Enhancement of Digital Mammograms Using Intuitionistic Fuzzy Entropy Function
Keywords:
Contrast limited adaptive histogram equalization, Gabor Information set, Hanman classifier, Image Enhancement, intuitionistic Fuzzy Entropy, Mammograms, type-II fuzzy setAbstract
A novel intuitionistic fuzzy entropy-based algorithm is developed for increasing the contrast of digital mammograms. The contrast enhancement helps in the early detection of masses and microcalcification in tissues thus paving the way for the auxiliary diagnosis of breast cancer. The current techniques for the enhancement of digital mammograms do not consider the uncertainty in pixel intensities of mammograms. The proposed technique aims at enhancing the mammograms using intuitionistic fuzzy entropy. To enhance contrast, Contrast Limited Adaptive Histogram Equalization (CLAHE) is implemented on small regions. The quantitative measures such as Discrete Entropy, Absolute Mean Brightness Error, Absolute Mean Brightness Coefficient, and Contrast Improvement Index and qualitative measure like visual quality are shown to be better than those of the state-of-the-art techniques. An application of the Hanman classifier on the enhanced images gives better classification accuracy than that of classifiers like support vector machine and convolutional neural network.
References
Simon S. (2019 Jan 8). Facts & figures 2019: US cancer death rate has dropped 27% in 25 years. [Online]. American Cancer Society. https://www.cancer.org/research/acs-research-news/facts-and-figures-2019.html
Hong WK, Holland JF, Frei E. Holland-Frei cancer medicine. People's Medical Pub. House 8th ed. / editors. 2010.
Philpotts LE. Can computer-aided detection be detrimental to mammographic interpretation? Radiology. 2009 Oct; 253(1): 17–22.
Anand S, Gayathri S. Mammogram image enhancement by two-stage adaptive histogram equalization. Optik. 2015 Nov 1; 126(21): 3150–2.
Jaya VL, Gopikakumari R. Fuzzy rule based enhancement in the SMRT domain for low contrast images. Procedia Comput Sci. 2015 Jan 1; 46: 1747–53.
Jenifer S, Parasuraman S, Kadirvelu A. Contrast enhancement and brightness preserving of digital mammograms using fuzzy clipped contrast-limited adaptive histogram equalization algorithm. Appl Soft Comput. 2016 May 1; 42: 167–77.
Pawar M, Talbar S. Local entropy maximization based image fusion for contrast enhancement of mammogram. J King Saud Univ-Comput Inf Sci. 2021 Feb 1; 33(2): 150–60.
Haußecker H, Tizhoosh HR. Fuzzy image processing. In Computer Vision and Applications. 2000 Jan 1; 541–576. Academic Press.
Chaira T. Intuitionistic fuzzy segmentation of medical images. IEEE Trans Biomed Eng. 2010 Feb 17; 57(6): 1430–6.
Montes I, Pal NR, Janiš V, Montes S. Divergence measures for intuitionistic fuzzy sets. IEEE Trans Fuzzy Syst. 2014 Apr 4; 23(2): 444–56.
Lin KP. A novel evolutionary kernel intuitionistic fuzzy $ c $-means clustering algorithm. IEEE Trans Fuzzy Syst. 2013 Aug 29; 22(5): 1074–87.
Mushrif MM, Ray AK. A-IFS histon based multithresholding algorithm for color image segmentation. IEEE Signal Process Lett. 2009 Feb 6; 16(3): 168–71.
Ananthi VP, Balasubramaniam P, Lim CP. Segmentation of gray scale image based on intuitionistic fuzzy sets constructed from several membership functions. Pattern Recognit. 2014 Dec 1; 47(12): 3870–80.
Szmidt E, Kacprzyk J. Entropy for intuitionistic fuzzy sets. Fuzzy Sets Syst. 2001 Mar 16; 118(3): 467–77.
Deng H, Deng W, Sun X, Liu M, Ye C, Zhou X. Mammogram enhancement using intuitionistic fuzzy sets. IEEE Trans Biomed Eng. 2016 Nov 2; 64(8): 1803–14.
