mechanical cross-section is taken to measure the Al splash in three different directions. The results show that Al splash can be controlled by optimising the bond pad thickness, hardness and additive for reliable Cu and PdCu wire bonding

Termination Factor for Iterative Noise Reduction in MRI Images Using Histograms of Second-order Derivatives

Histograms of second-order derivatives are generated from the pixel data of MRI images. The histograms are then used to calculate a factor that is to be used for iterative processing. The factor is intended to limit the number of iterations, with the goal of preventing further loss of detail. The factor uses two conditions that depend on the profiles of the histograms. The methodology uses sample MRI images and versions of these images with Rician noise introduced into them. The noisy images are subjected to iterative noise reduction with a recursive averaging filter. The control tests in the methodology use the ground truth images to limit the number of iterations, with PSNR and SSIM peaks used as the measurements for determining when the iterations stop. The other tests use the proposed termination factor for the limitation. The results of the tests are compared to determine the effectiveness of the termination factor. The proposed termination factor does not cause divergence, but there are still different numbers of iterations in the case of MRI images with image subjects that have discrete regions and details resembling noise. The tests also reveal that differences between the histograms of derivatives and Laplace curves have to be retained in order to prevent loss of information

Wireless Control System for Six-Legged Autonomous Insect Robot

Insect robots are a special type of robots that designed to imitate the behavior of insects. Insect robots have many advantages such as the ability to move over uneven terrain, less power consumption and smaller in size. This paper shows the progress made during the development of a six-legged robot system inspired by ants and crickets. The resulted robot is able to mimic insects in terms of gait pattern and physical size. The robot is controlled wirelessly by using a Bluetooth xBee module and remote devices including a mobile phone with android application, a personal computer with windows software, and a Bluetooth wireless controller made the Arduino development platform