An approach to edge detection in medical imaging through histogram analysis and morphological gradient
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Edge detection takes importance in image processing systems for computer-aided diagnosis, where
sharp changes in pixel intensity are analyzed to obtain fast and accurate information about regions of
interest to the specialist. A method for feature enhancement and edge detection in medical images
was developed using image processing by analyzing the pixel distribution histogram and
morphological gradient operation. Images from the MINI MIAS dataset and the COVID-CT dataset
were used. The method is based on image processing and is applied to mammography and chest CT
images, where blur filtering steps are accompanied by morphological gradient filtering, in addition to
obtaining the threshold for edge detection by analyzing the point of maximum pixel concentration
according to the distribution histogram. The processing is presented in a graphical user interface
developed in Python language. The method is validated by comparison with other edge detection
techniques such as the Canny Algorithm, and with deep learning methods such as Holistically-Nested
Edge Detection. The proposed method improves image quality in both mammograms and CT scans
compared to other techniques. It also presents the best performance considering internal and external
edge detection, as well as an average response time of 1.054 seconds and 2.63 % of Central Processing
Unit requirement. The developed system is presented as a support tool for use in computer-aided
diagnosis processes due to its high efficiency in edge detection.
(1) Larrazabal AJ, Nieto N, Peterson V, Milone DH, Ferrante E. Gender imbalance in medical imaging datasets produces biased classifiers for computeraided diagnosis. Proc Natl Acad Sci U S A. 2020;117(23):12592–4. https://doi.org/10.1073/pnas.191901211 7
(2) Kido S, Hirano Y, Hashimoto N. Detection and classification of lung abnormalities by use of convolutional neural network (CNN) and regions with CNN features (R-CNN). In: 2018 International Workshop on Advanced Image Technology, IWAIT 2018. 2018. p. 1–4. https://doi.org/10.1109/IWAIT.2018.836 9798
(3) Fujita H. AI-based computer-aided diagnosis (AI-CAD): the latest review to read first. Radiol Phys Technol [Internet]. 2020;13(1):6–19. https://doi.org/10.1007/s12194-019- 00552-4
(4) Mohammed ZF, Abdulla AA. Thresholding-based White Blood Cells Segmentation from Microscopic Blood Images. UHD J Sci Technol. 2020;4(1):9–17. https://doi.org/10.21928/uhdjst.v4n1y20 20.pp9-17
(5) Mohite V, Deoghare AB, Pandey KM. Modeling of Human Airways CAD model Using CT Scan Data. Mater Today Proc [Internet]. 2019;22:1710–4. https://doi.org/10.1016/j.matpr.2020.02. 189
(6) Huang Z, Xiao J, Xie Y, Hu Y, Zhang S, Li X, et al. The correlation of deep learning-based CAD-RADS evaluated by coronary computed tomography angiography with breast arterial calcification on mammography. Sci Rep [Internet]. 2020;10(1):1–8. https://doi.org/10.1038/s41598-020- 68378-4
(7) Xia K, Gu X, Zhang Y. Oriented grouping-constrained spectral clustering for medical imaging segmentation. Multimed Syst [Internet]. 2020;26(1):27–36. https://doi.org/10.1007/s00530-019- 00626-8
(8) Freitas-Junior R, Rodrigues DCN, Corrêa RS, Oliveira LFP, Couto LS, Urban LABD, et al. Opportunistic mammography screening by the Brazilian Unified Health System in 2019. Mastology. 2020;30:1–4. http://dx.doi.org/10.29289/25945394202 020190030