Dabass J, Arora S, Vig R, Hanmandlu M. Mammogram image enhancement using entropy and CLAHE based intuitionistic fuzzy method. In 2019 IEEE 6th international conference on signal processing and integrated networks (SPIN). 2019 Mar 7; 24–29.
Chaira T. Intuitionistic fuzzy approach for enhancement of low contrast mammogram images. Int J Imaging Syst Technol. 2020 Dec; 30(4): 1162–72.
Kr Ghosh S, Biswas B, Ghosh A. Development of intuitionistic fuzzy special embedded convolutional neural network for mammography enhancement. Comput Intell. 2021 Feb; 37(1): 47–69.
Chandrika RR, Karthikeyan N, Karthik S. Simplified contrast enhancement fuzzy technique in digital mammograms for detecting suspicious cells. J Med Imaging Health Inform. 2017 Apr 1; 7(2): 316–22.
Christopher D, Simon P. A novel approach for mammogram enhancement using nonlinear unsharp masking and L0 gradient minimization. Procedia Comput Sci. 2020 Jan 1; 171: 1848–57.
Chaira T. Medical image processing: Advanced fuzzy set theoretic techniques. CRC Press; 2015 Jan 28.
Atanassov KT, Stoeva S. Intuitionistic fuzzy sets. Fuzzy Sets Syst. 1986 Aug 1; 20(1): 87–96.
Pal SK, King R. Image enhancement using smoothing with fuzzy sets. IEEE Trans Syst Man Cybern. 1981 Jul 7; 11(7): 494–500.
Pal SK, King RA. Image enhancement using fuzzy set. Electronic Letters. May 1980; 16(10): 376378. Online Available from http://library.isical.ac.in:8080/jspui/bitstream/10263/
/1/Image%20enhancement%20using%20fuzzy%20set-EL-16-10-1980-p%20376-378.pdf.
Vlachos IK, Sergiadis GD. Hesitancy histogram equalization. In 2007 IEEE International Fuzzy Systems Conference. 2007 Jul 23; 1–6.
Vlachos IK, Sergiadis GD. Intuitionistic fuzzy image processing. In: Soft Computing in Image Processing: Recent Advances. 2007; 383–414. Berlin, Heidelberg: Springer.
Ban AI. Nearest interval approximation of an intuitionistic fuzzy number. In Computational Intelligence, Theory and Applications: International Conference 9th Fuzzy Days in Dortmund, Germany, Sep 18–20, 2006 Proceedings. 2006; 229–240. Berlin, Heidelberg: Springer.
Hanmandlu M, Jha D. An optimal fuzzy system for color image enhancement. IEEE Trans Image Process. 2006 Sep 18; 15(10): 2956–66.
Suckling J, et al. The mammographic images analysis society digital mammogram database. In Exerpta Medica. International Congress Series 1069. 1994; 375–378.
Chaira T. An improved medical image enhancement scheme using Type II fuzzy set. Appl Soft Comput. 2014 Dec 1; 25: 293–308.
Bora DJ, Thakur RS. An efficient technique for medical image enhancement based on interval type-2 fuzzy set logic. InProgress in Computing, Analytics and Networking: Proceedings of ICCAN 2017. 2018; 667–678. Springer Singapore.
Sayeed F, Hanmandlu M. Properties of information sets and information processing with an application to face recognition. Knowl Inf Syst. 2017 Aug; 52(2): 485–507.
Sayeed F, Hanmandlu M. Three information set-based feature types for the recognition of faces. Signal Image Video Process. 2016 Feb; 10(2): 327–34.
Pezeshki H, Rastgarpour M, Sharifi A, Yazdani S. Extraction of spiculated parts of mammogram tumors to improve accuracy of classification. Multimed Tools Appl. 2019 Jul 30; 78(14): 19979–20003.
Li B, Ge Y, Zhao Y, Guan E, Yan W. Benign and malignant mammographic image classification based on convolutional neural networks. In Proceedings of the 2018 10th International Conference on Machine Learning and Computing. 2018 Feb 26; 247–251.
Rabidas R, Chakraborty J, Midya A. Analysis of 2D singularities for mammographic mass classification. IET Comput Vis. 2017 Feb; 11(1): 22–32.
