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Novel fuzzy logic expert system-based edge detection for X-ray images

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Abstract

Edge detection is an essential computer vision phenomenon that is useful for contour detection and in turn it is useful for acquiring vital information. There are a variety of images, including medical, satellite, industrial and general-purpose images. An X-ray is a form of medical imaging that is created by transmitting electronic radiation into a patient's body to get an image of the human body's internal structures. This allows orthopedic surgeons and radiologists to make more accurate diagnoses of illness. Due to its versatility, soft computing approaches are regarded as effective edge detection tools. This study provides a novel fuzzy logic-based technique for edge detection in which the quality of edges is governed by a sharpening-guided filter. The proposed model holds four different patterns for extracting the edges from an image namely \(2\times 2\) mask, \(3\times 2\) mask, \(2\times 3\) mask and \(3\times 3\) mask. These patterns of masks target the edge pixels of an image along with the other pixels. The proposed model provides an enhanced approach for detecting edges in X-ray pictures of humans. Various statistical measurements are used to evaluate the proposed model to prove its significance. By adjusting the smoothing parameters appropriately, it has been discovered that the detected edges are significantly enhanced. The proposed model is implemented and evaluated under four different metrics namely Correlation Coefficient, Figure of Merit, Structure Similarity Image Metrics and Euclidian Distance. The proposed model results are compared five different state-of-the-art algorithms to prove the significance, and the result shows that the proposed model identifies the edge in a well-structured manner.

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Data in this research paper will be shared upon request to the corresponding author.

References

  • Angelov P, Sadeghi-Tehran P, Ramezani R (2011) An approach to automatic real-time novelty detection, object identification, and tracking in video streams based on recursive density estimation and evolving Takagi-Sugeno fuzzy systems. Int J Intell Syst 26(3):189–205

    Article  MATH  Google Scholar 

  • Angelov P, Zhou X (2008) On line learning fuzzy rule-based system structure from data streams. In: 2008 IEEE international conference on fuzzy systems (IEEE world congress on computational intelligence), pp 915–922. IEEE.

  • Baltierra S, Valdebenito J, Mora M (2022) A proposal of edge detection in images with multiplicative noise using the Ant Colony System algorithm. Eng Appl Artif Intell 110:104715

    Article  Google Scholar 

  • Bharodiya AK, Gonsai AM (2018) Research review on feature extraction methods of human being’s X-ray image analysis’. Natl J Syst Inf Technol 11(1):9–22

    Google Scholar 

  • Bharodiya AK, Gonsai AM (2019) An improved edge detection algorithm for X-Ray images based on the statistical range. Heliyon 5(10):e02743

    Article  Google Scholar 

  • Biswas S, Hazra R (2018) Robust edge detection based on modified Moore-Neighbor’. Opt Int J Light Electron Opt 168:931–943

    Article  Google Scholar 

  • Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 6:679–698

    Article  Google Scholar 

  • Chakraborty S, Mali K (2022) SUFEMO: a superpixel based fuzzy image segmentation method for COVID-19 radiological image elucidation. Appl Soft Comput 129:109625

    Article  Google Scholar 

  • Cosgrove C, Yuille AL (2019) Adversarial examples for edge detection: they exist, and they transfer. http://arxiv.org/abs/ 1906.00335

  • Elmi S, Elmi Z (2022) A robust edge detection technique based on Matching Pursuit algorithm for natural and medical images. Biomed Eng Adv 4:100052

    Article  Google Scholar 

  • Farbod M, Akbarizadeh G, Kosarian A, Rangzan K (2018) Optimized fuzzy cellular automata for synthetic aperture radar image edge detection. J Electron Imaging 27(1):013030

    Article  Google Scholar 

  • Gevers T, Gijsenij A, Van de Weijer J, Geusebroek JM (2012) Color in computer vision: fundamentals and applications. Wiley, New York

    Book  Google Scholar 

  • Gonzalez CI, Melin P, Castro JR, Castillo O, Mendoza O (2016) Optimization of interval type-2 fuzzy systems for image edge detection. Appl Soft Comput 47:631–643

    Article  Google Scholar 

  • Goswami JC, Chan AK (2011) Fundamentals of wavelets: theory, algorithms, and applications. Wiley, New York

    Book  MATH  Google Scholar 

  • Ineneji C, Kusaf M (2019) Hybrid weapon detection algorithm, using material test and fuzzy logic system. Comput Electr Eng 78:437–448

    Article  Google Scholar 

  • Jeong C, Yang HS, Moon K (2019) A novel approach for detecting the horizon using a convolutional neural network and multi-scale edge detection. Multidimens Syst Signal Process 30(3):1187–1204

    Article  MATH  Google Scholar 

  • Liu Y, Lew MS (2016) Learning relaxed deep supervision for better edge detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 231–240

  • Marmanis D, Schindler K, Wegner JD, Galliani S, Datcu M, Stilla U (2018) Classification with an edge: improving semantic image segmentation with boundary detection. ISPRS J Photogramm Remote Sens 135:158–172

    Article  Google Scholar 

  • Mathur S, Ahlawat A (2008) Application of fuzzy logic in image edge detection. In: International conference “intelligent information and engineering systems

