Medical Image Edge Detection Based on Neuro-Fuzzy Approach
Edge detection is one of the most important tasks in image processing. Medical image edge detection plays an important role in segmentation and object recognition of the human organs. It refers to the process of identifying and locating sharp discontinuities in medical images. In this paper, a neuro-fuzzy based approach is introduced to detect the edges for noisy medical images. This approach uses desired number of neuro-fuzzy subdetectors with a postprocessor for detecting the edges of medical images. The internal parameters of the approach are optimized by training pattern using artificial images. The performance of the approach is evaluated on different medical images and compared with popular edge detection algorithm. From the experimental results, it is clear that this approach has better performance than those of other competing edge detection algorithms for noisy medical images.
[1] M.M. Subashini, S.K. Sahoo., “Pulse coupled neural networks and its applications”, International Journal of Expert Systems with Applications, Vol.41, pp.3965-3974, 2014.
[2] M.I. Rajab, M.S. Woolfson, and S.P. Morgan, “Application of region-based segmentation and neural network edge detection to skin lesions”, Computerized Medical Imaging and Graphics, Vol. 28, pp.61-68, 2004.
[3] Raman Maini and Himanshu Aggarwal, “Study and Comparison of Various Image Edge Detection Techniques”, International Journal of Image Processing (IJIP), Vol. 3, 20, pp.1-12, 2010.
[4] R. C. Gonzalez, R.E. Woods and S.L. Eddins, “Digital Image Processing Using MATLAB”, 2nd Edn., Mc Graw Hill, New Delhi, 2010.
[5] J. Canny, “A Computational Approach to Edge Detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 8, pp. 679-687, 1986.
[6] Jing Xiao-jun, Yu Nong, and Shang Yong, “Image Filtering Based on Mathematical Morphology and Visual Perception Principle”, Chinese Journal of Electronics, Vol. 13, pp. 612-616, 2004.
[7] Richard A P, “A New Algorithm for Image Noise Reduction Using Mathematical morphology”, IEEE Transaction on Image Processing, Vol. 4, pp. 554-568, 1995.
[8] J. Mehena, “Medical Image edge detection based on mathematical morphology”, International Journal of Computer and communication technology, Vol. 2, pp.45-48, 2011.
[9] Liang, L.R., Looney, C.G., “Competitive fuzzy edge detection”, Applied Soft Computing, Vol. 3, pp. 132-137, 2003.
[10] J. Mehena and M.C. Adhikary, “Medical Image edge detection based on soft computing approach”, International Journal of Innovative Research in computer and communication Engineering, Vol. 3, pp.6801-6807, 2015.
[11] Choi YS, Krishnapuram R., “A robust approach to image enhancement based on fuzzy logic”, IEEE Trans. Image Processing, Vol.6, pp.808-825, 1997.
[12] Abdallah A. Alshennawy, and Ayman A. Aly., “Edge Detection in Digital Images Using Fuzzy Logic Technique”, World Academy of Science, Engineering and Technology, pp.178-186,2009.
[13] Pushpajit A. Khaire and Nileshsingh V. Thakur, “A Fuzzy Set Approach for Edge Detection”, International Journal of Image Processing, Vol.6, pp.403-412, 2012.
[14] Russo, F., “Edge Detection in Noisy Images Using Fuzzy Reasoning”, IEEE Trans. On Inst. and Meas., Vol.47, pp.802-808, 1998.
[15] M. T. Hagan and M. B. Menhaj, “Training feed forward networks with the Marquardt algorithm,” IEEE Trans. Neural Network., Vol. 5, pp. 989-993,1994.
[16] Yuksel ME., “Edge detection in noisy images by neuro-fuzzy processing”, AEU International Journal of Communication, Vol. 61, pp.82-89, 2007.
[17] J. Vasavada and S. Tiwari., “An Edge detection method for grayscale images based on BP feedforward Neural network”, International Journal of Computer applications, Vol.67, pp.23-28, 2013.
[18] Hassanpour, H. and Asadi, S., “Image quality enhancement using pixel wise gamma correction”, International Journal of Engineering-Transactions B: Applications, Vol. 24, No. 4, pp. 301-311, 2011.
[19] J. Mehena and M. C. Adhikary, “Medical Image Segmentation and Detection of MR Images Based on Spatial Multiple- Kernel Fuzzy C-Means Algorithm”, International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering, World Academy of Science, Engineering and Technology, Vol. 9, Issue 6, pp. 508 - 512, June-2015.
[1] M.M. Subashini, S.K. Sahoo., “Pulse coupled neural networks and its applications”, International Journal of Expert Systems with Applications, Vol.41, pp.3965-3974, 2014.
