scholarly journals Image Processing Method on Radiographic Image of Piping using MATLAB: Enhancement and Detection Process

2019 ◽  
Vol 8 (12) ◽  
pp. 5557-5561
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Signal To Noise Ratio ◽  
Original Data ◽  
Edge Enhancement ◽  
Radiographic Image ◽  
Radiographic Images ◽  
Filtering Method ◽  
Full Reference ◽  
Opening And Closing

Image enhancement is a pre-processing process to enhance the quality and information content of original data. This paper investigates two methods of image augmentation that is deployed to remove noise and improve radiographic images. The first method is image filtering, which consists of smoothing, sharpening and edge enhancement (Sobel & Prewitt) operations. The filtering method emphasizes certain characteristics or eliminates other details. While the second method is morphological technique that utilizes the opening and closing operation, which employed to removed distorted noise and imperfection on the processed images. Each method and operation applied to the image is evaluated subjectively based on the enhance image quality. The image quality measured using MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio) which is a full reference metrics. The image quality results are compared to give a wide picture on the performance of the enhanced images. The image processing operations accomplished by using MATLAB image processing toolbox

Retinal Digital Image Quality Improvement as A Diabetes Retinopatic Disease Detection Effort

2020 ◽  
Vol 4 (2) ◽  
pp. 53-60
Author(s):  
Latifah Listyalina ◽  
Yudianingsih Yudianingsih ◽  
Dhimas Arief Dharmawan
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Image Quality ◽  
Digital Image ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Vital Role ◽  
Retinal Images ◽  
Signal To Noise ◽  
Noise Ratio

Image processing is a technical term useful for modifying images in various ways. In medicine, image processing has a vital role. One example of images in the medical world, namely retinal images, can be obtained from a fundus camera. The retina image is useful in the detection of diabetic retinopathy. In general, direct observation of diabetic retinopathy is conducted by a doctor on the retinal image. The weakness of this method is the slow handling of the disease. For this reason, a computer system is required to help doctors detect diabetes retinopathy quickly and accurately. This system involves a series of digital image processing techniques that can process retinal images into good quality images. In this research, a method to improve the quality of retinal images was designed by comparing the methods for adjusting histogram equalization, contrast stretching, and increasing brightness. The performance of the three methods was evaluated using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Signal to Noise Ratio (SNR). Low MSE values and high PSNR and SNR values indicated that the image had good quality. The results of the study revealed that the image was the best to use, as evidenced by the lowest MSE values and the highest SNR and PSNR values compared to other techniques. It indicated that adaptive histogram equalization techniques could improve image quality while maintaining its information.

RADIOGRAPHIC IMAGE ENHANCEMENT USING HYBRID ALGORITHM

2016 ◽  
Vol 78 (6-7) ◽  
Author(s):  
Varin Chouvatut ◽  
Ekkarat Boonchieng
Keyword(s):  
Image Quality ◽  
Spatial Resolution ◽  
Computed Radiography ◽  
Median Filter ◽  
Correct Diagnosis ◽  
Image Contrast ◽  
Radiographic Image ◽  
Radiographic Image Enhancement ◽  
Radiographic Images ◽  
Low Contrast

Radiographic image quality is important in the medical field since it can increase the visibility of anatomical structures and even improve the medical diagnosis. Because the image quality depends on contrast, noise, and spatial resolution, images with low contrast, a lot of noises, or low resolution will decrease image quality, leading to an incorrect diagnosis. Therefore, radiographic images should be enhanced to facilitate medical expertise in making correct diagnosis. In this paper, radiographic images are enhanced by hybrid algorithms based on the idea of combining three image processing techniques: Contrast Limited Adaptive Histogram Equalization for enhancing image contrast, Median Filter for removing noises, and Unsharp Masking for increasing spatial resolution. Two series of medical images consisting of 20 x-ray images and 20 computed radiography images are enhanced with this method. Peak Signal to Noise Ratio (PSNR) and image contrast are computed in order to measure image quality. The results indicate that the enhanced images have better PSNR.

scholarly journals Using the Natural Scenes’ Edges for Assessing Image Quality Blindly and Efficiently

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Saifeldeen Abdalmajeed ◽  
Jiao Shuhong
Keyword(s):  
Image Quality ◽  
Image Quality Assessment ◽  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Natural Image ◽  
Natural Scenes ◽  
Signal To Noise ◽  
Enhanced Sensitivity ◽  
Full Reference ◽  
Distortion Type

