Journal articles: 'Image quality analysis'

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Desai, Miss Shivpriya, and Dr A. P. Rao. "Seed Quality Analysis Using Image Processing and ANN." International Journal of Trend in Scientific Research and Development Volume-1, Issue-4 (June 30, 2017): 705–9. http://dx.doi.org/10.31142/ijtsrd137.

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Lim, P. C., T. Kim, S. I. Na, K. D. Lee, H. Y. Ahn, and J. Hong. "ANALYSIS OF UAV IMAGE QUALITY USING EDGE ANALYSIS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4 (September 19, 2018): 359–64. http://dx.doi.org/10.5194/isprs-archives-xlii-4-359-2018.

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Abstract. UAVs (Unmanned aerial Vehicles) can acquire images easily without large cost. For this reason, use of UAV is spreading to diverse fields such as orthoimages and DEM/DSM production. The spatial resolution of images is usually expressed as a GSD (Ground Sampling Distance). The GSD from UAV has higher performance than other platforms such as satellites and aircraft because it shoot at low altitude. However, blurring and noise may occur on UAV images due to the weather and the stability of UAV. However, since the GSD from UAV cannot sufficiently meet the spatial resolving power of the actual image system, a criterion for determining the spatial resolution of image is needed. Therefore we emphasize that the quality of the image needs to be analysed. Actual performance indicators such as GRD (Ground Resolved Distance) and NIIRS (National Image Interpretability Rating Scales), which can be measured through image analysis, are representative examples of image quality interpretation. It is possible to extract NIIRS form image quality related parameters such as MTF (Modulation Transfer Function), RER (Relative Edge Response) and SNR (Signal to Noise Ratio). In this paper, we aim to apply the Edge analysis method to UAV and to analyse the result. The analysis result showed that while GSD and NIIRS were highly dependent to imaging altitude, GRD and image sharpness showed optimal altitude ranges. The exact optimal range varied between images taken at different weather conditions. While we need a further study, this may indicate that edge analysis may provide an optimal operational altitude range suitable for the sensors.

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Aljahdali, Majed H., Alexander Woodman, Lamiaa Al-Jamea, Saeed M. Albatati, and Chris Williams. "Image Analysis for Ultrasound Quality Assurance." Ultrasonic Imaging 43, no. 3 (February 15, 2021): 113–23. http://dx.doi.org/10.1177/0161734621992332.

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The quality assurance (QA) of ultrasound transducers is often identified as an area requiring continuous development in terms of the tools available to users. Periodic evaluation of the transducers as part of the QA protocol is important, since the quality of the diagnostics. Some of the key criteria determining the process of developing a QA protocol include the complexity of setup, the time required, accuracy, and potential automation to achieve scale. For the current study, a total of eight different ultrasound machines (12 transducers) with linear transducers were obtained separately. The results from these 12 transducers were used to validate the protocol. WAD-QC was used as part of this study to assess in-air reverberation patterns obtained from ultrasound transducers. Initially, three in-air reverberation images obtained from normal transducers and three obtained from defective transducers were used to calculate the uniformity parameters. The results were applied to 12 other images obtained from independent sources. Image processing results with WAD-QC were verified with imageJ. A comparison of raw data for uniformity showed consistency, and using controls based on mean absolute deviation yielded identical results. WAD-QC can be considered as a powerful mechanism for quick, efficient, and accurate analysis of in-air reverberation patterns obtained from ultrasound transducers.

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Gupta, Pooja, and Kuldip Pahwa. "Clock Algorithm Analysis for Increasing Quality of Digital Images." International Journal of Image and Graphics 16, no. 03 (July 2016): 1650016. http://dx.doi.org/10.1142/s0219467816500169.

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A digital image is not an exact snapshot of reality; it is only a discrete approximation. Thus, the captured images are always bit different from the images actually perceived by human eyes. These variations occur due to varying lighting conditions, weathers conditions like rain and fog, distance of scene from camera, image capturing angle, etc. The problem becomes more severe if these images are captured using low resolution image capturing devices like: Mobile phones, CCTV Cameras, Webcam, VGA cameras etc. Image enhancement addresses a solution of generating a high quality image from its low contrast version. Color enhancement is a process that differentiates objects in an image; as well as provides the detailed information of that image. This paper proposes color enhancement of low resolution digital images using clock algorithm. It is claimed that the proposed clock algorithm employed here produces good quality images in comparison with the existing color enhancement techniques. The simulation results proved that the proposed clock algorithm efficiently enhances the quality of digital low resolution images and analytically their quality improvement is observed in terms of peak signal to noise ratio (PSNR), mean square error (MSE) and bit error rate (BER) over the existing color enhancement techniques.

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Golub, Y. I. "Image quality assessment." «System analysis and applied information science», no. 4 (January 5, 2022): 4–15. http://dx.doi.org/10.21122/2309-4923-2021-4-4-15.

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Quality assessment is an integral stage in the processing and analysis of digital images in various automated systems. With the increase in the number and variety of devices that allow receiving data in various digital formats, as well as the expansion of human activities in which information technology (IT) is used, the need to assess the quality of the data obtained is growing. As well as the bar grows for the requirements for their quality.The article describes the factors that deteriorate the quality of digital images, areas of application of image quality assessment functions, a method for normalizing proximity measures, classes of digital images and their possible distortions, image databases available on the Internet for conducting experiments on assessing image quality with visual assessments of experts.

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Starovoitov, V. V., Y. I. Golub, and M. M. Lukashevich. "Digital fundus image quality assessment." «System analysis and applied information science», no. 4 (January 5, 2022): 25–38. http://dx.doi.org/10.21122/2309-4923-2021-4-25-38.