  • Menga Y, Zhang Z, Yin H et al (2018) Automatic detection of particle size distribution by image analysis based on local adaptive canny edge detection and modified circular Hough transform. Micron 106:34–41

    Article  Google Scholar 

  • Park M, Kang B, Jin SJ et al (2009) Computer aided diagnosis system of medical images using incremental learning method. Expert Syst Appl 36:7242–7251

    Article  Google Scholar 

  • Raheja S, Kumar A (2021) Edge detection based on type-1 fuzzy logic and guided smoothening. Evol Syst 12(2):447–462

    Article  Google Scholar 

  • Rajeswari M, Thirugnanasambandam K, Raghav RS, Prabu U, Saravanan D, Anguraj DK (2021) Flower pollination algorithm with Powell’s method for the minimum energy broadcast problem in wireless sensor network. Wirel Personal Commun 119:1111–1135

    Article  Google Scholar 

  • Rangarajan S (2005) Algorithms for edge detection. Stony Brook University. http://www.ee.sunysb.edu/~cvl/ese558/s2005/Reports/Srikanth%20Rangarajan/submission.doc. Accessed 10 Apr 2019

  • Ren H, Zhao S, Gruska J (2018) Edge detection based on single-pixel imaging. Opt Express 26(5):5501–5511

    Article  Google Scholar 

  • Rogowska J (2000) Overview and fundamentals of medical image segmentation. In: Handbook of medical imaging, processing and analysis. Academic Press, Inc., Orlando, pp 69–85

  • Sargano AB, Angelov P, Habib Z (2017) A comprehensive review on handcrafted and learning-based action representation approaches for human activity recognition. Appl Sci 7(1):110

    Article  Google Scholar 

  • Setayesh M, Zhang M, Johnston M (2013) A novel particle swarm optimisation approach to detecting continuous, thin and smooth edges in noisy images. Inf Sci 246:28–51

    Article  MathSciNet  MATH  Google Scholar 

  • Shih FY (2010) Image processing and pattern recognition: fundamentals and techniques. Wiley, New York

    Book  Google Scholar 

  • Snyder WE, Qi H (2017) Fundamentals of computer vision. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  • Soltani A et al (2018) A new expert system based on fuzzy logic and image processing algorithms for early glaucoma diagnosis. Biomed Signal Process Control 40:366–377

    Article  Google Scholar 

  • Thirugnanasambandam K, Raghav RS (2019) Experimental analysis of ant system on travelling salesman problem dataset TSPLIB. EAI Endorsed Trans Pervasive Health Technol 5(19):163092

    Article  Google Scholar 

  • Thirugnanasambandam K, Sudha SV, Saravanan D, Ravi RV, Anguraj DK, Raghav RS (2021b) Reinforced Cuckoo Search based fugitive landfill methane emission estimation. Environ Technol Innov 21:101207

    Article  Google Scholar 

  • Thirugnanasambandam K, Rajeswari M, Bhattacharyya D, Kim J-Y (2022) Directed Artificial Bee Colony algorithm with revamped search strategy to solve global numerical optimization problems. Autom Softw Eng 29:1–31

    Article  Google Scholar 

  • Thirugnanasambandam K, Anitha R, Enireddy V, Raghav RS, Anguraj DK, Arivunambi A (2021a) Pattern mining technique derived ant colony optimization for document information retrieval. J Ambient Intell Humaniz Comput, 1-13

  • Yang C, Wang W, Feng X (2022) Joint image restoration and edge detection in cooperative game formulation. Signal Process 191:108363

    Article  Google Scholar 

  • Zhang W, Zhao Y, Breckon TP, Chen L (2017) Noise robust image edge detection based upon the automatic anisotropic Gaussian kernels. Pattern Recognit 63:193–205

    Article  Google Scholar 

  • Zhang K, Zhang Y, Wang P et al (2018) An improved sobel edge algorithm and FPGA implementation. Procedia Comput Sci 131:243–248

    Article  Google Scholar 

  • Zhou X, Angelov P (2007) Autonomous visual self-localization in completely unknown environment using evolving fuzzy rule-based classifier. In: 2007 IEEE symposium on computational intelligence in security and defense applications, pp 131–138. IEEE

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Funding

The authors declare that they have no known competing financial interests.

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Authors and Affiliations

  1. Centre for Smart Grid Technologies, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai Campus, Chennai, India

    Kalaipriyan Thirugnanasambandam

  2. Department of Computer Science and Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India

    U. Prabu

  3. School of Computing, SASTRA Deemed To Be University, Thanjavur, Tamil Nadu, 613 401, India

    Dindayal Mahto, P. R. Rajendiran, R. Venkatesan & R. S. Raghav

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  1. Kalaipriyan Thirugnanasambandam
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  2. U. Prabu
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  3. Dindayal Mahto
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  4. P. R. Rajendiran
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  5. R. Venkatesan
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  6. R. S. Raghav
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Correspondence to R. S. Raghav.

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Thirugnanasambandam, K., Prabu, U., Mahto, D. et al. Novel fuzzy logic expert system-based edge detection for X-ray images. Soft Comput 27, 10975–10997 (2023). https://doi.org/10.1007/s00500-023-08616-9

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