--

8 (1) 2018

Edge detection based on augmented lagrangian method for lowquality medical images


Author - Affiliation:
Vo Thi Hong Tuyet - Ho Chi Minh City Open University , Vietnam
Corresponding author: Vo Thi Hong Tuyet - tuyet.vth@ou.edu.vn

Abstract
Medical images are useful for the treatment process. They contain a lot of information on displaying abnormalities in your body. The contour of medical images is a matter of interest. In there, edge detection is a process prepared for boundaries. Therefore, the edge detection of medical images is very important. Other previous methods must sacrifice time for the accurate results. It is because the medical images in the real world have many impurities. In this paper, I propose a method of detecting edges in medical images which have impurities by using augmented lagrangian method to improve the Canny algorithm. My algorithm improves the ability to detect edges faster. Compared with other recent methods, the proposed method is more efficient.

Keywords
augmented lagrangian method; canny; edge detection

Full Text:
PDF

References

Bhatt, A. D., & Warkhedkar, R. V. (2008). Reverse engineering of human body: A B-Spline based heterogeneous modeling approach. Computer-Aided Design and Applications, 5(1/4), 194-208.


Bhatt, A. D., & Warkhedkar, R. V. (2009). Material-solid modeling of human body: A heterogeneous B-Spline based approach. Computer-Aided Design, 41(8), 586-597.


Brigger, P., & Unser, M. (1998). Multi-scale B-spline snakes for general contour detection. Wavelet Applications in Signal and Image Processing VI, SPIE, 3458, 92-102.


Canny, J. (1986). A computational approach to edge detection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, PAMI, 8(6), 679-698.


Deriche, R. (1987). Using Canny's criteria to derive a recursively implemented optimal edge detector. International Journal of Computer Vision, 1, 167-187.


Easley, G., Labate, D., & Lim, W. Q. (2008). Sparse directional image representations using the discrete shearlet transform. Applied and Computational Harmonic Analysis, 25(1), 25-46.


Gonzalez, C. I., Castro, J. R., Melin, P., & Castillo, O. (2015). Cuckoo search algorithm for the optimization of type-2 fuzzy image edge detection systems. In IEEE Congress on Evolutionary Computation (CEC) (pp. 449-455). New York, NY: IEEE.


Marr, D., & Hildreth, E. (1980). Theory of edge detection. Proceedings of the Royal Society, 207, 187-217.


Srishti. (2014). Technique based on Cuckoo’s search algorithm for exudates detection in diabetic retinopathy. Ophthalmology research: An international journal, SCIENCEDOMAIN international, 2(1), 43-54.


Stanley, H., Khoshabeh, R., Kristofor, B. G., Philip, E. G., & Truong, Q. N. (2011). An augmented lagrangian method for total variation video restoration. IEEE Transactions on Image Processing, 20(11), 3097-3111.


Strang, G. (1989). Wavelets and dilation equations: A brief introduction. SIAM Review, 31(4), 614-627.


Vincent, O. R., & Folorunso, O. (2009). A descriptive algorithm for sobel image edge detection. Proceedings of Informing Science & IT Education Conference (InSITE) (pp. 98-107).


Yuping, W., & Yuanlong, C. (1995). Multiscale B-spline wavelet for edge detection. Science in China (Series A), 38(4), 499-512.


Zhang, L., & Bao, P. (2002). Edge detection by scale multiplication in wavelet domain. Pattern Recognition Letters, 23(14), 1771-1784.



Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.