(9) Nizamuddin MK, Kirthana S. Reconstruction of human femur bone from CT scan images using CAD techniques. IOP Conf Ser Mater Sci Eng. 2018;455(1):1–9. https://doi.org/10.1088/1757- 899X/455/1/012103
(10) Gómez-Ríos D, López-Agudelo VA, Urrego-Sepúlveda JC, Ramírez-Malule H. Research on repurposed antivirals currently available in Colombia as treatment alternatives for COVID-19. Ing y Compet [Internet]. 2021;23(1):e10290. https://doi.org/10.25100/iyc.v23i1.10290
(11) Sweetlin JD, Nehemiah HK, Kannan A. Feature selection using ant colony optimization with tandem-run recruitment to diagnose bronchitis from CT scan images. Comput Methods Programs Biomed [Internet]. 2017;145:115–25. http://dx.doi.org/10.1016/j.cmpb.2017.0 4.009
(12) Yadav SP, Yadav S. Image fusion using hybrid methods in multimodality medical images. Med Biol Eng Comput. 2020;58(4):669–87. http://dx.doi.org/10.1007/s11517-020- 02136-6
(13) Luque Sulbaran GC, Walbaum García BV, Camus Appuhn M, Domínguez Covarrubias F, Merino Lara T, Acevedo C F, et al. Cáncer de mama triple negativo: terapias sistémicas actuales y experiencia local. Rev Cir (Mex). 2021;73(2):188–96. https://doi.org/10.35687/s2452- 45492021002942
(14) Natanael G, Zet C, Fosalau C. Estimating the distance to an object based on image processing. In: EPE 2018 - Proceedings of the 2018 10th International Conference and Expositions on Electrical And Power Engineering. 2018. p. 211–6. https://doi.org/10.1109/ICEPE.2018.855 9642
(15) Khatami A, Nazari A, Khosravi A, Lim CP, Nahavandi S. A weight perturbation-based regularisation technique for convolutional neural networks and the application in medical imaging. Expert Syst Appl [Internet]. 2020;149:113196. https://doi.org/10.1016/j.eswa.2020.1131 96
(16) Lin WC, Wang JW. Edge detection in medical images with quasi high-pass filter based on local statistics. Biomed Signal Process Control [Internet]. 2018;39:294–302. http://dx.doi.org/10.1016/j.bspc.2017.08. 011
(17) Dhruv B, Mittal N, Modi M. Early and Precise Detection of Pancreatic Tumor by Hybrid Approach with Edge Detection and Artificial Intelligence Techniques. EAI Endorsed Trans Pervasive Heal Technol. 2018;7(28):e1. http://dx.doi.org/10.4108/eai.31-5- 2021.170009
(18) Devkota B, Alsadoon A, Prasad P, Singh A, Elchouemi A. Image segmentation for early stage brain tumor detection using mathematical morphological reconstruction. Procedia Computer Science [Internet]. 2018;125:115–23. https://doi.org/10.1016/j.procs.2017.12.0 17
(19) Manikandan LC, Selvakumar RK, Nair SAH, Sanal Kumar KP. Hardware implementation of fast bilateral filter and canny edge detector using Raspberry Pi for telemedicine applications. J Ambient Intell Humaniz Comput [Internet]. 2021;12(5):4689–95. https://doi.org/10.1007/s12652-020- 01871-w
(20) Kimura-Sandoval Y, Arévalo-Molina ME, Cristancho-Rojas CN, KimuraSandoval Y, Rebollo-Hurtado V, Licano-Zubiate M, et al. Validation of Chest Computed Tomography Artificial Intelligence to Determine the Requirement for Mechanical Ventilation and Risk of Mortality in Hospitalized Coronavirus Disease-19 Patients in a Tertiary Care Center In Mexico City. Rev Investig Clínica. 2021;73(2):1–9. https://doi.org/10.24875/ric.20000451
(21) Khuriwal N, Mishra N. Breast Cancer Detection from Histopathological Images Using Deep Learning. 3rd Int Conf Work Recent Adv Innov Eng ICRAIE 2018. 2018;2018(November):1–4. http://dx.doi.org/10.1109/ICRAIE.2018. 8710426