Two real blind/no-reference (NR) image quality assessment (IQA) algorithms in the spatial domain are developed. To measure image quality, the introduced approach uses an unprecedented concept for gathering a set of novel features based on edges of natural scenes. The enhanced sensitivity of the human eye to the information carried by edge and contour of an image supports this claim. The effectiveness of the proposed technique in quantifying image quality has been studied. The gathered features are formed using both Weibull distribution statistics and two sharpness functions to devise two separate NR IQA algorithms. The presented algorithms do not need training on databases of human judgments or even prior knowledge about expected distortions, so they are real NR IQA algorithms. In contrast to the most general no-reference IQA, the model used for this study is generic and has been created in such a way that it is not specified to any particular distortion type. When testing the proposed algorithms on LIVE database, experiments show that they correlate well with subjective opinion scores. They also show that the introduced methods significantly outperform the popular full-reference peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) methods. Besides they outperform the recently developed NR natural image quality evaluator (NIQE) model.

scholarly journals Intervariability in radiographic parameters and general evaluation of a low-dose fluoroscopic technique in patients with idiopathic scoliosis

2021 ◽  
Vol 10 (9) ◽  
pp. 205846012110432
Author(s):  
Christian Wong ◽  
Jens Adriansen ◽  
Jytte Jeppsen ◽  
Andreas Balslev-Clausen
Keyword(s):  
Image Quality ◽  
Idiopathic Scoliosis ◽  
Low Dose ◽  
Signal To Noise Ratio ◽  
Radiographic Images ◽  
Dose Area Product ◽  
General Evaluation ◽  
Radiographic Parameters ◽  
Cobb’S Angle ◽  
Noise Ratio

Background Radiographic images in adolescent idiopathic scoliosis (AIS) have a potential radiation-induced oncogenic effect; thus lowering radiation dose by using fluoroscopic imaging technique of low-dose fluoroscopic technique (LFT) which might be relevant for clinical evaluation. Purpose To compare radiographs of LFT with gold standard radiographs for AIS ordinary radiographic technique (ORT). Material and Methods Image quality was evaluated for LTF and ORT of a child phantom and two 3D-printed models (3DPSs) of AIS. We measured the primary physical characteristics of noise, contrast, spatial resolution, signal-to-noise ratio, and contrast-to-noise ratio. Three independent evaluators assessed the radiographs by observer-based methods of image criteria (ICS) and visual grading analysis(VGAS). Radiation doses were evaluated by the dose-area-product (DAP) of the 25 phantom radiographs. Reliability and agreement of Cobb’s angle (CA) and other radiographic parameters were evaluated on the 3DPSs and reliability on 342 LFT. Results The average noise and contrast were approximately 15-fold higher for LFT. SNR and CNR were similar. Overall, ICS and VGAS were 3-fold higher for ORT than for LFT for L3 and similar for Th6. Reliability and agreement were good for the experimental LFT, and the interclass correlation coefficient for CA was 0.852 for the clinical LFT. The average DAP and effective dose for LFT were 8-fold lower than those for ORT. Conclusion In conclusion, LFT is reliable for CA measurements and is thus useful for clinical outpatient follow-up evaluation. Even though the image quality is lower for LFT than ORT, the merits are the substantially reduced radiation and a lowered malignancy risk without compromising the measurement of Cobb’s angle, thus following the principles of ALARA.

scholarly journals Development of Stand Alone Application Tool for Processing and Quality Measurement of Weld Imperfection Image Captured by μ-Focused Digital Radiography Using MATLAB- Based Graphical User Interface

2012 ◽  
Vol 9 (2) ◽  
pp. 64 ◽  
Author(s):  
PZ Nadila ◽  
YHP Manurung ◽  
SA Halim ◽  
SK Abas ◽  
G Tham ◽  
...  
Keyword(s):  
Image Processing ◽  
User Interface ◽  
Image Quality ◽  
Digital Radiography ◽  
Graphical User Interface ◽  
Signal To Noise Ratio ◽  
Quality Measurement ◽  
Noise Removal ◽  
Signal To Noise ◽  
Noise Ratio

Digital radiography incresingly is being applied in the fabrication industry. Compared to film- based radiography, digitally radiographed images can be acquired with less time and fewer exposures. However, noises can simply occur on the digital image resulting in a low-quality result. Due to this and the system’s complexity, parameters’ sensitivity, and environmental effects, the results can be difficult to interpret, even for a radiographer. Therefore, the need of an application tool to improve and evaluate the image is becoming urgent. In this research, a user-friendly tool for image processing and image quality measurement was developed. The resulting tool contains important components needed by radiograph inspectors in analyzing defects and recording the results. This tool was written by using image processing and the graphical user interface development environment and compiler (GUIDE) toolbox available in Matrix Laboratory (MATLAB) R2008a. In image processing methods, contrast adjustment, and noise removal, edge detection was applied. In image quality measurement methods, mean square error (MSE), peak signal-to-noise ratio (PSNR), modulation transfer function (MTF), normalized signal-to-noise ratio (SNRnorm), sensitivity and unsharpness were used to measure the image quality. The graphical user interface (GUI) wass then compiled to build a Windows, stand-alone application that enables this tool to be executed independently without the installation of MATLAB. 