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Diabetic retinopathy (DR) is a disease caused by complications of diabetes. It starts asymptomatically and can end in blindness. To detect it, doctors use special fundus cameras that allow them to register images of the retina in the visible range of the spectrum. On these images one can see features, which determine the presence of DR and its grade. Researchers around the world are developing systems for the automated analysis of fundus images. At present, the level of accuracy of classification of diseases caused by DR by systems based on machine learning is comparable to the level of qualified medical doctors.The article shows variants for representation of the retina in digital images by different cameras. We define the task to develop a universal approach for the image quality assessment of a retinal image obtained by an arbitrary fundus camera. It is solved in the first block of any automated retinal image analysis system. The quality assessment procedure is carried out in several stages. At the first stage, it is necessary to perform binarization of the original image and build a retinal mask. Such a mask is individual for each image, even among the images recorded by one camera. For this, a new universal retinal image binarization algorithm is proposed. By analyzing result of the binarization, it is possible to identify and remove imagesoutliers, which show not the retina, but other objects. Further, the problem of no-reference image quality assessment is solved and images are classified into two classes: satisfactory and unsatisfactory for analysis. Contrast, sharpness and possibility of segmentation of the vascular system on the retinal image are evaluated step by step. It is shown that the problem of no-reference image quality assessment of an arbitrary fundus image can be solved.Experiments were performed on a variety of images from the available retinal image databases.

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Mateika, Darius, and Romanas Martavicius. "ANALYSIS OF THE COMPRESSION RATIO AND QUALITY IN AERIAL IMAGES." Aviation 11, no. 4 (December 31, 2007): 24–28. http://dx.doi.org/10.3846/16487788.2007.9635973.

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In modern photomap systems, images are stored in centralized storage. Choosing a proper compression format for the storage of an aerial image is an important problem. This paper analyses aerial image compression in popular compression formats. For the comparison of compression formats, an image quality evaluation algorithm based on the calculation of the mean exponent error value is proposed. An image quality evaluation experiment is presented. The distribution of errors in aerial images and explanation of the causes for worse than usual compression effect are analysed. An integrated solution for the aerial image compression problem is proposed and the compression format most suitable for aerial images is specified.

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Sheik, Syed Amma. "Analysis of image quality metric for ROI using image restoration techniques." International Journal of Engineering & Technology 3, no. 2 (May 18, 2014): 262. http://dx.doi.org/10.14419/ijet.v3i2.2359.

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Analysis of an image plays vital role in the image processing field, which leads to the inventions of applications in the area of telemedicine, remote sensing via satellites and other spacecrafts, radar, sonar and acoustic image processing etc. This concept is a key factor in research field. One of the common image analysis is use of Region of Interest (ROI) image, which is an effortless way of analyzing images. This paper proposes a method to analyze the Image Quality Metric (IQM) for a ROI based color image. IQM is accomplished by the use of the three image restoration algorithms such as Blind deconvolution algorithm, Wiener Filtering algorithm and Lucy Richardson algorithm. Keywords: Blind Deconvolution, Lucy Richardson, Point Spread Function, Region of Interest, Image Quality Metric, Wiener Filter.

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Anwar, Fahmi. "Image Quality Analysis of PNG Images on WhatsApp Messenger Sending." Telematika 14, no. 1 (January 15, 2012): 1–12. http://dx.doi.org/10.35671/telematika.v14i1.1114.

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Technology is growing rapidly, especially in communication with various types of information services such as internet-based messages. One of the most popular internet-based messages in Indonesia is WhatsApp Messenger. WhatsApp is a chat application that can be used on many platforms. Message sending on WhatsApp is carried out end-to-end encryption from the sender to the message recipient. The sending of messages in PNG images is secured using end-to-end encryption and compressed according to predefined rules. This study analyzes Image Compression and Alpha channel in PNG by comparing PNG images before being sent with PNG images that have gone through the sending process on WhatsApp using the test-driven development (TDD) method. The analysis results contain comparisons based on the RMSE, SSIM, PSNR, and MD5 hash values. Delivery with a gallery image attachment type using an image transparent background changes to a white image background. While those with a background other than transparent have good image quality because it has a PSNR value of more than 35 dB, and submissions with document attachment types do not experience changes in MD5 hash value and image quality.

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Blake, R. E., and A. Juozapavičius. "Quality of Colour Image Segmentation: the Measures." Nonlinear Analysis: Modelling and Control 5 (December 5, 2000): 53–66. http://dx.doi.org/10.15388/na.2000.5.0.15240.

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Colour image segmentation is an important tool in many applications, like robotics, computer vision and data compression. Differing highly from grey-scale images, the colour segmentation usually has more complicated and time consuming algorithms and is controlled by a larger set of parameters. The measures of quality of colour segmentation are presented in the article, enabling users to evaluate the most efficient set of parameters for a procedure of colour segmentation.The evaluation of parameters are suggested to be provided by heuristical and statistical methods, also presenting relationship of such measures.

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Yun, Eun-Kyung, and Sung-Bae Cho. "Adaptive fingerprint image enhancement with fingerprint image quality analysis." Image and Vision Computing 24, no. 1 (January 2006): 101–10. http://dx.doi.org/10.1016/j.imavis.2005.09.017.

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Starovoitov, V. V., and F. V. Starovoitov. "COMPARATIVE ANALYSIS OF NO-REFERENCE QUALITY MEASURES FOR DIGITAL IMAGES." «System analysis and applied information science», no. 1 (May 4, 2017): 24–32. http://dx.doi.org/10.21122/2309-4923-2017-1-24-32.

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This paper presents results of a comparative analysis of 34 measures published in the scientific literature and used for evaluation of the image quality without a reference image. In English literature, they are called no-reference (NR) measure or measures NR-type. The first article, the term no-reference, was published in 2000 and each year a growing number of publications on new measures NR-type. However, comparative studies of such measures is not practically conducted. Such measures are very important for a) just made photo quality evaluation, b) assessment of image enhancement transformations and selection of their parameters (such as contrast and brightness adjustments, tone-mapping, decolorization and others). Publicly available image quality databases used for study no-reference quality measures (TID2013, etc.), contain 4-5 variants of images distorted by predefined transformations with unknown parameters. We presented six types of experiments to analyze correlation of the computed numerical quality values with visual estimates of the test images quality. Four of the experiments are new: comparison of images after gamma-correction and contrast enhancement with different parameters, as well as analysis of the retouched images and photos taken with different focal length. It was shown experimentally that no one of the known no-reference quality assessment measure is universal, and the calculated value cannot be converted to a quality scale, excluding factors influencing the distortion of the image. Most of the studied measures calculates local estimates in small neighborhoods, and their arithmetic mean is the quality index of the image. If the image contains large areas of uniform brightness, the measures of this type can give incorrect quality assessment, which will not correlate with the visual assessments.

