Abstract
In breast cancer imaging, a poor quality nature image, especially one with low contrast, may provide insufficient data for visual interpretation of cancerous regions. Breast cancer survival rates are increasing as detection and analytical methods improve. On the other hand, breast cancer remains the most invasive disease that affects women. To improve the visual aspect of medical images, a combination methodology termed Genetic algorithm based histogram equalization is suggested. Histogram equalization is a quick and easy approach to boost visual contrast. The Genetic algorithm is best suited for multiple constraint optimization problems with an objective function that is subject to a variety of tough and easy restrictions. In this work, a Genetic algorithm with histogram equalization based image enrichment technique is suggested as the data mining method for separating information guidelines of breast cancer analysis forecast. Experiments were carried out on an extensive range of medical images for assessing the suggested method's performance both qualitatively and numerically. When related to leading edge enhancement processes, the projected method achieves improved performance in terms of entropy, structural similarity index, contrast improvement index, peak signal to noise ratio, mean square error and computational difficulty. The recommended procedure progresses contrast while conserving brightness and visual excellence. The suggested technique affords better quality for disease inspection and analysis.
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Ahmad, F., Mat Isa, N., Hussain, Z., & Sulaiman, S. (2012). A genetic algorithm-based multi-objective optimization of an artificial neural network classifier for breast cancer diagnosis. Neural Computing and Applications, 23, 1427–1435. https://doi.org/10.1007/s00521-012-1092-1
Anter Ahmed, Abu Elsoud, Mohamed, Hassanien, Aboul Ella (2014) Automatic mammographic parenchyma classification according to BIRADS Dictionary. https://doi.org/10.4018/978-1-4666-6030-4.ch002.
Aličković, E., & Subasi, A. (2015). Breast cancer diagnosis using GA feature selection and rotation forest. Neural Computing and Applications, 28, 753–763. https://doi.org/10.1007/s00521-015-2103-9
Alirezaei, M., Niaki, S., & Niaki, S. (2019). A bi-objective hybrid optimization algorithm to reduce noise and data dimension in diabetes diagnosis using support vector machines. Expert Systems with Applications, 127, 47–57. https://doi.org/10.1016/j.eswa.2019.02.037
Anter, A. M., & Hassenian, A. E. (2018). Computational intelligence optimization approach based on particle swarm optimizer and neutrosophic set for abdominal CT liver tumor segmentation. Journal of Computational Science, 25, 376–387. https://doi.org/10.1016/j.jocs.2018.01.003
Anter, A. M., & Hassenian, A. E. (2019). CT liver tumor segmentation hybrid approach using neutrosophic sets, fast fuzzy c-means and adaptive watershed algorithm. Artificial Intelligence in Medicine, 97, 105–117. https://doi.org/10.1016/j.artmed.2018.11.007
Anter, A., Hassenian, A., & Oliva, D. (2019). An improved fast fuzzy c-means using crow search optimization algorithm for crop identification in agricultural. Expert Systems with Applications, 118, 340–354. https://doi.org/10.1016/j.eswa.2018.10.009
Anter, A. M., Bhattacharyya, S., & Zhang, Z. (2020). Multi-stage fuzzy swarm intelligence for automatic hepatic lesion segmentation from CT scans. Applied Soft Computing, 96, 106677. https://doi.org/10.1016/j.asoc.2020.106677
Bahador, M., Keshtkar, M., & ZARIEE A,. (2018). Numerical and experimental investigation on the breast cancer tumour parameters by inverse heat transfer method using genetic algorithm and image processing. Sādhanā. https://doi.org/10.1007/s12046-018-0900-4
Bahadure, N., Ray, A., & Thethi, H. (2017). Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM. International Journal of Biomedical Imaging, 2017, 1–12. https://doi.org/10.1155/2017/9749108
Belciug, S., & Gorunescu, F. (2012). A hybrid neural network/genetic algorithm applied to breast cancer detection and recurrence. Expert Systems, 30, 243–254.
Brasil, Ministerio da Saude, Instituto Nacional do Cancer (INCA), Estimativa (2010) Incidência de câncer no Brasil, http://www.inca.gov.br/estimativa/2010/estimativa2010 1201.pdf.
