Mammography Images Segmentation Based on Fuzzy Set and Thresholding
DOI:
https://doi.org/10.23851/mjs.v29i3.644Keywords:
Mammography, Segmentation, Fuzzy logic, Mass detection, ThresholdingAbstract
Breast cancer is the most widespread cancer that influences ladies about the world. Early recognition of breast tumor is a standout amongst the hugest variables influencing the probability of recuperation from the illness. Hence, mammography remains the most precise and best device for distinguishing breast malignancy. This paper presents a method for segment the boundary of breast masses regions in mammograms via a proposed algorithm based on fuzzy set techniques. Firstly, it was used data set (mini-MIAS) for evaluate algorithm. it was preprocessing the data set to remove noise and propose a fuzzy set by using fuzzy inference system by generated two input parameters (employs image gradient), then used thresholding filter. Then it was evaluated this proposed method, qualitative and quantitative results were obtained to demonstrate the efficiency of this method and confirm the possibility of its use in improving the diagnosis.Downloads
References
Senthilkumar and G. Umamaheswari, "Breast cancer detection using combined curvelet based enhancement and a novel segmentation methods," BIOMEDICAL PAPERS-OLOMOUC, vol. 159, pp. 83-86, 2015.
A. Ferrero, S. Salicone, A. Mencattini, G. Rabottino, and M. Salmeri, "Uncertainty evaluation in a fuzzy classifier for microcalcifications in digital mammography," in Instrumentation and Measurement Technology Conference (I2MTC), 2010 IEEE, 2010, pp. 1250-1255.
M. A. Duarte, A. V. Alvarenga, C. M. Azevedo, M. J. G. Calas, A. F. Infantosi, and W. C. Pereira, "Evaluating geodesic active contours in microcalcifications segmentation on mammograms," Computer methods and programs in biomedicine, vol. 122, pp. 304-315, 2015.
M. Abdel-Nasser, A. Moreno, and D. Puig, "Towards cost reduction of breast cancer diagnosis using mammography texture analysis," Journal of Experimental & Theoretical Artificial Intelligence, vol. 28, pp. 385-402, 2016.
N. Saidin, U. K. Ngah, H. A. M. Sakim, D. N. Siong, M. K. Hoe, and I. L. Shuaib, "Density based breast segmentation for mammograms using graph cut and seed based region growing techniques," in Computer Research and Development, 2010 Second International Conference on, 2010, pp. 246-250.
N. Singh, A. G. Mohapatra, and G. Kanungo, "Breast cancer mass detection in mammograms using K-means and fuzzy C-means clustering," International Journal of Computer Applications (0975–8887), vol. 22, 2011.
A. Touil and K. Kalti, "Iterative fuzzy segmentation for an accurate delimitation of the breast region," Computer methods and programs in biomedicine, vol. 132, pp. 137-147, 2016.
C. J. Baines, D. V. Mcfarlane, and A. B. Miller, "The Role of the Reference Radiologist; Estimates of Inter-Observer Agreement and Potential Delay in Cancer Detection in the National Breast Screening Study," Investigative Radiology, vol. 25, pp. 971-976, 1990.
K. El Fahssi, A. Elmoufidi, A. Abenaou, S. Jai-Andaloussi, and A. Sekkaki, "Mass segmentation in mammograms based on the minimisation of energy and active contour model," in Medical Measurements and Applications (MeMeA), 2014 IEEE International Symposium on, 2014, pp. 1-5.
W. I. Suleiman, S. Lewis, D. Georgian-Smith, M. G. Evanoff, and M. F. McEntee, "Number of mammography cases read per year is a strong predictor of sensitivity," Journal of Medical Imaging, vol. 1, p. 015503, 2014.
M. Y. Kamil, "Morphological gradient in brain magnetic resonance imaging based on intuitionistic fuzzy approach," in Multidisciplinary in IT and Communication Science and Applications (AIC-MITCSA), Al-Sadeq International Conference on, 2016, pp. 1-3.
M.Y. Kamil and A. M. Salih, “Mammography Images Segmentation via Fuzzy C-mean and K-mean,” International Journal of Intelligent Engineering and Systems, Vol.12, No.1, p. 22-29, 2019.
B. O. Alijla, A. T. Khader, L. C. Peng, M. A. Al-Betar, and W. L. Pei, "Fuzzy Rough Set Approach for Selecting the Most Significant Texture Features in Mammogram Images," in Information and Communication Technology (PICICT), 2013 Palestinian International Conference on, 2013, pp. 51-56.
J. Kaur and M. Mahajan, "Hybrid of Fuzzy Logic and Random Walker Method for Medical Image Segmentation," International Journal of Image, Graphics and Signal Processing, vol. 7, p. 23, 2015.
V. Ananthi, P. Balasubramaniam, and P. Raveendran, "A thresholding method based on interval-valued intuitionistic fuzzy sets: an application to image segmentation," Pattern Analysis and Applications, pp. 1-13, 2017.
S. Arora and Q. Kaur, "Modified edge detection technique using fuzzy inference system," Computer Application, vol. 44, pp. 9-12, 2012.
A. D. Borkar and M. Atulkar, "Fuzzy Inference System for Image Processing," International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), vol. 2, pp. pp: 1007-1010, 2013.
A. D. Borkar and M. Atulkar, "Detection of edges using fuzzy inference system," International Journal of Innovative Research in Computer and Communication Engineering, vol. 1, 2013.
C. L. Chowdhary and D. Acharjya, "Segmentation of Mammograms Using a Novel Intuitionistic Possibilistic Fuzzy C-Mean Clustering Algorithm," in Nature Inspired Computing, ed: Springer, 2018, pp. 75-82.
P. Kaur, A. Soni, and A. Gosain, "RETRACTED: A robust kernelized intuitionistic fuzzy c-means clustering algorithm in segmentation of noisy medical images," ed: Elsevier, 2013.
A. M. Salih and M.Y. Kamil, “Mammography Image Segmentation Based on Fuzzy Morphological Operations,” 1st Annual International Conference on Information and Sciences (AiCIS'18), 2018.
L. A. Zadeh, "Fuzzy sets," Inform. control, vol. 8, pp. 338-365, 1965.
J. M. Mendel, "A comparison of three approaches for estimating (synthesizing) an interval type-2 fuzzy set model of a linguistic term for computing with words," Granular Computing, vol. 1, pp. 59-69, 2016.
D. Yu and S. Shi, "Researching the development of Atanassov intuitionistic fuzzy set: Using a citation network analysis," Applied Soft Computing, vol. 32, pp. 189-198, 2015.
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