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Yao, Chen, Yan Xia, and Jiamin Zhu. "Image Enhancement by Frequency Analysis." MATEC Web of Conferences 228 (2018): 02008. http://dx.doi.org/10.1051/matecconf/201822802008.

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Because of lighting or the quality of CMOS/CCD, poor images are often gained, which greatly affect subjective observation. Image enhancement can improve the contrast of poor image. In our paper, we propose a new image enhancement algorithm based on frequency analysis. A central energy of FFT is utilized for computation of image enhancement factors. A linear mapping is used for image mapping. Finally, some experimental results are shown for illustration of our algorithm advantage.

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Yu, Cheng Yi, Yi Ying Chang, Yen Chieh Ouyang, Shen Chuan Tai, and Tzu Wei Yu. "Image Tracking and Analysis Algorithm by Independent Component Analysis." Applied Mechanics and Materials 44-47 (December 2010): 1622–27. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.1622.

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Along with digitizing and multimedia era, the image has not changed from the original entity into any changes can be dealt with digital preservation methods. Although the digital image capture technology means more and more developed, but there are still many variables affect the quality of an image. An image quality usually depends on the user's usage or changes in the natural environment. Due to the natural environment of the most common factors that influence is light, so an image of the brightness distribution over the target object caused by extreme hardly recognizable condition common. Therefore, we will use the independent component analysis of an input color images Red, Green, and Blue three Color Space to the main component analysis, in order to achieve the target tracking and analysis.

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Yang, Miao, Ge Yin, Haiwen Wang, Jinnai Dong, Zhuoran Xie, and Bing Zheng. "A Underwater Sequence Image Dataset for Sharpness and Color Analysis." Sensors 22, no. 9 (May 7, 2022): 3550. http://dx.doi.org/10.3390/s22093550.

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The complex underwater environment usually leads to the problem of quality degradation in underwater images, and the distortion of sharpness and color are the main factors to the quality of underwater images. The paper discloses an underwater sequence image dataset called TankImage-I with gradually changing sharpness and color distortion collected in a pool. TankImage-I contains two plane targets, a total of 78 images. It includes two lighting conditions and three different water transparency. The imaging distance is also changed during the photographing process. The paper introduces the relevant details of the photographing process, and provides the measurement results of the sharpness and color distortion of the sequence images. In addition, we verify the performance of 14 image quality assessment methods on TankImage-I, and analyze the results of 14 image quality assessment methods from the aspects of sharpness and color, which provides a reference for the design and improvement of underwater image quality assessment algorithm and underwater imaging system design.

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Longo, Renata, Maura Tonutti, Luigi Rigon, Fulvia Arfelli, Diego Dreossi, Elisa Quai, Fabrizio Zanconati, Edoardo Castelli, Giuliana Tromba, and Maria A. Cova. "Clinical study in phase- contrast mammography: image-quality analysis." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 372, no. 2010 (March 6, 2014): 20130025. http://dx.doi.org/10.1098/rsta.2013.0025.

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The first clinical study of phase-contrast mammography (PCM) with synchrotron radiation was carried out at the Synchrotron Radiation for Medical Physics beamline of the Elettra synchrotron radiation facility in Trieste (Italy) in 2006–2009. The study involved 71 patients with unresolved breast abnormalities after conventional digital mammography and ultrasonography exams carried out at the Radiology Department of Trieste University Hospital. These cases were referred for mammography at the synchrotron radiation facility, with images acquired using a propagation-based phase-contrast imaging technique. To investigate the contribution of phase-contrast effects to the image quality, two experienced radiologists specialized in mammography assessed the visibility of breast abnormalities and of breast glandular structures. The images acquired at the hospital and at the synchrotron radiation facility were compared and graded according to a relative seven-grade visual scoring system. The statistical analysis highlighted that PCM with synchrotron radiation depicts normal structures and abnormal findings with higher image quality with respect to conventional digital mammography.

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Avilés-Rodríguez, Gener José, Juan Iván Nieto-Hipólito, María de los Ángeles Cosío-León, Gerardo Salvador Romo-Cárdenas, Juan de Dios Sánchez-López, Patricia Radilla-Chávez, and Mabel Vázquez-Briseño. "Topological Data Analysis for Eye Fundus Image Quality Assessment." Diagnostics 11, no. 8 (July 23, 2021): 1322. http://dx.doi.org/10.3390/diagnostics11081322.

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The objective of this work is to perform image quality assessment (IQA) of eye fundus images in the context of digital fundoscopy with topological data analysis (TDA) and machine learning methods. Eye health remains inaccessible for a large amount of the global population. Digital tools that automize the eye exam could be used to address this issue. IQA is a fundamental step in digital fundoscopy for clinical applications; it is one of the first steps in the preprocessing stages of computer-aided diagnosis (CAD) systems using eye fundus images. Images from the EyePACS dataset were used, and quality labels from previous works in the literature were selected. Cubical complexes were used to represent the images; the grayscale version was, then, used to calculate a persistent homology on the simplex and represented with persistence diagrams. Then, 30 vectorized topological descriptors were calculated from each image and used as input to a classification algorithm. Six different algorithms were tested for this study (SVM, decision tree, k-NN, random forest, logistic regression (LoGit), MLP). LoGit was selected and used for the classification of all images, given the low computational cost it carries. Performance results on the validation subset showed a global accuracy of 0.932, precision of 0.912 for label “quality” and 0.952 for label “no quality”, recall of 0.932 for label “quality” and 0.912 for label “no quality”, AUC of 0.980, F1 score of 0.932, and a Matthews correlation coefficient of 0.864. This work offers evidence for the use of topological methods for the process of quality assessment of eye fundus images, where a relatively small vector of characteristics (30 in this case) can enclose enough information for an algorithm to yield classification results useful in the clinical settings of a digital fundoscopy pipeline for CAD.

