The International Arab Journal of Information Technology (IAJIT)


Evolutionary Computing Model for Finding Breast Cancer Masses using Image Enhancement Procedures with Artificial Intelligent Algorithms

In this research, Particle Swarm Optimization (PSO) based image equalization is projected to enhance the contrast of different breast cancer images. Breast cancer is the highest and another important root of tumor disease in females worldwide. Mass and microcalcification clusters are a significant early signs of breast cancer. The mortality rate can effectively be decreased by early diagnosis and treatment. Most practical approach for the early detection and identification of breast cancer diseases is mammography. Mammographic images contaminated by noise usually involve image enhancement techniques to aid interpretation. Contrast enhancement is divided into two categories: development of direct contrast and enhancement of indirect contrast. Indirect contrast improvement is used in the image histogram update. Histogram Equalization (HE) is the modest enhancement of the indirect contrast approach usually used for contrast enhancement. The proposed method's average entropy is 5.3251 with the highest structural similarity index 0.99725. The best contrast improvement of this method is 1.0404 and Peak Signal to Noise Ratio (PSNR) is 46.3803. The MSE value is 2157.08. This paper recommends an innovative method of enhancing digital mammogram image contrast based on different HE approaches. The performance of the projected method has been related to other prevailing techniques using the parameters, namely, discrete entropy, contrast improvement index, structural similarity index measure, mean square error, and peak signal-to-noise ratio. Investigational findings indicate that the projected strategy is efficient and robust and shows better results than others.

[1] Al-Najdawi N., Biltawi M., and Tedmori S., “Mammogram Image Visual Enhancement, Mass Segmentation and Classification,” Applied Soft Computing Journal, vol. 35, pp. 175-185, 2015.

[2] Baran A., Kurrant D., Zakaria A., Fear E., and LoVetri J., “Breast Cancer Imaging Using Microwave Tomography with Radar-derived Prior Information,” in Proceedings of the IEEE USNC- URSI Radio Science Meeting, Memphis, pp. 259- 259, 2014. DOI: 10.1109/USNC- URSI.2014.6955642

[3] Beheshti Z., Shamsuddin S., Beheshti E., and Yuhaniz S., “Enhancement of Artificial Neural Network Learning Using Centripetal Accelerated Particle Swarm Optimization for Medical Diseases Diagnosis,” Soft Computing, vol. 18, pp. 2253-2270, 2014. 013-1198-0

[4] Cai X., Li X., Razmjooy N., and Ghadimi N., “Breast Cancer Diagnosis by Convolutional Neural Network and Advanced Thermal Exchange Optimization Algorithm,” Computational and Mathematical Methods in Medicine, vol. 2021, pp. 1-13, 2021.

[5] Chaira T., “Intuitionistic Fuzzy Approach for Enhancement of Low Contrast Mammogram Images,” International Journal of Imaging Systems and Technology, vol. 30, no. 4, pp. 1162- 1172, 2020. DOI:10.1002/ima.22437

[6] Do Nascimento M., Martins A., Neves L., Ramos R., Flores E., and Carrijo G., “Classification of Masses in Mammographic Image Using Wavelet Domain Features and Polynomial Classifier,” Expert Systems with Applications, vol. 40, no. 15, pp. 6213-6221, 2013.

[7] Dheeba J., Albert Singh N., and Tamil Selvi S., “Computer-aided Detection of Breast Cancer on Mammograms: A Swarm Intelligence Optimized Wavelet Neural Network Approach,” Journal of Biomedical Informatics, vol. 49, pp. 45-52, 2014.

[8] Garg G. and Juneja M., “Particle Swarm Optimization Based Segmentation of Cancer in Multi-parametric Prostate MRI,” Multimedia Tools and Applications, vol. 80, no. 20, pp. 30557- 30580, 2021. DOI:10.1007/s11042-021-11133-2

[9] Giri P. and Saravanakumar K., “Breast Cancer Detection Using Image Processing Techniques,” Oriental Journal of Computer Science and 642 The International Arab Journal of Information Technology, Vol. 20, No. 4, July 2023 Technology, vol. 10, no. 2, pp. 391-399, 2017. DOI :

[10] Gonzalez R. and Woods R., Digital Image Processing, Pearson Prentice Hall, 2002.

