Monte Carlo method is widely used for simulation of industrial and medical Radiography due to its powerful ability to simulate statistical phenomena such as radiation generation and transport, and detection processes. Digital Radiography is practically applied in two branches of computational Radiography (CR) and direct Radiography (DR). In this study, computational Radiography simulation using the FLUKA Monte Carlo code was developed to provide a framework for studying image contrast as one of the most important parameters of image quality. To investigate the contrast a standard aluminum sensitivity component made of ASTM 1647 standard was used. The simulation of this gauge, along with a complete simulation of the X-ray generator and imaging plate, has been done to assess the image contrast, and and a method has been presented to examine the image contrast. In order to validate the proposed method, practical experiments have been carried out on the contrast sensitivity standard gauge. The simulation results obtained through the proposed method are in good agreement with the results of practical experiments.

Background: This paper presents a method to improve medical Radiography images based on the use of statistical signal moments. Methods: In this paper, the image with noise is considered as a statistical signal, and the noise reduction is performed by using fractional moments. The fractional moment’ s method, on the one hand, has a speed similar to the moment method, and, on the other hand, has not the limitations of the moment method, which sometimes achieves inaccurate results. The proposed method is ultimately examined on radiographic images (CT). Results: The information obtained from the fractional moments of the received signal is a criterion to estimate the noise parameters and the gray scales of the main image. One of the limitations of the proposed method is that the image should be sent several times, because in statistical discussions, we cannot make a decision with only one sample. The error of the proposed noise reduction method in terms of the number of times the original image was sent, is about 0. 009, 0. 0009, 0. 0002, and 0. 0001, for n = 3, n = 6, n = 9 and n = 14, respectively. Conclusion: The simulation results show that the proposed method is more effective than the most conventional noise reduction methods, both in the low signal to noise ratio and in terms of image quality, and is more powerful than the most notable noise removal methods in restoring the subtleties and image details.

The use of raw Radiography results in lung disease identification has not acceptable performance. Machine learning can help identify diseases more accurately. Extensive studies were performed in classical and deep learning-based disease identification, but these methods do not have acceptable accuracy and efficiency or require high learning data. In this paper, a new method is presented for automatic interstitial lung disease identification on Radiography images to address these challenges. In the first step, patient information is removed from the images; the remaining pixels are standardized for more precise processing. In the second step, the reliability of the proposed method is improved by Radon transform, extra data is removed using the Top-hat filter, and the detection rate is increased by Discrete Wavelet Transform and Discrete Cosine Transform. Then, the number of final features is reduced with Locality Sensitive Discriminant Analysis. The processed images are divided into learning and test categories in the third step to create different models using learning data. Finally, the best model is selected using test data. Simulation results on the NIH dataset show that the decision tree provides the most accurate model by improving the harmonic mean of sensitivity and accuracy by up to 1.09times compared to similar approaches.

Introduction: Adult stature represents fundamental biological characteristics of individuals and populations. Stature estimation from skeletal dimensions is one of the essential parameters in the establishment of an individual identity. In this study, Relative parameters of ulna and tibia bones were measured with Radiography and used to establish the mathematical models for stature estimation of Iranian adults (from 20 to 40 years old) in Tehran Province.Materials and Methods: The Radiography of ulna and tibia bones of 49 male and 52 female adult individuals were taken on normal position and the lengths between relative landmarks were measured. The body height of each subject was recorded. Linear regression equations for stature estimation between body height and the lengths of ulna and tibia bones were established.Results: The mean stature of men and women estimated 171±3.6 and160±3.6 the mean length of tibia and ulna in menwere 43.6±0.4 and 27.3±0.6 and in women 40±1.8 and 25±0.74 single linear regression equations and 2 multiple regression equations were obtained.Conclusion: These equations could be used to estimate the body height of Tehranian population aged from 20 to 40. The lengths of the ulna and tibia bones measured on the Radiography films could be useful to stature estimation of the personal forensic identification.

Measuring defect sizes is very important in determining weld strength. This size is also used to evaluate the object on the test for accept or reject criteria according to the relative standards. One of the important methods for measuring different defects' sizes is industrial Radiography testing (RT), a non-destructive testing method. Radiography is carried out using penetrating X or Gamma rays. Radiography is a volumetric method and can give information from the inside the different objects. Various factors, such as the magnification of the radiographic image, the fogginess of the radiographs, the non-point source, and the inherent scattering of X-rays, affect on the measurement and the accuracy of the defect sizing. In particular, size measurement for defects with larger distance respect to the film or Radiography detector can be with more uncertainty. This is due to more shadowiness of rays with larger distances. For Radiography imaging, an industrial Computed Radiography System (CR) was used. General purpose imaging phosphor plates with a laser resolution of 50 micrometers have been used. The X-ray source was an industrial powerful X-ray tube with a voltage of up to 300 kilovolts. In this research, utilizing the distance measurement between the lines in the duplex image quality indicator (DIQI) tool as a known length, the size of defects in standard parts in Sonakit educational test kit that have specific defects is estimated and compared with the original value. To decrease the blurring, a recursive filter method is used to make the edges sharper to better estimate the defect sizes. The results show that the measurement of defects is related to the accuracy of the user in estimating the pixels of the defect regions and the sharpness of the edges. The measurement error is reported between 6% and 19% for the defect measurement in the standard parts examined.