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Yuhendra and Minarni. "Optical SAR Images Fusion: Comparative Analysis of Resulting Images Data." MATEC Web of Conferences 215 (2018): 01002. http://dx.doi.org/10.1051/matecconf/201821501002.

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Image fusion is a useful tool for integrating low spatial resolution multispectral (MS) images with a high spatial resolution panchromatic (PAN) image, thus producing a high resolution multispectral image for better understanding of the observed earth surface. A main proposed the research were the effectiveness of different image fusion methods while filtering methods added to speckle suppression in synthetic aperture radar (SAR) images. The quality assessment of the filtering fused image implemented by statistical parameter namely mean, standard deviation, bias, universal index quality image (UIQI) and root mean squared error (RMSE). In order to test the robustness of the image quality, either speckle noise (Gamma map filter) is intentionally added to the fused image. When comparing and testing result, Gram Scmidth (GS) methods have shown better results for good colour reproduction, as compared with high pass filtering (HPF). And the other hands, GS, and wavelet intensity hue saturation (W-IHS) have shown the preserving good colour with original image for Landsat TM data.

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Kaplan, Lance M., Stephen D. Burks, Rick S. Blum, Richard K. Moore, and Quang Nguyen. "Analysis of Image Quality for Image Fusion via Monotonic Correlation." IEEE Journal of Selected Topics in Signal Processing 3, no. 2 (April 2009): 222–35. http://dx.doi.org/10.1109/jstsp.2009.2014500.

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Groch, K. "Image Segmentation and Quality Control Measures in Microarray Image Analysis." Journal of the Association for Laboratory Automation 6, no. 3 (July 1, 2001): 73–76. http://dx.doi.org/10.1016/s1535-5535(04)00140-6.

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Groch, Kevin, Alexander Kuklin, Anton Petrov, and Soheil Shams. "Image Segmentation and Quality Control Measures in Microarray Image Analysis." JALA: Journal of the Association for Laboratory Automation 6, no. 3 (June 2001): 73–76. http://dx.doi.org/10.1016/s1535-5535-04-00140-6.

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Ahmed, Tawsin Uddin, Seyed Ali Amirshahi, and Marius Pedersen. "Image demosaicing: Subjective analysis and evaluation of image quality metrics." Electronic Imaging 35, no. 8 (January 16, 2023): 301–1. http://dx.doi.org/10.2352/ei.2023.35.8.iqsp-301.

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A, Pasumponpandian. "IMAGE INPAINTING TECHNIQUE FOR HIGH QUALITY AND RESOLUTION ENHANCED IMAGE CREATION." Journal of Innovative Image Processing 1, no. 01 (October 25, 2019): 39–50. http://dx.doi.org/10.36548/jiip.2019.1.005.

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The image in-painting is the method of improving or enhancing the damaged and the missing parts of the images. This process would be very essential preprocessing procedure in case of the medical image analysis for the diagnosis of the disease. The traditional ways of in-painting being ineffective the paper proposes hybrid image in-painting technique combining the edge connect, patch match and the deep image prior for the images to improve the quality and the resolution of the images, the proposed method is tested with different number of images from the gathered form the website to prove the competence of the proposed image in-painting technique.

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Dwivedi, Ashish, and Nirupma Tiwari. "Analysis of color Image Enhancement Using DWT, Wavelet Shrinkageand FHE Methods." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 8 (August 30, 2017): 56. http://dx.doi.org/10.23956/ijarcsse.v7i8.21.

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Image enhancement (IE) is very important in the field where visual appearance of an image is the main. Image enhancement is the process of improving the image in such a way that the resulting or output image is more suitable than the original image for specific task. With the help of image enhancement process the quality of image can be improved to get good quality images so that they can be clear for human perception or for the further analysis done by machines.Image enhancement method enhances the quality, visual appearance, improves clarity of images, removes blurring and noise, increases contrast and reveals details. The aim of this paper is to study and determine limitations of the existing IE techniques. This paper will provide an overview of different IE techniques commonly used. We Applied DWT on original RGB image then we applied FHE (Fuzzy Histogram Equalization) after DWT we have done the wavelet shrinkage on Three bands (LH, HL, HH). After that we fuse the shrinkage image and FHE image together and we get the enhance image.

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Phaneendra, K., B. Tirupathi Rao, B. Nikhila Sree, J. Haswanth Kumar, and B. Harshavardhan Raju. "Novel Approach for MRI Image Quality Analysis by Hybrid Preprocessing Techniques." International Journal for Modern Trends in Science and Technology 6, no. 5 (May 26, 2020): 27–32. http://dx.doi.org/10.46501/ijmtst060505.

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Image pre-preparing methods are utilized to improve the nature of a image before handling into an application. This uses a little neighborhood of a pixel in an information image to get another splendor esteem in the yield image. These pre-preparing methods are likewise called as filtration and goals upgrade. The clinical image quality parameters are fundamentally clamor and goals. The fundamental goal of this paper is to improve the image quality by denoising and goals upgrade. A large portion of the imaging methods are corrupted by clamor. So as to protect the edges and form data of the clinical images, the effective denoising and an improved upgrade method is required. This paper focuses the normal, middle and wiener sifting for image denoising and an addition based Discrete Wavelet Transform (DWT) strategy for goals improvement. The presentation of these strategies is assessed utilizing Peak Signal to Noise Ratio (PSNR). From the outcomes, it uncovers that the effective denoising and goals upgrade method is basic for image pre-handling.

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Wang, Zhi-guo, Wei Wang, and Baolin Su. "Multi-sensor Image Fusion Algorithm Based on Multiresolution Analysis." International Journal of Online Engineering (iJOE) 14, no. 06 (June 22, 2018): 44. http://dx.doi.org/10.3991/ijoe.v14i06.8697.

