Suhaila Abd Halim

The geometrical feature of weld defect in assessing digital radiographic image

Digital Radiography is a filmless radiographic image that has been introduced to replace conventional film. It is one of the nondestructive techniques (NDT) that is used to indicate the internal condition of the sample being inspected. It is also an efficient technique in defect detection. Weld defect is any flaws that compromise the usefulness of finished weld specimen. The segmentation of weld defect in radiographic image is an important task which can be used for defect feature analysis by extracting its boundary. This study was performed to extract the weld defect and evaluate its geometrical feature. The boundary of defect is extracted by converting the image into binary form. The defect boundary is detected by recognizing the black pixel of eight neighborhoods of 3×3 filtering. The coordinates of the boundary pixel are stored and used to calculate the information of defect features. The information can be used by interpreter to interpret a defect.

Application of message embedding technique in ElGamal Elliptic Curve Cryptosystem

The use of elliptic curve for public key cryptosystem has been developed almost twenty years ago. The strength of the elliptic curve cryptosystem relies on the elliptic curve discrete logarithm problem (ECDLP). In this paper, a method of embedding plaintexts to points on the elliptic curve is proposed. The method deploys elliptic curve based on encryption and decryption processes by using the two protocols, the ElGamal Elliptic Curve Cryptosystem. Maple 10 is used to determine and compute points on the elliptic curves as well as to compute the addition and scalar multiplication operations using the proposed method.

Performance of noise removal methods with image quality parameter on μ-focused digital radiographic image

This study deals with noise removal methods on radiographic image which acquired using μ-focused digital radiography machine. The purposes of the study are to enhance the quality of radiographic image using noise removal methods and analyze the image quality using error measurement metrics. Median, gaussian, average and circular averaging filters are the noise removal methods applied on original radiographic image to produce processed image. Then, the processed image are measured in terms of Signal to Noise Ratio (SNR), Maximum Absolute Error (MAXABS), Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Root Mean Square Error (RMSE). Besides that, the image quality is also measured using Modulation Transfer Function (MTF). Results show that gaussian filter gives the best enhancement of image quality based on error metrics and MTF. The development of image enhancement and quality measurement methods implementation are done using MATLAB R2009a.

An algorithm for beta-spline surface reconstruction from multi slice CT scan images using MATLAB pmode

This study focuses on the development of algorithm to reconstruct surface of a human head using Beta-Spline technique. The development of algorithm started with the data point extraction from CT scan images to parallel computation. Parallel computation is used to handle with the large number of data points. Ars Cluster, which is a low-cost parallel laboratory in UiTM Shah Alam, is used in this computation on a MATLAB pmode platform with MATLAB Distributed Computing Server (MDCS). The computation time is taken and the results shows that the parallel computation will gives less computation time compared to by using sequential. The computation time and the surface of a human head are displayed on a MATLAB GUI.

The performance of PDE-based image denoising on radiographic images

Image is normally contaminated by noise during its acquisition process. In order to deal with this problem, image denoising is introduced as the preliminary process in image processing. The approach of performing image denoising is currently an active research area as the techniques used frequently have some advantages or disadvantages, depending on the quality of the image used. The commonly used spatial filters such as median, mean and Gaussian filters would cause blurring effect on image. Therefore, a PDE-based image denoising will be introduced to overcome the problem. The technique is selected because it has a good denoising effect and hence, a better edge preservation on image. In this paper, a second-order PDE and a fourth-order PDE are computationally solve using finite difference method and implemented on radiographic images for the purpose of removing image noise. Then, the performances of the models are evaluated using peak signal to noise ratio (PSNR) to evaluate the image quality. In addition...

Weld defect features extraction on digital radiographic image using Chan-Vese model

Segmentation is the most important and critical task that widely used in image processing. In this study, Level set based on Chan-Vese model is explored and applied to detect weld defect on digital radiographic image. Region of interest (ROI) from original image is defined to reduce the area of image been processed. The ROI image is smoothen using median 3×3 filter to improve its quality for better detection result. Then, the defect in the ROI image is segmented using Chan-Vese model with two different size of rectangle as initial contour for initialization. The algorithm for the whole processes is implemented using MATLAB R2009a. From the detection result, its features are calculated that have been calibrated to absolute units (mm) from pixel units. The application of segmentation technique such as level set method able to assist radiographer in detecting and defining the properties of the defect accurately.

Surface reconstruction from computed tomography (CT) image of human head with the effect of noise

Computed Tomography (CT) is a diagnostic imaging test to examine internal organs, bones, soft tissue and blood vessels. In image, quality cannot be compromised as it could affect the diagnosis results during examination stage. Hence, digital image processing can be implemented in order to improve the image quality for better analysis results. The aims of this paper are to detect and extract the contour of each slice CT image that produced data points, calculate the distance error of the extracted data points and implement the 3D surface reconstruction. The study of noise behavior and effect on surface is really important for image enhancement area. In this study, a human head from multi slices CT scan images is used for implementation purposes. Iterative image reconstruction of beta-spline curve fitting is introduced as reconstruction algorithm. The developed algorithms are implemented using MATLAB R2016a. A noise is introduced on each slice of CT image in order to evaluate the performance of the developed algorithm. Results show that the surface reconstruction algorithm is successfully implemented on a set of CT scan image with the existence of noise.

Partial Differential Equation (PDE) Based Image Smoothing System for Digital Radiographic Image

Over the last few decades, partial differential equations (PDEs) have become one of the significant mathematical methods that are widely used in the current image processing area. One of its common applications is in image smoothing which is an essential preliminary step in image processing. Smoothing is necessary because it affects the result of further processes in image processing. In this project, a system based on second-order PDE and fourth-order PDE models are developed and implemented in digital radiographic image that contain welding defects. The results obtained from these models show better image quality as compared to conventional filters, such as median filter and Gaussian filter. The system is beneficial in assisting radiographic inspectors to produce a better evaluation and analysis on defects in welding images. In addition, non-destructive testing consultants from industries and academician from universities can also utilize this system for training and research purposes.

Fourth-order partial differential equation noise removal on welding images

Partial differential equation (PDE) has become one of the important topics in mathematics and is widely used in various fields. It can be used for image denoising in the image analysis field. In this paper, a fourth-order PDE is discussed and implemented as a denoising method on digital images. The fourth-order PDE is solved computationally using finite difference approach and then implemented on a set of digital radiographic images with welding defects. The performance of the discretized model is evaluated using Peak Signal to Noise Ratio (PSNR). Simulation is carried out on the discretized model on different level of Gaussian noise in order to get the maximum PSNR value. The convergence criteria chosen to determine the number of iterations required is measured based on the highest PSNR value. Results obtained show that the fourth-order PDE model produced promising results as an image denoising tool compared with median filter.

Perona Malik anisotropic diffusion model using Peaceman Rachford scheme on digital radiographic image

In image processing, it is important to remove noise without affecting the image structure as well as preserving all the edges. Perona Malik Anisotropic Diffusion (PMAD) is a PDE-based model which is suitable for image denoising and edge detection problems. In this paper, the Peaceman Rachford scheme is applied on PMAD to remove unwanted noise as the scheme is efficient and unconditionally stable. The capability of the scheme to remove noise is evaluated on several digital radiography weld defect images computed using MATLAB R2009a. Experimental results obtained show that the Peaceman Rachford scheme improves the image quality substantially well based on the Peak Signal to Noise Ratio (PSNR). The Peaceman Rachford scheme used in solving the PMAD model successfully removes unwanted noise in digital radiographic image.