[2] M.I. Rajab, M.S. Woolfson, and S.P. Morgan, “Application of region-based segmentation and neural network edge detection to skin lesions”, Computerized Medical Imaging and Graphics, Vol. 28, pp.61-68, 2004.
[3] Raman Maini and Himanshu Aggarwal, “Study and Comparison of Various Image Edge Detection Techniques”, International Journal of Image Processing (IJIP), Vol. 3, 20, pp.1-12, 2010.
[4] R. C. Gonzalez, R.E. Woods and S.L. Eddins, “Digital Image Processing Using MATLAB”, 2nd Edn., Mc Graw Hill, New Delhi, 2010.
[5] J. Canny, “A Computational Approach to Edge Detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 8, pp. 679-687, 1986.
[6] Jing Xiao-jun, Yu Nong, and Shang Yong, “Image Filtering Based on Mathematical Morphology and Visual Perception Principle”, Chinese Journal of Electronics, Vol. 13, pp. 612-616, 2004.
[7] Richard A P, “A New Algorithm for Image Noise Reduction Using Mathematical morphology”, IEEE Transaction on Image Processing, Vol. 4, pp. 554-568, 1995.
[8] J. Mehena, “Medical Image edge detection based on mathematical morphology”, International Journal of Computer and communication technology, Vol. 2, pp.45-48, 2011.
[9] Liang, L.R., Looney, C.G., “Competitive fuzzy edge detection”, Applied Soft Computing, Vol. 3, pp. 132-137, 2003.
[10] J. Mehena and M.C. Adhikary, “Medical Image edge detection based on soft computing approach”, International Journal of Innovative Research in computer and communication Engineering, Vol. 3, pp.6801-6807, 2015.
[11] Choi YS, Krishnapuram R., “A robust approach to image enhancement based on fuzzy logic”, IEEE Trans. Image Processing, Vol.6, pp.808-825, 1997.
[12] Abdallah A. Alshennawy, and Ayman A. Aly., “Edge Detection in Digital Images Using Fuzzy Logic Technique”, World Academy of Science, Engineering and Technology, pp.178-186,2009.
[13] Pushpajit A. Khaire and Nileshsingh V. Thakur, “A Fuzzy Set Approach for Edge Detection”, International Journal of Image Processing, Vol.6, pp.403-412, 2012.
[14] Russo, F., “Edge Detection in Noisy Images Using Fuzzy Reasoning”, IEEE Trans. On Inst. and Meas., Vol.47, pp.802-808, 1998.
[15] M. T. Hagan and M. B. Menhaj, “Training feed forward networks with the Marquardt algorithm,” IEEE Trans. Neural Network., Vol. 5, pp. 989-993,1994.
[16] Yuksel ME., “Edge detection in noisy images by neuro-fuzzy processing”, AEU International Journal of Communication, Vol. 61, pp.82-89, 2007.
[17] J. Vasavada and S. Tiwari., “An Edge detection method for grayscale images based on BP feedforward Neural network”, International Journal of Computer applications, Vol.67, pp.23-28, 2013.
[18] Hassanpour, H. and Asadi, S., “Image quality enhancement using pixel wise gamma correction”, International Journal of Engineering-Transactions B: Applications, Vol. 24, No. 4, pp. 301-311, 2011.
[19] J. Mehena and M. C. Adhikary, “Medical Image Segmentation and Detection of MR Images Based on Spatial Multiple- Kernel Fuzzy C-Means Algorithm”, International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering, World Academy of Science, Engineering and Technology, Vol. 9, Issue 6, pp. 508 - 512, June-2015.
@article{"International Journal of Information, Control and Computer Sciences:73000", author = "J. Mehena and M. C. Adhikary", title = "Medical Image Edge Detection Based on Neuro-Fuzzy Approach ", abstract = "Edge detection is one of the most important tasks in image processing. Medical image edge detection plays an important role in segmentation and object recognition of the human organs. It refers to the process of identifying and locating sharp discontinuities in medical images. In this paper, a neuro-fuzzy based approach is introduced to detect the edges for noisy medical images. This approach uses desired number of neuro-fuzzy subdetectors with a postprocessor for detecting the edges of medical images. The internal parameters of the approach are optimized by training pattern using artificial images. The performance of the approach is evaluated on different medical images and compared with popular edge detection algorithm. From the experimental results, it is clear that this approach has better performance than those of other competing edge detection algorithms for noisy medical images.", keywords = "Edge detection, neuro-fuzzy, image segmentation, artificial image, object recognition.", volume = "10", number = "1", pages = "229-4", }