(22) Divyashree B V., Kumar GH. Breast Cancer Mass Detection in Mammograms Using Gray Difference Weight and MSER Detector. SN Comput Sci [Internet]. 2021;2(2):1–13. https://doi.org/10.1007/s42979-021- 00452-8
(23) Shuja J, Alanazi E, Alasmary W, Alashaikh A. COVID-19 open source data sets: A comprehensive survey. Appl Intell. 2021;51(3):1296–325. https://dx.doi.org/10.1007%2Fs10489- 020-01862-6
(24) Niño Rondón CV, Castro Casadiego SA, Medina Delgado B, Guevara Ibarra D, Camargo Ariza LL. Comparativa entre la técnica de umbralización binaria y el método de Otsu para la detección de personas. Rev UIS Ing. 2021;20(2):65– 73. https://doi.org/10.18273/revuin.v20n2- 2021006
(25) Singhal P, Verma A, Garg A. A study in finding effectiveness of Gaussian blur filter over bilateral filter in natural scenes for graph based image segmentation. In: 2017 4th International Conference on Advanced Computing and Communication Systems, ICACCS 2017. 2017. p. 4–9. Available from: http://dx.doi.org/10.1109/ICACCS.2017. 8014612
(26) Ramadan ZM. Effect of kernel size on Wiener and Gaussian image filtering. Telkomnika (Telecommunication Comput Electron Control). 2019;17(3):1455–60. http://dx.doi.org/10.12928/telkomnika.v 17i3.11192
(27) Palma CA, Cappabianco FAM, Ide JS, Miranda PAV. Anisotropic diffusion filtering operation and limitations - Magnetic resonance imaging evaluation. IFAC Proc Vol. 2014;47(3):3887–92. https://doi.org/10.3182/20140824-6-ZA1003.02347
(28) Firoz R, Ali MS, Khan MNU, Hossain MK, Islam MK, Shahinuzzaman M. Medical Image Enhancement Using Morphological Transformation. J Data Anal Inf Process. 2016;4(1):1–12. http://dx.doi.org/10.4236/jdaip.2016.410 01
(29) Zhao F, Ma Y, Zhang J. Medical image processing based on mathematical morphology. In: Proceedings of the 2012 International Conference on Computer Application and System Modeling, ICCASM 2012. 2012. p. 0948–50. Available from: https://dx.doi.org/10.2991/iccasm.2012.2 41
(30) Xu H, Xu X, Zuo Y. Applying morphology to improve Canny operator’s image segmentation method. J Eng. 2019;2019(23):8816–9. http://dx.doi.org/10.1049/joe.2018.9113
(31) Ilhan HO, Serbes G, Aydin N. Automated sperm morphology analysis approach using a directional masking technique. Comput Biol Med [Internet]. 2020;122:103845. https://doi.org/10.1016/j.compbiomed.20 20.103845
(32) Gao X, Pan Z, Gao E, Fan G. Reversible data hiding for high dynamic range images using two-dimensional prediction-error histogram of the second time prediction. Signal Processing. 2020;173:107579. http://dx.doi.org/10.1016/j.sigpro.2020.1 07579
(33) Li W, Huang Q, Srivastava G. Contour Feature Extraction of Medical Image Based on Multi-Threshold Optimization. Mob Networks Appl. 2020;26(2):381–9. https://doi.org/10.1007/s11036-020- 01674-5
(34) Petrović N, Moyà-Alcover G, Varona J, Jaume-i-Capó A. Crowdsourcing human-based computation for medical image analysis: A systematic literature review. Health Informatics J. 2020;26(4):2446–69. https://doi.org/10.1177%2F1460458220 907435
(35) Hrgarek N. Certification and regulatory challenges in medical device software development. In: 2012 4th International Workshop on Software Engineering in Health Care, SEHC 2012 - Proceedings. 2012. p. 40–3. Available from: https://dl.acm.org/doi/10.5555/2667036. 2667043
Accepted 2021-10-02
Published 2022-05-26
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