[11] Gorai A. and Ghosh A., “Gray-level Image Enhancement by Particle Swarm Optimization,” in Proceedings of the IEEE World Congress on Nature and Biologically Inspired Computing, Coimbatore, pp. 72-77, 2009. DOI: 10.1109/NABIC.2009.5393603

[12] Gordon R. and Rangayyan R., “Feature Enhancement of Film Mammograms Using Fixed and Adaptive Neighborhoods,” Applied Optics, vol. 23, no. 4, pp. 560-564, 1984. 13- 2055

[13] Guo G. and Razmjooy N., “A New Interval Differential Equation for Edge Detection and Determining Breast Cancer Regions in Mammography Images,” Systems Science and Control Engineering, vol. 7, no. 1, pp. 346-356, 2019. DOI:10.1080/21642583.2019.1681033

[14] Guzman-Cabera R., Guzmán-Cabrera R., Guzmán-Sepúlveda R., Torres-Cisneros M., May-Arrioja D., and Ruiz-Pinales J., “Digital Image Processing Technique for Breast Cancer Detection,” International Journal of Thermophysics, vol. 34, pp. 1519-1531, 2013.

[15] Heath M., Bowyer K., Kopans D., and Moore R., Digital Mammography, Computational Imaging and Vision, Springer-Science and Business Media, 1998. DOI: 5318-8.

[16] Instituto Nacional de Câncer-Inca-português (Brasil).Available es/media/document/livro-abc-4-edicao.pd, Last Visited, 2021.

[17] Janga P. and Sharma R., “An Adaptive Image Enhancement Technique by Combining Cuckoo Search and Particle Swarm Optimization Algorithm and RWT,” International Journal of Pure and Applied Mathematics, vol. 117, no. 9, pp. 101-105, 2017. doi: 10.12732/ijpam.v117i9.18

[18] Juhl J., Crummy A., and Kuhlman J., Essentials of Radiologic Imaging, Lippincott-Raven, 1998. DOI:10.1016/S0720-048X(99)00062-5

[19] Kim Y., “Contrast Enhancement Using Brightness Preserving Bi-Histogram Equalization,” IEEE Translactions on Consumer Electronics, vol. 43, no. 1, pp. 1-8, 1997. DOI: 10.1109/30.580378

[20] Lai S., Li X., and Bischof W., “On Techniques for Detecting Circumscribed Masses in Mammograms,” IEEE Transaction on Medical Imaging, vol. 8, no. 4, pp. 377-386, 1989. DOI: 10.1109/42.41491

[21] Liu Q., Liu Z., Yong S., Jia K., and Razmjooy N., “Computer-aided Breast Cancer Diagnosis Based on Image Segmentation and Interval Analysis,” Automatika, vol. 61, pp. 496-506, no. 3, 2020.

[22] Martens J. and Meesters L., “Image Dissimilarity,” Signal Processing, vol. 70, no. 3, pp. 155-176, 1998. 1684(98)00123-6

[23] Menon R., Raha P., Kothari S., Chakraborty S., Chakrabarti I., and Karim R., “Automated Detection and Classification of Mass from Breast Ultrasound Images,” in Proceedings of the 5th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, Patna, pp. 1-4, 2015. DOI: 10.1109/NCVPRIPG.2015.7490070

[24] Mohan S. and Mahesh T., “Particle Swarm Optimization Based Contrast Limited Enhancement for Mammogram Images,” in Proceedings of the 7th International Conference on Intelligent Systems and Control (ISCO), Coimbatore, pp. 384-388, 2013. DOI: 10.1109/ISCO.2013.6481185

[25] Nam S. and Choi J., “A Method of Image Enhancement and Fractal Dimension for Detection of Microcalcifications in Mammogram,” in Proceedings of the the 20th Annual International Conference of IEEE Engineering in Medicine and Biology Society, Hong Kong, pp. 1009-1012, 1998. DOI: 10.1109/IEMBS.1998.745620

[26] Pereira D., Ramos R., and Do Nascimento M., “Segmentation and Detection of Breast Cancer in Mammograms Combining Wavelet Analysis and Genetic Algorithm,” Computer Methods and Programs in Biomedicine, vol. 114, no. 1, pp. 88- 101, 2014.

