Edge Detection in Image Processing using Sobel Operator
Keywords:
Convolution, Edge detection, First-order derivative, Kernel intensity, Noise sensitivity, Sobel operatorAbstract
Biomedical applications of nanoparticles are rapidly expanding, supporting diagnostics, therapeutics, biosensing, implant surface improvements, and advanced medical textiles. Medical imaging benefits from image enhancement, segmentation, pattern recognition, and computer-aided diagnosis (CAD) systems. Edge detection plays a key role in identifying structures in medical images. A CAD system is developed to classify abnormal cervical cells using morphological and statistical edge-based segmentation, achieving 98% accuracy. Because image processing on large datasets is computationally intensive, parallel and multithreaded Sobel edge detection algorithms help reduce processing time. Quantum image processing further accelerates edge detection by using quantum superposition and parallelism with improved Sobel operators. Several improved Sobel-based methods enhance edge clarity, reduce noise, and extract detailed contours. Fractional order derivative (FOD)-based Sobel performs better than Prewitt and Laplacian in PSNR, SSIM, and FSIM metrics for fish images. Ant Colony Optimization (ACO)-based edge detection accelerates convergence using modified pheromone updates. Detection, medical diagnosis, SEM image analysis, and contrast enhancement tasks also employ edge detection and morphological operations. The system is implemented in a 512×512 image that takes 0.009ms in the processing system. For a 3×3-pixel block, the design uses 22 logic gates with a minimum delay of 1.5 fs. It occupies an area of 111 nm² and operates at a supply voltage of 1.05V. The circuit’s average power consumption is measured at 2.27 μWat.
References
Y. Saxena, N. Mishra, M. Sameer, and P. Dahiya, “Improved edge detection approach to tackle edge thickness and better edge connectivity,” in Proc. 2nd Int. Conf. on Intelligent Technologies (CONIT), Hubli, India, 2022, pp. 1–5.
P. Meenakshi, K. Bhavana, and A. K. Nair, “Pneumonia detection using X-ray image analysis with image processing techniques,” in Proc. 7th Int. Conf. on Communication and Electronics Systems (ICCES), Coimbatore, India, 2022, pp. 1657–1662.
E. V. H. S. David and Febriana, “Modified local updates of the ant colony optimization algorithm for image edge detection,” in Proc. 10th Int. Conf. on Cyber and IT Service Management (CITSM), Yogyakarta, Indonesia, 2022, pp. 1–6.
Charu, P. Kumar, and K. Singh, “Hardware model for efficient edge detection in images,” in Proc. IEEE Int. Conf. on Computing, Power and Communication Technologies (GUCON), Greater Noida, India, 2020, pp. 798–802.
R. Joshi, M. A. Zaman, and S. Katkoori, “Novel bit-sliced near-memory computing-based VLSI architecture for fast Sobel edge detection in IoT edge devices,” in Proc. IEEE Int. Symp. on Smart Electronic Systems (iSES), Chennai, India, 2020, pp. 291–296.
S. Zade, R. Korde, R. Sonone, and M. Shah, “Performance analysis of parallel image processing operations,” in Proc. Int. Conf. on Communication and Signal Processing (ICCSP), Chennai, India, 2020, pp. 597–601.
P. Srujana, J. Priyanka, V. Y. S. S. S. Patnaikuni, and N. Vejendla, “Edge detection with different parameters in digital image processing using GUI,” in Proc. 5th Int. Conf. on Computing Methodologies and Communication (ICCMC), Erode, India, 2021, pp. 795–802.
J. Deka and S. Laskar, “Comparative analysis of FOD-based Prewitt, Sobel, and Laplacian operators for edge detection on freshwater fish images,” in Proc. Int. Conf. on Emerging Smart Computing and Informatics (ESCI), Pune, India, 2020, pp. 65–70.
G. E. Rani, R. Murugeswari, and N. Rajini, “Edge detection in scanning electron microscope (SEM) images using various algorithms,” in Proc. 4th Int. Conf. on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2020, pp. 401–405.
G. A. G. and R. Ajai A. S., “VLSI implementation of improved Sobel edge detection algorithm,” in Proc. Int. Conf. on Communication, Control and Information Sciences (ICCISc), Idukki, India, 2021, pp. 1–6.
T. Wu, L. Wang, and J. Zhu, “Image edge detection based on Sobel with morphology,” in Proc. IEEE 5th Information Technology, Networking, Electronic and Automation Control Conf. (ITNEC), Xi’an, China, 2021, pp. 1216–1220.
A. Alammari, M. Alkaabi, M. Bubashait, and A. Alqaddumi, “Bitmap image processing and edge detection using Sobel algorithm and multi-threaded parallel techniques,” in Proc. Int. Conf. on Data Analytics for Business and Industry (ICDABI), Sakheer, Bahrain, 2021, pp. 493–499.
S. S. Kalyan, V. Pratyusha, N. Nishitha, and T. K. Ramesh, “Vehicle detection using image processing,” in Proc. IEEE Int. Conf. for Innovation in Technology (INOCON), Bengaluru, India, 2020, pp. 1–5.
A. Saad, K. Rafat, A. Soltan, and M. S. Darweesh, “Accelerated edge detection algorithm for high-speed applications,” in Proc. 9th Int. Japan–Africa Conf. on Electronics, Communications, and Computations (JAC-ECC), Alexandria, Egypt, 2021, pp. 5–8.
