Detection of Masses in Mammogram Based on Non-Linear Filtering Techniques
B. Sridhar1, K. V. V. S. Reddy2, and A. M. Prasad3
1.Department of ECE, Lendi Institute of Engineering & Technology, Vizianagaram, India
2.Department of ECE, Andhra University, Visakhapatnam, India
3.Department of ECE, JNT University, Kakinada, India
2.Department of ECE, Andhra University, Visakhapatnam, India
3.Department of ECE, JNT University, Kakinada, India
Abstract-The present method proposed for cancer detection in mammograms. To extract various intensity regions is a difficult task in mammograms. It has low intensity values and noise impose at low frequencies. A nonlinear filter is more suitable to extract the types of noise and enhance sharpness of edges. The characteristic shows the adaptive bilateral filter is a nonlinear filter. Now a day’s Adaptive bilateral filters (ABF) is showing Optimum results to remove the noises and enhance the a edges of objects in the image sharply. A gradient operator with adaptive controlled parameter can efficiently highlight and segmented the edges. The proposed method is a combination of an ABF and morphological gradient operator is applied for masses detection. The method is experimented on MIAS database mammograms and ground truth is calculated. The method is validated with performance metrics.
Index Terms—non-linear filters, adaptive bilateral filters, morphological gradient, cancer detection, mammograms
Cite: B. Sridhar, K. V. V. S. Reddy, and A. M. Prasad, "Detection of Masses in Mammogram Based on Non-Linear Filtering Techniques," Journal of Medical and Bioengineering, Vol. 4, No. 6, pp. 430-435, December 2015. Doi: 10.12720/jomb.4.6.430-435
Index Terms—non-linear filters, adaptive bilateral filters, morphological gradient, cancer detection, mammograms
Cite: B. Sridhar, K. V. V. S. Reddy, and A. M. Prasad, "Detection of Masses in Mammogram Based on Non-Linear Filtering Techniques," Journal of Medical and Bioengineering, Vol. 4, No. 6, pp. 430-435, December 2015. Doi: 10.12720/jomb.4.6.430-435
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