X-rays and neutron Radiography images are one of the most effective defects and structure detection methods. The interactions between neutrons and X-rays are different in the material, and therefore, different information can be obtained from the radiographs. Due to neutron and X-rays photon scattering, focal spot size, electronic noises, etc., the images are blurred and their quality is reduced. In this study, while investigating the radiographs of X-rays, and neutrons, the defects and internal structure of objects are investigated. The results show that neutron Radiography performs very well in detecting the internal structure of low atomic number materials. X-ray Radiography is effective for high atomic numbers as metal. Gaussian convolution is used to enhance the Radiography images and reduce blurriness components. The results show that by reducing the background, the blurriness components can be reduced and the defects areas and internal structure of the objects can be better investigated. Specialists evaluated the results in Radiography,the results show that the expert’, s evaluation approved the image enhancement.

Background: Clinical identification of the inferior alveolar canal (IAC) is crucial before performing surgical interventions such as mandibular third molar extraction or jaw fixation to preserve the integrity of the IAC. This study evaluated the visibility of IAC borders using panoramic Radiography (conventional and CBCT reformatted) and cross-sectional CBCT images. Methods: The conventional panoramic (CP) and CBCT images of 328 patients were evaluated, and the visibility of the IAC was assessed by three examiners across four equal 1 cm-wide regions, from anterior to posterior (Areas 1 to 4). For CBCT, reformatted panoramic (CRP) views were generated using curved multiplanar reformatting at the mandibular mid-root level within the software. Four cross-sectional images were obtained for each region. Visibility was rated as visible (score=1) or non-visible (score=0) across the three imaging modalities. Statistical significance was set at P value<0. 05. Results: Across all three radiographic modalities, the inferior border of the IAC was more consistently visible than the superior border. The highest visibility of the inferior border was observed in Area 4, with visibility rates of 92. 1% for cross-sectional CBCT, 91. 5% for CBCT-reformatted panoramic, and 92. 4% for CP. The lowest visibility was found at the superior border in Area 2, with visibility rates of 86. 9% for cross-sectional CBCT, 80. 2% for CBCT-reformatted panoramic, and 67. 4% for CP. Conclusion: Visualization of the IAC in the distal area of the mental foramen is more challenging than in other areas across all radiographic modalities. Given the superior visibility levels observed in CBCT images, especially for the superior border in Areas 1, 2, and 3, CBCT is recommended over CP Radiography.

Obstruction of turbine blade in different turbines can lead to dangerous accidents, and it is important to check these blades during construction and operation. Due to blockage of air channels, hot spots can lead to blade damage. In this research, X-ray and neutron Radiography are used to examine the blades. Reviews of radiographs show that neutrons produce better images, and internal canals and defects are better seen in these images than X-ray images. It is worth emphasizing that neutron Radiography with gadolinium tagging can determine the obstruction of blade and evaluate the density of ceramic materials inside the blade. Regarding neutron Radiography with film, it can be emphasized that in addition to determining the obstruction of air ducts, the density of ceramic materials inside the ducts can also be evaluated. Also, by tagging with neutron absorbing materials, the contrast of the remaining materials from the ceramic muscle can be increased. Boron, indium and gadolinium can be named as important neutron absorbers. To improve the quality of neutron Radiography images, Gadolinium is mainly used as a high neutron absorber. Although the contrast has increased in the reconstructed images, the reconstructed images from neutron Radiography still show their superiority in showing the ducts and channel blockages. Different image processing methods can be implemented for the contrast enhancement. In this research, Gaussian convolution method is used to increase the contrast of the radiographs. Although contrast has increased in the reconstructed images by the Gaussian convolution method, the reconstructed images from neutron Radiography show the blade structure and its channel blockages accurately. The opinion of Radiography specialists also shows that the neutron Radiography method gives 60% more information than the X-ray method, and the reconstructed images increased the contrast of the images between 10 to 20%.

Visual inspection of baggage in airports and other critical centers takes a great time. In many cases, especially when the objects inside the load are positioned at specific angles, they may not be detectable by rapid visual examinations. Nowadays, X-ray imaging devices are used to detect objects at the passengers' luggage. Radiographic images obtained due to scattering of photon have a degree of fogging, and sometimes the exact diagnosis of objects is difficult. Image processing methods can improve the contrast and the ability to recognize objects. Different noises and scattering levels exist in radiographic images, which justify the use of automated image processing methods. In this study, Gabor filter and wavelet methods with automatic threshold level have been used to improve the quality of images. According to the results, there is no significant difference in the degree of detection of the objects, regardless of the differences in the reconstructed images. In both methods, the contrast and detection capabilities of the primary Radiography images have improved significantly. The computation time of the implementation of the discrete wavelet algorithm is about one eighth of Gabor's time of execution, which can be remarkable given the importance of image processing speed in the area of passenger traffic inspection.

Hats are used to protect the head and for safety or fashion in different cultures and nations. Identifying the fabric, designs, and sewing the hat can give us good information about the history of fashion in different nations. In this study, five hats belonging to the Tavichi family in Italy were investigated by Radiography. The design and construction, morphology, internal structure and damaged regions have been considered. Image processing algorithms can increase the quality of Radiography images. In this research, total variation (TV) and shape from shading (SFS) algorithms have been used to enhance the quality of the images. The TV method is based on minimizing the changes and is used to eliminate noise in images. In the 3D method, a 3D image is created from a 2D image based on the light reflected. The results show that Radiography testing is an effective method for identifying the structure of old hats and can show the internal structure and connections of the components without splitting the fabric. The processed images also have better contrast and can be used to identify components and structures. The Radiography and restoration experts have evaluated the reconstructed images. They have confirmed the effectiveness of processing methods in extracting efficient information from radiographs. Also, the profile lines of the images show that the contrast changes in the reconstructed images are greater than the original radiographs, and the components of the reconstructed images are clearer.