JSIP  Vol.5 No.4 , November 2014
Edge Detection with a Preprocessing Approach
Abstract: Edge detection is the process of determining where boundaries of objects fall within an image. So far, several standard operators-based methods have been widely used for edge detection. However, due to inherent quality of images, these methods prove ineffective if they are applied without any preprocessing. In this paper, an image preprocessing approach has been adopted in order to get certain parameters that are useful to perform better edge detection with the standard operators-based edge detection methods. The proposed preprocessing approach involves computation of the histogram, finding out the total number of peaks and suppressing irrelevant peaks. From the intensity values corresponding to relevant peaks, threshold values are obtained. From these threshold values, optimal multilevel thresholds are calculated using the Otsu method, then multilevel image segmentation is carried out. Finally, a standard edge detection method can be applied to the resultant segmented image. Simulation results are presented to show that our preprocessed approach when used with a standard edge detection method enhances its performance. It has been also shown that applying wavelet edge detection method to the segmented images, generated through our preprocessing approach, yields the superior performance among other standard edge detection methods.
Cite this paper: Abo-Zahhad, M. , Gharieb, R. , Ahmed, S. and Donkol, A. (2014) Edge Detection with a Preprocessing Approach. Journal of Signal and Information Processing, 5, 123-134. doi: 10.4236/jsip.2014.54015.

[1]   Mukesh, K. and Rohini, S. (2013) Algorithm and Technique on Various Edge Detection: A Survey. International Journal (SIPIJ), 4, 65-75.

[2]   Canny, J. (1986) A Computational Approach to Edge Detection. Transactions on Pattern Analysis and Machine Intellgence, 3, 679-697.

[3]   Rufeil, E., Gimenez, J. and Flesia, G. (2012) Comparison of Edge Detection Algorithms on the Undecimated Wavelet Transform. CLAM 2012, Latin American Congress in Mathematics, Argentina.

[4]   Marr, D. and Hildreth, E. (1980) Theory of Edge Detection. Proceedings of the Royal Society of London. Series B, Biological Sciences, 207, 187-217.

[5]   Urkowitz, M. (1969) A Nonlinear Edge-Detection Technique. Proceedings of IEEE, 814-815.

[6]   Mallat, S. (1998) Wavelet Tour of Signal Processing. Academic Press.

[7]   Xu, Y., Weaver, J.B., Healy, D.M. and Lu, J. (1994) Wavelet Transform Domain Filters: A Spatially Selective Noise Filtration Technique. IEEE Transaction on Image Processing, 3, 747-758.

[8]   Brian, M. and Swami, A. (1999) Analysis of Multiscale Products for Step Detection and Estimation. IEEE Transaction on Information Theory, 45, 1043-1051.

[9]   Bao, P. and Zhang, L. (2003) Noise Reduction for Magnetic Resonance Images via Adaptive Multiscale Products Thresholding. IEEE Transaction on Medical Imaging, 22, 1089-1099.

[10]   Rani, V. and Sharma, D. (2012) A Study of Edge-Detection Methods. International Journal of Science, Engineering and Technology Research (IJSETR), 1, 62-65.

[11]   Gonzalez, R.C. and Woods, R.E. (2002) Digital Image Processing. 2nd Edition, Prentice Hall, Upper Saddle River.

[12]   Saluja, S., Singh, A.K. and Agrawal, S. (2013) A Study of Edge-Detection Methods. International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), 2, 994-999.

[13]   Raman, M. and Aggarwal, H. (2009) Study and Comparison of Various Image Edge Detection Techniques. International Journal of Image Processing (IJIP), 3, 1-12.

[14]   Bella, B., Tawfik, S. and EL-Gazzar, M.M. (2013) Multiscale Edge Detection Using Wavelet Transform Compared to Other Methodologies. International Journal of Mathematics and Computer Applications Research (IJMCAR), 3, 103-114.

[15]   Seema, B.K. (2013) An Edge Detection Approach Based on Wavelets. International Journal of Engineering Research & Technology (IJERT), 2, 2836-2840.

[16]   Otsu, N. (1979) A Threshold Selection Method from Gray Level Histograms. IEEE Transactions on Systems Man Cybernet, SMC-9, 62-66.

[17]   Nain, N., Jindal, G. and Jain, A. (2008) Dynamic Thresholding Based Edge Detection. Proceedings of the World Congress on Engineering, London, 2-4 July 2008.

[18]   Martin, D., Fowlkes, C. and Tal, D. (2001) A Database of Human Segmented Natural Images and Its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics. ICCV Vancouver, 1-8.

[19]   Khaire, P.A. and Thakur, N.V. (2012) A Fuzzy Set Approach for Edge Detection. International Journal of Image Processing (IJIP), 6, 403-412.