[27] Razmjooy N., Estrela V., and Loschi H., “Entropy- Based Breast Cancer Detection in Digital Mammograms Using World Cup Optimization Algorithm,” IGI Global, vol. 11, no. 3, pp. 1-18, 2020. DOI: 10.4018/978-1- 6684-7136- 4.ch034

[28] Rizzi M., D'Aloia M., and Castagnolo B., “Computer Aided Detection of Microcalcifications in Digital Mammograms Adopting a Wavelet Decomposition,” Integrated Computer-Aided Engineering, vol. 16, no. 2, pp. 91-103, 2009. DOI:10.3233/ICA-2009-0306

[29] Selvarajan D., Jaber A., and Ahmad I., “Comparative Analysis of PSO and ACO Based Selection Techniques for Medical Data Preservation,” The International Arab Journal of Information Technology, vol. 16, no. 4, pp. 731- 736, 2019.,%20No.%204/ Evolutionary Computing Model for Finding Breast Cancer Masses using Image ... 643 11461.pdf

[30] Suradi S., Abdullah K., and Mat Isa N., “Improvement of Image Enhancement for Mammogram Images Using Fuzzy Anisotropic Diffusion Histogram Equalization Contrast Adaptive Limited (FADHECAL),” Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, vol. 10, no. 1, pp. 67-75, 2022. DOI:10.1080/21681163.2021.1972344

[31] Sathya P. and Kayalvizhi R., “Optimum Multilevel Image Thresholding Based on Tsallis Entropy Method with Bacterial Foraging Algorithm,” International Journal of Computer Science Issues, vol. 7, no. 5, pp. 336-343, 2010.

[32] Shivhare E. and Saxena V., “Breast Cancer Diagnosis from Mammographic Images Using Optimized Feature Selection and Neural Network Architecture,” International Journal of Imaging Systems and Technology, vol. 31, no. 1, pp. 253- 269, 2021.

[33] Singh K., Kapoor R., and Sinha S., “Enhancement of Low Exposure Images Via Recursive Histogram Equalization Algorithms,” Optik, vol. 126, no. 20, pp. 2619-2625, 2015.

[34] Singh N., Kaur L., and Singh K., “Histogram Equalization Techniques for Enhancement of Low Radiance Retinal Images for Early Detection of Diabetic Retinopathy,” Engineering Science and Technology, vol. 22, no.3, pp. 736-745, 2019.

[35] Sumathi R., Venkatesulu M., and Arjunan S., “Extracting Tumor in MR Brain and Breast Image with Kapur's Entropy Based Cuckoo Search Optimization and Morphological Reconstruction Filters,” Biocybernetics Biomedical Engineering, vol. 38, no. 4, pp. 918-930, 2018.

[36] Strickland R. and Hahn H., “Wavelet Transforms for Detecting Microcalcifications in Mammograms,” IEEE Transactions on Medical Imaging, vol. 15, no. 2, pp. 218-229, 1996. DOI: 10.1109/42.491423

[37] Veluchamy M. and Subramani B., “Image Contrast and Color Enhancement Using Adaptive Gamma Correction and Histogram Equalization,” Optik-International Journal for Light and Electron Optics, vol. 183, pp. 329-337, 2019.

[38]  Vijayalakshmi S., John A., Sunder R., Mohan S., Bhattacharya S., Kaluri R., Feng G., and Usman T., “Multi-Modal Prediction of Breast Cancer Using Particle Swarm Optimization with Non-Dominating Sorting,” International Journal of Distributed Sensor Networks, vol. 16, no. 11, pp. 1-12, 2020. DOI:10.1177/1550147720971505

[39]  Wong M., He X., and Yeh W., “Image Clustering Using Particle Swarm Optimization,” in Proceedings of the IEEE Congress of Evolutionary Computation, New Orleans, pp. 262-268, 2011. DOI: 10.1109/CEC.2011.5949627

[40]      Zhou X., Shen Q., and Wang J., “Research of Image Enhancement Based on Particle Swarm Optimization,” Microelectronics and Computer, vol. 25, no. 4, pp. 42-44, 2008.