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To solve the fusion problem of visible and infrared images, based on image fusion algorithm such as region fusion, wavelet transform, spatial frequency, Laplasse Pyramid and principal component analysis, the quality evaluation index of image fusion was defined. Then, curve-let transform was used to replace the wavelet change to express the superiority of the curve. It integrated the intensity channel and the infrared image, and then transformed it to the original space to get the fused color image. Finally, two groups of images at different time intervals were used to carry out experiments, and the images obtained after fusion were compared with the images obtained by the first five algorithms, and the quality was evaluated. The experiment showed that the image fusion algorithm based on curve-let transform had good performance, and it can well integrate the information of visible and infrared images. It is concluded that the image fusion algorithm based on curve-let change is a feasible multi-sensor image fusion algorithm based on multi-resolution analysis.

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Saleh, Mohammed Ali, AbdElmgeid A. Ali, Kareem Ahmed, and Abeer M. Sarhan. "A Brief Analysis of Multimodal Medical Image Fusion Techniques." Electronics 12, no. 1 (December 26, 2022): 97. http://dx.doi.org/10.3390/electronics12010097.

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Recently, image fusion has become one of the most promising fields in image processing since it plays an essential role in different applications, such as medical diagnosis and clarification of medical images. Multimodal Medical Image Fusion (MMIF) enhances the quality of medical images by combining two or more medical images from different modalities to obtain an improved fused image that is clearer than the original ones. Choosing the best MMIF technique which produces the best quality is one of the important problems in the assessment of image fusion techniques. In this paper, a complete survey on MMIF techniques is presented, along with medical imaging modalities, medical image fusion steps and levels, and the assessment methodology of MMIF. There are several image modalities, such as Computed Tomography (CT), Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), and Single Photon Emission Computed Tomography (SPECT). Medical image fusion techniques are categorized into six main categories: spatial domain, transform fusion, fuzzy logic, morphological methods, and sparse representation methods. The MMIF levels are pixel-level, feature-level, and decision-level. The fusion quality evaluation metrics can be categorized as subjective/qualitative and objective/quantitative assessment methods. Furthermore, a detailed comparison between obtained results for significant MMIF techniques is also presented to highlight the pros and cons of each fusion technique.

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Chandrakanth, T., and B. Sandhya. "Analysis of SSIM based Quality Assessment across Color Channels of Images." International Journal of System Dynamics Applications 4, no. 3 (July 2015): 30–42. http://dx.doi.org/10.4018/ijsda.2015070102.

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Advances in imaging and computing hardware have led to an explosion in the use of color images in image processing, graphics and computer vision applications across various domains such as medical imaging, satellite imagery, document analysis and biometrics to name a few. However, these images are subjected to a wide variety of distortions during its acquisition, subsequent compression, transmission, processing and then reproduction, which degrade their visual quality. Hence objective quality assessment of color images has emerged as one of the essential operations in image processing. During the last two decades, efforts have been put to design such an image quality metric which can be calculated simply but can accurately reflect subjective quality of human perception. In this paper, the authors evaluated the quality assessment of color images using SSIM (structural similarity index) metric across various color spaces. They experimented to study the effect of color spaces in metric based and distance based quality assessment. The authors proposed a metric using CIE Lab color space and SSIM, which has better correlation to the subjective assessment in a benchmark dataset.

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Pedroza, Matheus, Glêndara A., and Warley Gramacho. "Analysis of Guava Quality by Image Processing." International Journal of Computer Applications 156, no. 3 (December 15, 2016): 30–36. http://dx.doi.org/10.5120/ijca2016912404.

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Wueller, Dietmar, and Ulla Bøgvad Kejser. "Standardization of Image Quality Analysis – ISO 19264." Archiving Conference 2016, no. 1 (April 19, 2016): 111–16. http://dx.doi.org/10.2352/issn.2168-3204.2016.1.0.111.

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Nielsen, H. M., and W. Paul. "QUALITY MEASUREMENTS OF TOMATOES BY IMAGE ANALYSIS." Acta Horticulturae, no. 421 (March 1998): 75–84. http://dx.doi.org/10.17660/actahortic.1998.421.6.

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Sakaguchi, Akio, Hyungsup Kim, Yo-Ichi Matsumoto, and Koichiro Toriumi. "Woven Fabric Quality Evaluation Using Image Analysis." Textile Research Journal 70, no. 11 (November 2000): 950–56. http://dx.doi.org/10.1177/004051750007001103.

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Phan, Thien D., Siddharth K. Shah, Damon M. Chandler, and Sohum Sohoni. "Microarchitectural analysis of image quality assessment algorithms." Journal of Electronic Imaging 23, no. 1 (February 26, 2014): 013030. http://dx.doi.org/10.1117/1.jei.23.1.013030.

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Yang, Xichen, Quansen Sun, and Tianshu Wang. "Image quality assessment via spatial structural analysis." Computers & Electrical Engineering 70 (August 2018): 349–65. http://dx.doi.org/10.1016/j.compeleceng.2016.08.014.

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Loftus, John, David Laurí, and Barry Lennox. "Product Quality Estimation Using Multivariate Image Analysis." IFAC Proceedings Volumes 47, no. 3 (2014): 10610–15. http://dx.doi.org/10.3182/20140824-6-za-1003.00614.

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Nivall, S., D. Holmquist, T. Gustavsson, and J. Wahlström. "Image quality in digital chromosome analysis systems." Clinical Genetics 48, no. 5 (June 28, 2008): 238–42. http://dx.doi.org/10.1111/j.1399-0004.1995.tb04096.x.

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Moraru, Luminița, Simona Moldovanu, and Cristian Dragos Obreja. "A Survey Over Image Quality Analysis Techniques for Brain MR Images." International Journal of Radiology 2, no. 1 (2015): 29–37. http://dx.doi.org/10.17554/j.issn.2313-3406.2015.02.5.