scholarly journals COMPARATIVE STUDY OF THE DIFFERENT VERSIONS OF THE GENERAL IMAGE QUALITY EQUATION

2019 ◽  
Vol IV-2/W5 ◽  
pp. 493-500 ◽  
Author(s):  
A. Q. Valenzuela ◽  
J. C. G. Reyes
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Paradigm Shift ◽  
Rating Scale ◽  
Signal To Noise Ratio ◽  
National Image ◽  
Image Processing Techniques ◽  
Quality Rating ◽  
Processing Techniques ◽  
Image Quality Rating

<p><strong>Abstract.</strong> The General Image Quality Equation (GIQE) is an analytical tool derived by regression modelling that is routinely employed to gauge the interpretability of raw and processed images, computing the most popular quantitative metric to evaluate image quality; the National Image Interpretability Rating Scale (NIIRS). There are three known versions of this equation; GIQE&amp;nbsp;3, GIQE&amp;nbsp;4 and GIQE&amp;nbsp;5, but the last one is scarcely known. The variety of versions, their subtleties, discontinuities and incongruences, generate confusion and problems among users. The first objective of this paper is to identify typical sources of confusion in the use of the GIQE, suggesting novel solutions to the main problems found in its application and presenting the derivation of a continuous form of GIQE&amp;nbsp;4, denominated GIQE&amp;nbsp;4C, that provides better correlation with GIQE&amp;nbsp;3 and GIQE&amp;nbsp;5. The second objective of this paper is to compare the predictions of GIQE&amp;nbsp;4C and GIQE&amp;nbsp;5, regarding the maximum image quality rating that can be achieved by image processing techniques. It is concluded that the transition from GIQE&amp;nbsp;4 to GIQE&amp;nbsp;5 is a major paradigm shift in image quality metrics, because it reduces the benefit of image processing techniques and enhances the importance of the raw image and its signal to noise ratio.</p>

scholarly journals Edge-texture 2D image quality metrics suitable for evaluation of image interpolation algorithms

2015 ◽  
Vol 12 (2) ◽  
pp. 405-425 ◽  
Author(s):  
Sanja Maksimovic-Moicevic ◽  
Zeljko Lukac ◽  
Miodrag Temerinac
Keyword(s):  
Image Quality ◽  
Image Quality Assessment ◽  
Signal To Noise Ratio ◽  
Quality Measures ◽  
Weighting Factor ◽  
Quality Indices ◽  
Compression Algorithms ◽  
Full Reference ◽  
Step Algorithm ◽  
Perceptual Assessment

A new objective, full-reference metrics of image quality is proposed in this paper. It should match perceptual (subjective) image quality assessment in a better way. The proposed method consists of two quality measures which separately indicate image quality on edges and in texture areas which are calculated in a three-step algorithm. The ?soft mask? is initially found for separation in edge and texture areas. Then, two MSEs (mean square error) with corresponding two PSNRs (peak signal-to-noise ratio) for edge and texture are calculated using soft mask as the weighting factor. Finally, the obtained two PSNRs are re-calculated into the two quality indices for edges and texture. Additionally, the separation factor, defined as percentage of edge areas in image, is considered, describing the influence of the image content on perceptual assessment. The proposed 2D metrics is especially suited for evaluations of different interpolation and compression algorithms.

EFFECTS OF IMAGE PROCESSING UPON RADIOGRAPHIC IMAGE QUALITY

2019 ◽  
Vol 128 (4) ◽  
pp. e160-e161
Author(s):  
D.D. RICE ◽  
J. CLARK ◽  
C. WADHWANI ◽  
K. ABRAMOVITCH ◽  
M. KATTADIYIL
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Radiographic Image

scholarly journals Neural Network Based Filtering Method for Cancer Detection

2021 ◽  
Vol 15 (1) ◽  
pp. 163-169
Author(s):  
J. Jaya ◽  
A. Sasi ◽  
B. Paulchamy ◽  
K.J. Sabareesaan ◽  
Sivakumar Rajagopal ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Cancer Detection ◽  
Human Body ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Linear Filters ◽  
Distribution Method ◽  
Filtering Method ◽  
Specific Distribution

Objective: The growth of anomalous cells in the human body in an uncontrolled manner is characterized as cancer. The detection of cancer is a multi-stage process in the clinical examination. Methods: It is mainly involved with the assistance of radiological imaging. The imaging technique is used to identify the spread of cancer in the human body. This imaging-based detection can be improved by incorporating the Image Processing methodologies. In image processing, the preprocessing is applied at the lower-level abstraction. It removes the unwanted noise pixel present in the image, which also distributes the pixel values based on the specific distribution method. Results: Neural Network is a learning and processing engine, which is mainly used to create cognitive intelligence in various domains. In this work, the Neural Network (NN) based filtering approach is developed to improve the preprocessing operation in the cancer detection process. Conclusion: The performance of the proposed filtering method is compared with the existing linear and non-linear filters in terms of Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR) and Image Enhancement Factor (IEF).

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