Chan, H.-P., Lo, S.-C.B., Sahiner, B., Lam, K. L., & Helvie, M. A. (1995). Computer-aided detection of mammographic microcalcifications: Pattern recognition with an artificial neural network. Medical Physics, 22, 1555–1567.
Chen, S.-D., & Ramli, A. R. (2003). Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation. IEEE Transactions on Consumer Electronics, 49(4), 1301–1309. https://doi.org/10.1109/TCE.2003.1261233
Cheng, H., Cai, X., Chen, X., et al. (2003). Computer-aided detection and classification of microcalcifications in mammograms: A survey. Pattern Recognition, 36, 2967–2991. https://doi.org/10.1016/s0031-3203(03)00192-4
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6, 182–197. https://doi.org/10.1109/4235.996017
ElSoud, M., & Anter, A. (2012). Automatic mammogram segmentation and computer aided diagnoses for breast tissue density according to BIRADS dictionary. International Journal of Computer Aided Engineering and Technology, 4, 165. https://doi.org/10.1504/ijcaet.2012.045655
Gaber, Tarek, Ismail Sayed, Gehad Anter, Ahmed Soliman, Mona Ali, Mona Semary, Noura Hassanien, Aboul Ella, Snasel, Vaclav (2015). Thermogram breast cancer detection approach based on Neutrosophic sets and fuzzy c-means algorithm. Conference proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. https://doi.org/10.1109/EMBC.2015.7319334.
Goldberg, D. (1989). Genetic algorithms in search and optimization (1st ed.). Addison-Wesley.
Gonzalez, R. C., & Woods, R. E. (2002). Digital image processing (2nd ed.). Prentice Hall.
Gulsrud, T.O., Kjode, S., (1996) Optimal filter for detection of stellate lesions and circumscribed masses in mammograms. In SPIE Visual Communications and Image Processing Orlando, Florida, March 17–20, 1: 430–440
Hamdani, T., Won, J., Alimi, A., & Karray, F. (2011). Hierarchical genetic algorithm with new evaluation function and bi-coded representation for the selection of features considering their confidence rate. Applied Soft Computing, 11, 2501–2509. https://doi.org/10.1016/j.asoc.2010.08.020
Heath, M., et al. (1998). Current status of the digital database for screening mammography. In Karssemeijer, N., Thijssen, M., Hendriks, J., van Erning, L. (Eds.) Digital Mammography. Computational Imaging and Vision, vol 13. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-5318-8_75.
Henriksen, E., Carlsen, J., Vejborg, I., et al. (2018). The efficacy of using computer-aided detection (CAD) for detection of breast cancer in mammography screening: A systematic review. Acta Radiologica, 60, 13–18. https://doi.org/10.1177/0284185118770917
Ittannavar, S., & Havaldar, R. (2021). Detection of breast cancer using the infinite feature selection with genetic algorithm and deep neural network. Distributed and Parallel Databases. https://doi.org/10.1007/s10619-021-07355-w
Jain, R., & Mazumdar, J. (2003). A genetic algorithm based nearest neighbor classification to breast cancer diagnosis. Australasian Physics & Engineering Sciences in Medicine, 26, 6–11. https://doi.org/10.1007/bf03178690
Kim, Y. T. (1997). Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Transactions on Consumer Electronics, 43(1), 1–8. https://doi.org/10.1109/30.580378
Li, K., Deb, K., Zhang, Q., & Kwong, S. (2015). An evolutionary many-objective optimization algorithm based on dominance and decomposition. IEEE Transactions on Evolutionary Computation, 19, 694–716. https://doi.org/10.1109/tevc.2014.2373386
Maleki, N., Zeinali, Y., & Niaki, S. (2021). A k-NN method for lung cancer prognosis with the use of a genetic algorithm for feature selection. Expert Systems with Applications, 164, 113981. https://doi.org/10.1016/j.eswa.2020.113981
Nie, D., Wang, L., Adeli, E., et al. (2019). 3-D fully convolutional networks for multimodal isointense infant brain image segmentation. IEEE Transactions on Cybernetics, 49, 1123–1136. https://doi.org/10.1109/tcyb.2018.2797905
Orcajo-Rincon, J., Muñoz-Langa, J., Sepúlveda-Sánchez, J. M., Fernández-Pérez, G. C., Martínez, M., Noriega-Álvarez, E., Sanz-Viedma, S., Vilanova, J. C., & Luna, A. (2022). Review of imaging techniques for evaluating morphological and functional responses to the treatment of bone metastases in prostate and breast cancer. Clinical and Translational Oncology, 24(7), 1290–1310. https://doi.org/10.1007/s12094-022-02784-0. Epub 2022 Feb 13. PMID: 35152355; PMCID: PMC9192443.