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Marzouk, Mohamed, and Mahmoud Hassouna. "Quality analysis using three-dimensional modelling and image processing techniques." Construction Innovation 19, no. 4 (October 7, 2019): 614–28. http://dx.doi.org/10.1108/ci-10-2018-0086.

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Purpose This paper aims to propose a system for defect detection in constructed elements that is able to indicate deformity positions. It also evaluates the defects in finishing materials of constructed building elements to support the subjective visual quality investigation of the aesthetics of an architectural work. Design/methodology/approach This strategy depends on defect features analysis that evaluates the defect value in digital images using digital image processing methods. The research uses the three-dimensional (3D) modeling techniques and image processing algorithms to generate a system that is able to perform some of the monitoring activities by computers. Based on the collected site scans, a 3D model is created for the building. Then, several images can be exported from the 3D model to investigate a specific element. Different image denoizing techniques are compared such as mean filter, median filter, Wiener filter and Split–Bregman iterations. The most efficient technique is implemented in the system. Then, the following six different methods are used for image segmentation to separate the concerned object from the background; color segmentation, region growing segmentation, histogram segmentation, local standard deviation segmentation, adaptive threshold segmentation and mean-shift cluster segmentation. Findings The proposed system is able to detect the cracks and defected areas in finishing works and calculate the percentage of the defected area compared to the total captured area in the photo with high accuracy. Originality/value The proposed system increases the precision of decision-making by decreasing the contribution of human subjective judgment. Investigation of different finishing surfaces is applied to validate the proposed system.

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Singh, A. K., H. V. Kumar, G. R. Kadambi, J. K. Kishore, J. Shuttleworth, and J. Manikandan. "Quality Metrics Evaluation of Hyperspectral Images." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 1221–26. http://dx.doi.org/10.5194/isprsarchives-xl-8-1221-2014.

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In this paper, the quality metrics evaluation on hyperspectral images has been presented using k-means clustering and segmentation. After classification the assessment of similarity between original image and classified image is achieved by measurements of image quality parameters. Experiments were carried out on four different types of hyperspectral images. Aerial and spaceborne hyperspectral images with different spectral and geometric resolutions were considered for quality metrics evaluation. Principal Component Analysis (PCA) has been applied to reduce the dimensionality of hyperspectral data. PCA was ultimately used for reducing the number of effective variables resulting in reduced complexity in processing. In case of ordinary images a human viewer plays an important role in quality evaluation. Hyperspectral data are generally processed by automatic algorithms and hence cannot be viewed directly by human viewers. Therefore evaluating quality of classified image becomes even more significant. An elaborate comparison is made between k-means clustering and segmentation for all the images by taking Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), Maximum Squared Error, ratio of squared norms called L2RAT and Entropy. First four parameters are calculated by comparing the quality of original hyperspectral image and classified image. Entropy is a measure of uncertainty or randomness which is calculated for classified image. Proposed methodology can be used for assessing the performance of any hyperspectral image classification techniques.

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Almeida, Solange Maria de, Ana Emília Figueiredo de Oliveira, Rívea Inês Ferreira, and Frab Norberto Bóscolo. "Image quality in digital radiographic systems." Brazilian Dental Journal 14, no. 2 (2003): 136–71. http://dx.doi.org/10.1590/s0103-64402003000200012.

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The aim of the present study was to evaluate the image quality of four direct digital radiographic systems. Radiographs were made of the maxillary central incisor and mandibular left molar regions of a dry skull, and an aluminum step-wedge. The X-ray generator operated at 10 mA, 60 and 70 kVp, and images were acquired with 3, 5, 8, 12, 24 and 48 exposure pulses. Six well-trained observers classified the images by means of scores from 1 to 3. Collected data were submitted to nonparametric statistical analysis using Fisher's exact test. Statistical analysis showed significant differences (p<0.01) in image quality with the four systems. Based on the results, it was possible to conclude that: 1) all of the digital systems presented good performance in producing acceptable images for diagnosis, if the exposures of the step-wedge and the maxillary central incisor region were made at 5 pulses, as well as at 8 pulses for the mandibular left molar region, selecting 60 or 70kVp; 2) higher percentages of acceptable images were obtained with the administration of lower radiation doses in CCD-sensors (charge-coupled device); 3) the Storage Phosphor systems produced acceptable images at a large range of exposure settings, that included low, intermediate and high radiation doses.

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Reiner, Bruce I. "Creating Accountability in Image Quality Analysis. Part 4: Quality Analytics." Journal of Digital Imaging 26, no. 5 (August 15, 2013): 825–29. http://dx.doi.org/10.1007/s10278-013-9628-1.

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Vyborny, C., P. Bunch, H. Chotas, J. Dobbins, L. Niklason, and C. Schaefer-Prokop. "Image Quality in Chest Radiography: Abstract." Journal of the ICRU 3, no. 2 (July 2003): 13. http://dx.doi.org/10.1093/jicru_3.2.13.

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Image quality in chest radiography is an important, but complex, subject. The complicated anatomy of the chest, as well as the various ways that chest disease may manifest itself, require careful consideration of radiographic technique. The manner in which human observers deal with the complexity of chest images adds further dimensions to image analysis that are not found in other radiography examinations. This report describes many issues that are related to the quality of chest radiographic images. In so doing, it relies upon the very extensive literature on this topic, a topic that has been one of the most thoroughly studied in all of radiography. Strategies that are generally agreed to improve the quality of chest radiographs are described, as are approaches to the assessment of image quality.

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Golub, Yu I., F. V. Starovoitov, and V. V. Starovoitov. "Impact of image size reducing for image quality assesment." «System analysis and applied information science», no. 2 (August 18, 2020): 35–45. http://dx.doi.org/10.21122/2309-4923-2020-2-35-45.

