Document Type : Research Article


Al-Farahidi University, Baghdad, Iraq.


Simple signs existent in mammograms for diagnosing breast cancer are considered to be microcalcifications or MCs. Therefore, true detection of MCs is needed to minimize schedule diagnosis, efficient care and death rate due to breast cancer. A challenging task is to evaluate and interpret mammograms and, moreover to the poor contrast consistency of MCs relative to the remainder of the tissue, the precise identification of MCs, such as the minor size and random shape and size of the MC clusters, has several obstacles. These restrictions in the manual analysis of MCs increase the demand for an automated recognition system to help radiologists in mammogram analysis and it is important to design strength algorithm for this purpose. The goal of this paper is to present an efficient procedure that can be used to enhance images for extracting features to give excellent classification. The classifier senses which the region was normal, benign or malignant. The performance of KNN classifier with fuzzy histogram equalization using Otsu’s multi-threshold segmentation gives excellent results in detection and recognition in mammograms for breast cancer distinguished in image mammograms obtained from the hospital.


[1] Yi-Jhe Huang, Ding-Yuan Chan, Da-Chuan Cheng, Yung-Jen Ho, Po-Pang Tsai, Wu-Chung Shen, and Rui-Fen Chen. Automated feature set selection and its application to mcc identification in digital mammograms for breast cancer detection. Sensors, 13(4):4855–4875, 2013.
[2] Massimo De Santo, Mario Molinara, Francesco Tortorella, and Mario Vento. Automatic classification of clustered microcalcifications by a multiple expert system. Pattern Recognition, 36(7): 1467–1477, 2003.
[3] L Bocchi, G Coppini, J Nori, and G Valli. Detection of single and clustered microcalcifications in mammograms using fractals models and neural networks. Medical engineering & physics, 26(4): 303–312, 2004.
[4] Meltem G¨uls¨un, Figen Ba¸saran Demirkazık, and Macit Arıy¨urek. Evaluation of breast microcalcifications according to breast imaging reporting and data system criteria and le gal’s classification. European journal of radiology, 47(3):227– 231, 2003.
[5] Elizabeth Lazarus, Martha B Mainiero, Barbara Schepps, Susan L Koelliker, and Linda S Livingston. Bi-rads lexicon for us and mammography: interobserver variability and positive predictive value. Radiology, 239(2):385–391, 2006. [6] Rafayah Mousa, Qutaishat Munib, and Abdallah Moussa. Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural. Expert systems with Applications, 28(4):713–723, 2005.
[7] Edward A Sickles. Breast calcifications: mammographic evaluation. Radiology, 160(2):289–293, 1986.
[8] Maria Rizzi, Matteo D’Aloia, and Beniamino Castagnolo. A fully automatic system for detection of breast microcalcification clusters. J. Med. Biol. Eng, 30(3):181–188, 2010.
[9] Marilyn J Morton, Dana H Whaley, Kathleen R Brandt, and Kimberly K Amrami. Screening mammograms: interpretation with computeraided detectionprospective evaluation. Radiology, 239(2):375–383, 2006.
[10] Sang Kyu Yang, Woo Kyung Moon, Nariya Cho, Jeong Seon Park, Joo Hee Cha, Sun Mi Kim, Seung Ja Kim, and Jung-Gi Im. Screening mammography–detected cancers: sensitivity of a computer-aided detection system applied to full-field digital mammograms. Radiology, 244 (1):104–111, 2007.
[11] S-M Lai, Xiaobo Li, and WF Biscof. On techniques for detecting circumscribed masses in mammograms. IEEE Transactions on Medical Imaging, 8(4):377–386, 1989.
[12] Robin N Strickland and Hee Il Hahn. Wavelet transforms for detecting microcalcifications in mammograms. IEEE Transactions on Medical Imaging, 15(2):218–229, 1996.
[13] Arun D Kulkarni. Computer vision and fuzzyneural systems. Prentice Hall PTR, 2001.
[14] Yuan-Hsiang Chang, Bin Zheng, and David Gur. Computer-aided detection of clustered microcalcifications on digitized mammograms: a robustness experiment. Academic radiology, 4(6):415– 418, 1997.
[15] Wei Zhang, Kunio Doi, Maryellen L Giger, Robert M Nishikawa, and Robert A Schmidt. An improved shift-invariant artificial neural network for computerized detection of clustered microcalcifications in digital mammograms. Medical Physics, 23(4):595–601, 1996.
[16] Heng-Da Cheng, Yui Man Lui, and Rita I Freimanis. A novel approach to microcalcification detection using fuzzy logic technique. IEEE transactions on medical imaging, 17(3):442–450, 1998.
[17] Khamis A Zidan and Shereen S Jumaa. An efficient enhancement method for finger vein images using double histogram equalization. 2020.
[18] Jiu-lun Fan and Feng Zhao. Two-dimensional otsu’s curve thresholding segmentation method for gray-level images. Acta Electronica Sinica, 35 (4):751, 2007.
[19] Shereen S Jumaa and Khamis Zidan. Finger vein recognition using two parallel enhancement ppproachs based fuzzy histogram equalization. Periodicals of Engineering and Natural Sciences, 7(1):514–529, 2019.
[20] Siba M Sharef, Firas Abdulrazzaq Rahem, and SS Jouma’a. Implementation of fuzzy logic techniques in detecting edges for noisy images. In The Second Engineering Conference of Control, Computers and Mechatronics Engineering (ECCCM2), pages 154–162, 2014.
[21] V Magudeeswaran and CG Ravichandran. Fuzzy logic-based histogram equalization for image contrast enhancement. Mathematical problems in engineering, 2013, 2013.
[22] M Dakovic, S Ivanovi´c, and S Mijovic. Mammograms restoration by using wiener filter. In AIP Conference Proceedings, volume 1722, page 300005. AIP Publishing LLC, 2016.
[23] P Mayo, F Rodenas, and G Verdu. Comparing methods to denoise mammographic images. In The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, volume 1, pages 247–250. IEEE, 2004.
[24] Rangaraj M Rangayyan, Fabio J Ayres, and JE Leo Desautels. A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs. Journal of the Franklin Institute, 344(3-4):312–348, 2007.
[25] Heng-Da Cheng, Xiaopeng Cai, Xiaowei Chen, Liming Hu, and Xueling Lou. Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern recognition, 36(12):2967–2991, 2003.
[26] Jean Serra. Image analysis and mathematical morphol-ogy. 1982.
[27] Ghada Saad, Ahmad Khadour, and Qosai Kanafani. Ann and adaboost application for automatic detection of microcalcifications in breast cancer. The Egyptian Journal of Radiology and Nuclear Medicine, 47(4):1803–1814, 2016.