Pereira, D., Ramos, R., & do Nascimento M,. (2014). Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm. Computer Methods and Programs in Biomedicine, 114, 88–101. https://doi.org/10.1016/j.cmpb.2014.01.014
PouryaHoseini, M. G., & Shayesteh. (2013). Efficient contrast enhancement of images using hybrid ant colony optimisation, genetic algorithm, and simulated annealing. Digital Signal Processing., 23, 879–893.
Rodríguez-Ruiz, A., Krupinski, E., Mordang, J., et al. (2019). Detection of breast cancer with mammography: Effect of an artificial intelligence support system. Radiology, 290, 305–314. https://doi.org/10.1148/radiol.2018181371
Rudolph, G. (1994). Convergence analysis of canonical genetic algorithms. IEEE Transactions on Neural Networks, 5, 96–101. https://doi.org/10.1109/72.265964
Rundo, L., Tangherloni, A., Nobile, M., et al. (2019). MedGA: A novel evolutionary method for image enhancement in medical imaging systems. Expert Systems with Applications, 119, 387–399. https://doi.org/10.1016/j.eswa.2018.11.013
Sathya, P., & Kayalvizhi, R. (2011). Modified bacterial foraging algorithm based multilevel thresholding for image segmentation. Engineering Applications of Artificial Intelligence, 24, 595–615. https://doi.org/10.1016/j.engappai.2010.12.001
Sharapov, R., & Lapshin, A. (2006). Convergence of genetic algorithms. Pattern Recognition and Image Analysis, 16, 392–397. https://doi.org/10.1134/s1054661806030084
Sun, C.-C., Ruan, S.-J., Shie, M.-C., & Pai, T.-W. (2005). Dynamic contrast enhancement based on histogram specification. IEEE Transactions on Consumer Electronics, 51(4), 1300–1305. https://doi.org/10.1109/TCE.2005.1561859
Thangavel, K., Karnan, M., Sivakumar, R., Kajamohideen, A., (2008). Automatic detection of microcalcification in mammograms: A review. In: Graphics Vision and Image Processing.
Thomas, G., Tapia, F., Daniel, P., & Stephen. (2011). Histogram specification: A fast and flexible method to process digital images. IEEE Transactions on Instrumentation and Measurement, 60, 1565–1578. https://doi.org/10.1109/TIM.2010.2089110
Tseng, M., & Liao, H. (2009). The genetic algorithm for breast tumor diagnosis—The case of DNA viruses. Applied Soft Computing, 9, 703–710. https://doi.org/10.1016/j.asoc.2008.09.013
Upadhyay, J., & Jaiswal, A. (2015). A joint implementation of adaptive histogram equalization and interpolation. Optik, 126, 5936–5940. https://doi.org/10.1016/j.ijleo.2015.08.150
Wang, Yu., Chen, Q., & Zhang, B. (1999). Image enhancement based on equal area dualistic sub-image histogram equalization method. IEEE Transactions on Consumer Electronics, 45(1), 68–75. https://doi.org/10.1109/30.754419
Whitley, D. (1994). A genetic algorithm tutorial. Statistics and Computing, 4(2), 65–85.
Zhang, C., & Nie, H. (2019). An adaptive enhancement method for breast X-ray images based on the nonsubsampled contourlet transform domain and whale optimization algorithm. Medical & Biological Engineering & Computing, 57, 2245–2263. https://doi.org/10.1007/s11517-019-02022-w
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Samraj, D., Ramasamy, K. & Krishnasamy, B. Enhancement and diagnosis of breast cancer in mammography images using histogram equalization and genetic algorithm. Multidim Syst Sign Process 34, 681–702 (2023). https://doi.org/10.1007/s11045-023-00880-0
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DOI: https://doi.org/10.1007/s11045-023-00880-0