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The article describes studies of the effect of image reduction on the quantitative assessment of their quality. Image reduction refers to the proportional reduction of horizontal and vertical image resolutions in pixels. Within the framework of these studies, correlation analysis between quantitative assessments of image quality and subjective assessments of experts was performed. For the experiments, we used images from the public TID2013 database with a resolution of 512 × 384 pixels and expert estimates of their quality, as well as photographs taken with a Nikon D5000 digital camera with a resolution of 4288 × 2848 pixels. All images were reduced in 2, 4 and 8 times. For this two methods were used: bilinear interpolation and interpolation by the nearest neighbor.22 measures were selected to evaluate image quality. Quantitative assessment of image quality was calculated in two stages. At the first stage, an array of local estimates was obtained in the vicinity of each pixel using the selected measures. At the second stage, a global quality assessment was calculated from the obtained local ones. To summarize local quality estimates, the parameters of 16 distributions of random variables were considered.According to the results of the experiments, it was concluded that the accuracy of the quality assessment for some measures decreases with image reduction (for example, FISH, GORD, HELM, LOEN measures). BREN and SHAR measures are recommended as the best. To reduce images, it is better to use the nearest neighbor interpolation method. At the same time, the computation time of estimates is reduced on average by 4 times while reducing images by 2 times. When images are reduced by 8 times, the calculation time decreases on average by 80 times. The amount of memory required to store the reduced images is 25 times less.

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Sundell, Veli-Matti, Teemu Mäkelä, Alexander Meaney, Touko Kaasalainen, and Sauli Savolainen. "Automated daily quality control analysis for mammography in a multi-unit imaging center." Acta Radiologica 60, no. 2 (May 16, 2018): 140–48. http://dx.doi.org/10.1177/0284185118776502.

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Background The high requirements for mammography image quality necessitate a systematic quality assurance process. Digital imaging allows automation of the image quality analysis, which can potentially improve repeatability and objectivity compared to a visual evaluation made by the users. Purpose To develop an automatic image quality analysis software for daily mammography quality control in a multi-unit imaging center. Material and Methods An automated image quality analysis software using the discrete wavelet transform and multiresolution analysis was developed for the American College of Radiology accreditation phantom. The software was validated by analyzing 60 randomly selected phantom images from six mammography systems and 20 phantom images with different dose levels from one mammography system. The results were compared to a visual analysis made by four reviewers. Additionally, long-term image quality trends of a full-field digital mammography system and a computed radiography mammography system were investigated. Results The automated software produced feature detection levels comparable to visual analysis. The agreement was good in the case of fibers, while the software detected somewhat more microcalcifications and characteristic masses. Long-term follow-up via a quality assurance web portal demonstrated the feasibility of using the software for monitoring the performance of mammography systems in a multi-unit imaging center. Conclusion Automated image quality analysis enables monitoring the performance of digital mammography systems in an efficient, centralized manner.

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Rubel, Andrii, Oleg Ieremeiev, Vladimir Lukin, Jarosław Fastowicz, and Krzysztof Okarma. "Combined No-Reference Image Quality Metrics for Visual Quality Assessment Optimized for Remote Sensing Images." Applied Sciences 12, no. 4 (February 14, 2022): 1986. http://dx.doi.org/10.3390/app12041986.

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No-reference image quality assessment is one of the most demanding areas of image analysis for many applications where the results of the analysis should be strongly correlated with the quality of an input image and the corresponding reference image is unavailable. One of the examples might be remote sensing since the transmission of such obtained images often requires the use of lossy compression and they are often distorted, e.g., by the presence of noise and blur. Since the practical usefulness of acquired and/or preprocessed images is directly related to their quality, there is a need for the development of reliable and adequate no-reference metrics that do not need any reference images. As the performance and universality of many existing metrics are quite limited, one of the possible solutions is the design and application of combined metrics. Several possible approaches to their composition have been previously proposed and successfully used for full-reference metrics. In the paper, three possible approaches to the development and optimization of no-reference combined metrics are investigated and verified for the dataset of images containing distortions typical for remote sensing. The proposed approach leads to good results, significantly improving the correlation of the obtained results with subjective quality scores.

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Deserno, T. M., H. P. Meinzer, T. Tolxdorff, and H. Handels. "Image Analysis and Modeling in Medical Image Computing." Methods of Information in Medicine 51, no. 05 (2012): 395–97. http://dx.doi.org/10.1055/s-0038-1627047.

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Summary Background: Medical image computing is of growing importance in medical diagnostics and image-guided therapy. Nowadays, image analysis systems integrating advanced image computing methods are used in practice e.g. to extract quantitative image parameters or to support the surgeon during a navigated intervention. However, the grade of automation, accuracy, reproducibility and robustness of medical image computing methods has to be increased to meet the requirements in clinical routine. Objectives: In the focus theme, recent developments and advances in the field of modeling and model-based image analysis are described. The introduction of models in the image analysis process enables improvements of image analysis algorithms in terms of automation, accuracy, reproducibility and robustness. Furthermore, model-based image computing techniques open up new perspectives for prediction of organ changes and risk analysis of patients. Methods: Selected contributions are assembled to present latest advances in the field. The authors were invited to present their recent work and results based on their outstanding contributions to the Conference on Medical Image Computing BVM 2011 held at the University of Lübeck, Germany. All manuscripts had to pass a comprehensive peer review. Results: Modeling approaches and model-based image analysis methods showing new trends and perspectives in model-based medical image computing are described. Complex models are used in different medical applications and medical images like radiographic images, dual-energy CT images, MR images, diffusion tensor images as well as microscopic images are analyzed. The applications emphasize the high potential and the wide application range of these methods. Conclusions: The use of model-based image analysis methods can improve segmentation quality as well as the accuracy and reproducibility of quantitative image analysis. Furthermore, image-based models enable new insights and can lead to a deeper understanding of complex dynamic mechanisms in the human body. Hence, model-based image computing methods are important tools to improve medical diagnostics and patient treatment in future.

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Wintergerst, Maximilian W. M., Linus G. Jansen, Frank G. Holz, and Robert P. Finger. "A Novel Device for Smartphone-Based Fundus Imaging and Documentation in Clinical Practice: Comparative Image Analysis Study." JMIR mHealth and uHealth 8, no. 7 (July 29, 2020): e17480. http://dx.doi.org/10.2196/17480.

