Abstract
The detection of edges is the one of the important stage in the application, associated with the machine vision, computer vision and the image processing. It is most commonly and highly preferred in the area were the extraction or the detection of the attribute are necessary. As the manual methods of diagnosis in the medical images acquired from the CT (computed tomography) and the MRI (magnetic resonance images) are very tedious and as well as time consuming, the paper puts forth the methodology to detect the edges in the CT and the MRI by employing Gabor Transform as well as the soft and the hard clustering. This proposed method is highly preferred among the image with dynamic variations. The technique used in the paper is evaluated using 4500 instance of the MRI and 3000 instance of CT. The results on the basis of the figure of merit (FOM) and Misclassification rate (MCR) are compared with other standard approaches and the performance was evinced.
References
- Kanopoulos, Nick, Nagesh Vasanthavada, and Robert L. Baker. "Design of an image edge detection filter using the Sobel operator." IEEE Journal of solid-state circuits 23, no. 2 (1988): 358-367.
- Gupta, Samta, and Susmita Ghosh Mazumdar. "Sobel edge detection algorithm." International journal of computer science and management Research 2, no. 2 (2013): 1578-1583.
- Wang, Xin. "Laplacian operator-based edge detectors." IEEE transactions on pattern analysis and machine intelligence 29, no. 5 (2007): 886-890.
- Shrivakshan, G. T., and Chandramouli Chandrasekar. "A comparison of various edge detection techniques used in image processing." International Journal of Computer Science Issues (IJCSI) 9, no. 5 (2012): 269.
- Manjunath, B. S. "Gabor wavelet transform and application to problems in early vision." In ASILOMAR CONFERENCE ON SIGNALS SYSTEMS AND COMPUTERS, pp. 796-796. COMPUTER SOCIETY PRESS, 1992.
- Jian, Muwei, and Lei Liu. "Texture image classification using visual perceptual texture features and gabor wavelet." Journal of computers 4, no. 8 (2009): 763.
- Zhitao, Xiao, Guo Chengming, Yu Ming, and Li Qiang. "Research on log Gabor wavelet and its application in image edge detection." In 6th International Conference on Signal Processing, 2002., vol. 1, pp. 592-595. IEEE, 2002.
- Raj, J. S. (2019). QOS Optimization of Energy Efficient Routing In Iot Wireless Sensor Networks. Journal of ISMAC, 1(01), 12-23.
- Joseph, S. I. T. (2019). Survey of Data Mining Algorithm’s For Intelligent Computing System. Journal of trends in Computer Science and Smart technology (TCSST), 1(01), 14-24.
- Raj, J. S., & Ananthi, J. V. (2019). Recurrent Neural Networks and Nonlinear Prediction in Support Vector Machines. Journal of Soft Computing Paradigm (JSCP), 1(01), 33-40.
- Manoharan, S. (2019). Study On Hermitian Graph Wavelets in Feature Detection. Journal of Soft Computing Paradigm (JSCP), 1(01), 24-32.
- Sathesh, A. (2019). Performance Analysis of Granular Computing Model in Soft Computing Paradigm For Monitoring Of Fetal Echocardiography. Journal of Soft Computing Paradigm (JSCP), 1(01), 14-23
- Bezdek, James C., Robert Ehrlich, and William Full. "FCM: The fuzzy c-means clustering algorithm." Computers & Geosciences 10, no. 2-3 (1984): 191-203.
- Alsabti, Khaled, Sanjay Ranka, and Vineet Singh. "An efficient k-means clustering algorithm." (1997).