Image Quality and Performance Analysis Using Frequency Domain Techniques
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Abstract
Image enhancement is both an art and a science, playing a pivotal role in enhancing the quality of high-resolution images like those captured by digital cameras. Its primary goal is to unveil hidden details within an image and augment the contrast in images with low contrast. This method offers a plethora of options for elevating the visual appeal of images, making it an indispensable tool in numerous applications that face challenges such as noise reduction, degradation, and blurring. In this paper, we implemented frequency domain high pass filters like ideal high pass filter, Butterworth high pass filter and Gaussian high pass filters with execution time using MATLAB. 60 cancer images also tested. The Gaussian high pass filter given better results than other two with less execution time.
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L. Tabar and P. Dean, Teaching atlas of mammography. New York: Thime,3rd ed., 2001. https://www.osumcradiology.org/resources/Supplimental-Books/Atlas-of-Mammography-(3rd-Edition).pdf
Valarmathi, Ms P., and V. Radhakrishnan. "Tumor prediction in mammogram using neural network." Global Journal of Computer Science and Technology (2013). https://computerresearch.org/index.php/computer/article/view/361
R. Schmidt, D.Wolverton, and C. Vyborny, “Computer-aided diagnosis (CAD) in mammography," in Syllabus: A Categorical Course in Breast Imaging, pp. 199-208, 1995. https://www.researchgate.net/publication/242220115_Computer-aided_Diagnosis_in_Diagnostic_Mammography_and_Multimodality
M.L.Giger, “Current issues in mammography," Proceedings of the 3rd International Workshop on Digital Mammography, pp. 53-59, Chicago, IL, June 1996. https://doi.org/10.35940/ijitee.B9777.13020124
C. Vyborny and M. Giger, “Computer vision and artificial intelligence in mammography," American Journal of Roentgenology, vol. 162, no. 3, pp. 699-708, 2019. https://ajronline.org/doi/full/10.2214/ajr.162.3.8109525
R. Reid, “Professional quality assurance for mammography screening programs," Radiology, vol. 177, pp. 8-10, 1990. http://journal-iiie-india.com/1_jan_24/18.2_jan.pdf
C. Metz and J. Shen, “Gains in accuracy from replicated readings of diagnostic images: Predication and assessment in terms of ROC analysis," Medical Decision Making, vol. 12, pp. 60-75, 2017. https://doi.org/10.1177/0272989X9201200110
R. Schmidt, R. Nishikawa, and K. Schreibman, “Computer detection of lesions missed by mammography," Proceedings of the 2nd International Workshop on Digital Mammography, pp. 289-294, July 10-12 2018. https://www.semanticscholar.org/paper/Digital-mammography-%3A-proceedings-of-the-2nd-on-Gale/7e0bc307c959f45b0e2abd0d2a94a4428eca4ca4
R. Schmidt, R. Nishikawa, R. Osnis, K. Schreibman, M. Giger, and K. Doi, “Computerized detection of lesions missed by mammography," Proceedings of the 3rd International Workshop on Digital Mammography, pp. 105-110, June 9-12 2019.
“R2 technology pre-market approval (PMA) of the M1000 image checker," US. Food and Drug Administration (FDA) application #P970058, approved, June 26, 1998. https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpma/pma.cfm?id=P970058
R. Highnam and M. Brady, Mammographic Image Analysis. Dordrecht: Kluwer Academic Publishers, 2017. DOI: https://doi.org/10.1007/978-94-011-4613-5
L. Bassett, V. Jackson, R. Jahan, Y. Fu, and R. Gold, Diagnosis of diseases of the breast. W.B. Saunders Company, Philadelphia, PA, 1997. https://www.amazon.in/Diagnosis-Diseases-Breast-Lawrence-Bassett/dp/0721637965
C. Metz, “ROC methodology in radiologic imaging," Investigative Radiology, vol. 21, pp. 720-733, 2018. DOI: https://doi.org/10.1097/00004424-198609000-00009
C. Metz, “Evaluation of digital mammography by ROC analysis," Proceedings of the 3rd International Workshop on Digital Mammography, pp. 61-68, June 9-12 1996. DOI: https://doi.org/10.1016/j.ejrad.2009.10.002
C. Metz, “Receiver operating characteristic (ROC) analysis in medical imaging," ICRU News, pp. 7-16, 2017. DOI: https://doi.org/10.1016/j.jacr.2006.02.021
D. Chakraborty, “Maximum likelihood analysis of free-response receiver operating characteristic (FROC) data," Medical Physics, vol. 16, p. 561, 2017. DOI: https://doi.org/10.1118/1.596358
D. Chakraborty and L. Winter, “Free-response methodology: alternate analysis and a new observer-performance experiment," Radiology, vol. 174, p. 873, 1990. DOI: https://doi.org/10.1148/radiology.174.3.2305073
R. Swensson, “Unified measurement of observer performance in detecting and localizing target objects on images," Medical Physics, vol. 23, p. 1709, 2018. DOI: https://doi.org/10.1118/1.597758
R. Wagner, S. Beiden, and C. Metz, “Continuous vs. categorical data for ROC analysis: Some quantitative considerations," Academic Radiology, vol. 8, pp. 328-334, 2001. DOI: https://doi.org/10.1016/s1076-6332(03)80502-0
N. Karssemeijer and W. Veldkamp, “Normalisation of local contrast in mammograms," IEEE Transactions on Medical Imaging, vol. 19, no. 7, pp. 731-738, 2017. DOI: https://doi.org/10.1109/42.875197
K. McLoughlin, P. Bones, and P. Dachman, “Connective tissue representation for detection of microcalcification in digital mammograms," Proceedings of SPIE Medical Image 2002: Image Proceesing, vol. 4684, pp. 1246-1256, 2017. DOI: http://doi.org/10.1117/12.467084
Deepika, V., Nivedha, C., P. S, S. Roshini., & Kumar, G. S. A. (2020). Variance Reduction in Low Light Image Enhancement Model. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 9, Issue 4, pp. 139–142). DOI: https://doi.org/10.35940/ijrte.d4723.119420
Akila, Mrs. P. G., Batri, K., Sasi, G., & Ambika, R. (2019). Denoising of MRI Brain Images using Adaptive Clahe Filtering Method. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 1s, pp. 91–95). DOI: https://doi.org/10.35940/ijeat.a1018.1091s19
Saha, T., & Vishal, Dr. K. (2024). A Study of Application of Digital Image Processing in Medical Field and Medical Image Segmentation by Edge Detection. In International Journal of Emerging Science and Engineering (Vol. 12, Issue 4, pp. 3–8). DOI: https://doi.org/10.35940/ijese.g9890.12040324
Raveena, C., Kalaivani, R. S., Yagna, B., & Rakshitha, T. R. (2020). Deep Submerged Image Enhancement and Restoration Process using CNN. In International Journal of Innovative Technology and Exploring Engineering (Vol. 9, Issue 10, pp. 112–117). DOI: https://doi.org/10.35940/ijitee.j7407.0891020
Nasir, F. M., & Watabe, H. (2020). Validation of the Image Registration Technique from Functional Near Infrared Spectroscopy (fNIRS) Signal and Positron Emission Tomography (PET) Image. In International Journal of Management and Humanities (Vol. 4, Issue 9, pp.
–69). DOI: https://doi.org/10.35940/ijmh.i0877.054920