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Background Smartphone-based fundus imaging allows for mobile and inexpensive fundus examination with the potential to revolutionize eye care, particularly in lower-resource settings. However, most smartphone-based fundus imaging adapters convey image quality not comparable to conventional fundus imaging. Objective The purpose of this study was to evaluate a novel smartphone-based fundus imaging device for documentation of a variety of retinal/vitreous pathologies in a patient sample with wide refraction and age ranges. Methods Participants’ eyes were dilated and imaged with the iC2 funduscope (HEINE Optotechnik) using an Apple iPhone 6 in single-image acquisition (image resolution of 2448 × 3264 pixels) or video mode (1248 × 1664 pixels) and a subgroup of participants was also examined by conventional fundus imaging (Zeiss VISUCAM 500). Smartphone-based image quality was compared to conventional fundus imaging in terms of sharpness (focus), reflex artifacts, contrast, and illumination on semiquantitative scales. Results A total of 47 eyes from 32 participants (age: mean 62.3, SD 19.8 years; range 7-93; spherical equivalent: mean –0.78, SD 3.21 D; range: –7.88 to +7.0 D) were included in the study. Mean (SD) visual acuity (logMAR) was 0.48 (0.66; range 0-2.3); 30% (14/47) of the eyes were pseudophakic. Image quality was sufficient in all eyes irrespective of refraction. Images acquired with conventional fundus imaging were sharper and had less reflex artifacts, and there was no significant difference in contrast and illumination (P<.001, P=.03, and P=.10, respectively). When comparing image quality at the posterior pole, the mid periphery, and the far periphery, glare increased as images were acquired from a more peripheral part of the retina. Reflex artifacts were more frequent in pseudophakic eyes. Image acquisition was also possible in children. Documentation of deep optic nerve cups in video mode conveyed a mock 3D impression. Conclusions Image quality of conventional fundus imaging was superior to that of smartphone-based fundus imaging, although this novel smartphone-based fundus imaging device achieved image quality high enough to document various fundus pathologies including only subtle findings. High-quality smartphone-based fundus imaging might represent a mobile alternative for fundus documentation in clinical practice.

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Czajkowska, Joanna, Jan Juszczyk, Laura Piejko, and Małgorzata Glenc-Ambroży. "High-Frequency Ultrasound Dataset for Deep Learning-Based Image Quality Assessment." Sensors 22, no. 4 (February 14, 2022): 1478. http://dx.doi.org/10.3390/s22041478.

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This study aims at high-frequency ultrasound image quality assessment for computer-aided diagnosis of skin. In recent decades, high-frequency ultrasound imaging opened up new opportunities in dermatology, utilizing the most recent deep learning-based algorithms for automated image analysis. An individual dermatological examination contains either a single image, a couple of pictures, or an image series acquired during the probe movement. The estimated skin parameters might depend on the probe position, orientation, or acquisition setup. Consequently, the more images analyzed, the more precise the obtained measurements. Therefore, for the automated measurements, the best choice is to acquire the image series and then analyze its parameters statistically. However, besides the correctly received images, the resulting series contains plenty of non-informative data: Images with different artifacts, noise, or the images acquired for the time stamp when the ultrasound probe has no contact with the patient skin. All of them influence further analysis, leading to misclassification or incorrect image segmentation. Therefore, an automated image selection step is crucial. To meet this need, we collected and shared 17,425 high-frequency images of the facial skin from 516 measurements of 44 patients. Two experts annotated each image as correct or not. The proposed framework utilizes a deep convolutional neural network followed by a fuzzy reasoning system to assess the acquired data’s quality automatically. Different approaches to binary and multi-class image analysis, based on the VGG-16 model, were developed and compared. The best classification results reach 91.7% accuracy for the first, and 82.3% for the second analysis, respectively.

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Chandrakala, M. "Image Analysis of Sauvola and Niblack Thresholding Techniques." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 14, 2021): 2353–57. http://dx.doi.org/10.22214/ijraset.2021.34569.

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Image segmentation is a critical problem in computer vision and other image processing applications. Image segmentation has become quite challenging over the years due to its widespread use in a variety of applications. Image thresholding is a popular image segmentation technique. The segmented image quality is determined by the techniques used to determine the threshold value.A locally adaptive thresholding method based on neighborhood processing is presented in this paper. The performance of locally thresholding methods like Niblack and Sauvola was demonstrated using real-world images, printed text, and handwritten text images. Threshold-based segmentation methods were investigated using misclassification error, MSE and PSNR. Experiments have shown that the Sauvola method outperforms real-world images, printed and handwritten text images in terms of misclassification error, PSNR, and MSE.

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Khalaf, Walaa, Abeer Al Gburi, and Dhafer Zaghar. "Pre and Postprocessing for JPEG to Handle Large Monochrome Images." Algorithms 12, no. 12 (December 1, 2019): 255. http://dx.doi.org/10.3390/a12120255.

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Image compression is one of the most important fields of image processing. Because of the rapid development of image acquisition which will increase the image size, and in turn requires bigger storage space. JPEG has been considered as the most famous and applicable algorithm for image compression; however, it has shortfalls for some image types. Hence, new techniques are required to improve the quality of reconstructed images as well as to increase the compression ratio. The work in this paper introduces a scheme to enhance the JPEG algorithm. The proposed scheme is a new method which shrinks and stretches images using a smooth filter. In order to remove the blurring artifact which would be developed from shrinking and stretching the image, a hyperbolic function (tanh) is used to enhance the quality of the reconstructed image. Furthermore, the new approach achieves higher compression ratio for the same image quality, and/or better image quality for the same compression ratio than ordinary JPEG with respect to large size and more complex content images. However, it is an application for optimization to enhance the quality (PSNR and SSIM), of the reconstructed image and to reduce the size of the compressed image, especially for large size images.

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Journal articles on the topic 'Image quality analysis'

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