Pectoral Muscle Removal in Digital Mammograms Using Region Based Standard Otsu Technique

Jacinta C. Anusionwu, Vincent C. Chijindu, Joy N. Eneh, ThankGod I. Ozue, Nnabuike Ezukwoke, Mamilus A. Ahaneku, Edward C. Anoliefo, Walter A. Ohagwu

Abstract


Mammography is usually the first preference of imaging diagnostic modalities used for detection of breast cancer in the early stage. Two projections Cranio Caudal (CC) and Medio-Lateral Oblique (MLO) which depict different degrees for visualizing the breast are used during digital mammogram acquisition and the MLO view shows more breast tissue and Pectoral Muscle (PM) area when compared to CC view. Although, the PM is a criterion used to show proper positioning, it can result in biased results of mammographic analysis like: cancer detection and breast tissue density estimation, because the PM area has similar or even higher intensity than breast tissue and breast lesions if present. This paper proposed a Region Based Standard Otsu thresholding method for the elimination of PM area present in MLO mammograms. The proposed algorithm was implemented using 322 digital mammograms from the Mammographic Image Analysis Society (MIAS) database, and the difference between the PM detected and the manually drawn PM region by an expert was evaluated. The results showed an average: Jaccard Similarity Index, False Positive Rate (FPR) and False Negative Rate (FNR) of 93.2%, 3.54% and 5.68% respectively and also an acceptable rate of 95.65%

Keywords


Digital Mammogram; Medio-Lateral Oblique; Pectoral Muscle; Standard Otsu thresholding; Jaccard Similarity Index; False Negative Rate; False Positive Rate

References


P. C. Pearlman et al., “The National Institutes of Health Affordable Cancer Technologies Program : Improving Access to Resource-Appropriate Technologies for Cancer Detection , Diagnosis , Monitoring , and Treatment in Low- and Middle-Income Countries,” IEEE J. Transl. Eng. Heal. Med., vol. 4, no. February, 2016.

World Health Organization, “Cancer - Key Facts.” [Online]. Available: https://www.who.int/en/news-room/fact-sheets/detail/cancer. [Accessed: 20-Oct-2020].

International Cancer Control Partnership, “Nigeria National Cancer Control Plan 2018 – 2022,” 2020. [Online]. Available: https://www.iccp-portal.org/system/files/plans/NCCP_Final %5B1%5D.pdf. [Accessed: 20-Aug-2020].

O. Erhabor et al., Breast Cancer in Nigeria: Diagnosis, Management and Challenges. UK: Author House, 2016.

American Cancer Society, “Breast Cancer Early Detection and Diagnosis.” [Online]. Available: https://www.cancer.org/cancer/breast-cancer.html. [Accessed: 20-Aug-2020].

R. A. Al-naggar, Principles and Practice of Cancer Prevention and Control. OMICS Group ebook, 2014.

A. Norouzi et al., “Medical Image Segmentation Methods, Algorithms, and Applications,” Inst. Electron. Telecommun. Eng. Tech. Rev., vol. 31, no. 3, pp. 199–213, 2014.

P. Chinmayi, L. Ailandeeewari, and M. Prabukumar, “Survey of Image Processing Techniques in Medical Image Analysis: Challenges and Methodologies,” in Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition, 2018, pp. 460–471.

S. J. Sushma and S. C. Prasanna Kumar, “Advancement in Research Techniques on Medical Imaging Processing for Breast Cancer Detection,” Int. J. Electr. Comput. Eng., vol. 6, no. 2, pp. 717–724, 2016.

M. Firmino, G. Angelo, H. Morais, M. R. Dantas, and R. Valentim, “Computer - Aided Detection ( CADe ) and Diagnosis ( CADx ) System for Lung Cancer with Likelihood of Malignancy,” BioMed Cent. J., vol. 15, no. 2, pp. 1–18, 2016.

M. A. Berbar, “Hybrid Methods for Feature Extraction for Breast Masses Classification,” Egypt. Informatics J., vol. 19, no. 1, pp. 63–73, 2018.

R. Guzman et al., “Digital Image Processing Technique for Breast Cancer Detection,” Int. J. Thermophys., pp. 1519–1531, 2013.

B. Halalli and A. Makandar, “Computer Aided Diagnosis - Medical Image Analysis Techniques,” in Intech open science, 2018, pp. 85–109.

F. Elzahra, A. Hateem, M. Mohammad, and M. Tarique, “Fourier Transform Based Early Detection of Breast Cancer by Mammogram Image Processing,” J. Biomed. Eng. Med. Imaging, vol. 2, no. 4, pp. 17–32, 2015.

H. Ture and T. Kayikcioglu, “Accurate Detection of Distorted Pectoral Muscle in Mammograms Using Specific Patterned Isocontours,” IEEE Access, vol. 8, 2020.

W. B. Yoon, J. E. Oh, E. Y. Chae, H. H. Kim, S. Y. Lee, and K. G. Kim, “Automatic Detection of Pectoral Muscle Region for Computer-Aided Diagnosis Using MIAS Mammograms,” vol. 2016, 2016.

S. Saxena, Y. Singh, and B. Agarwal, “Boundary Detection to Segment the Pectoral Muscle from Digital Mammograms Images,” Int. J. Eng. Adv. Technol., vol. 9, no. 3, pp. 1593–1603, 2020.

S. Gardezi, A. Faouzi, I. Faye, K. Nidal, and M. Eltoukhy, “Segmentation of Pectoral Muscle Using the Adaptive Gamma Corrections,” Multimed. Tools Appl., vol. 75, no. 24, 2017.

S. S. Fadhil, F. Abed, and A. Dawood, “Automatic Pectoral Muscles Detection and Removal in Mammogram Images,” Iraqi J. Sci., vol. 62, no. 2, pp. 676–688, 2021.

P. Vagssa, N. M. Doudou, T. Jolivo, O. Videme, and D. T. Kolyang, “Pectoral Muscle Deletion on a Mammogram to Aid in The Early Diagnosis of Breast Cancer,” Int. J. Eng. Sci. Technol., vol. 12, no. 3, pp. 57–65, 2020.

R. J. Ferrari, R. M. Rangayyan, J. E. L. Desautels, R. A. Borges, and A. F. Frère, “Automatic Identification of the Pectoral Muscle in Mammograms,” vol. 23, no. 2, pp. 232–245, 2004.

S. Sreedevi and E. Sherly, “A Novel Approach for Removal of Pectoral Muscles in Digital Mammogram,” Procedia - Procedia Comput. Sci., vol. 46, no. Icict 2014, pp. 1724–1731, 2015.

J. Suckling and et al, “The Mammographic Image Analysis Society Digital Mammogram Database,” International Congress Series 1069, 1994. [Online]. Available: peipa.essex.ac.uk/info/mias.html. [Accessed: 29-Jun-2019].

O. Marques, Practical Image and Video Processing using MATLAB. Hoboken, New Jersey: John Wiley and Sons, Inc, 2011.

A. M. Omer and M. Elfadil, “Preprocessing of Digital Mammogram Image Based on Otsu ’ s Threshold,” Am. Sci. Res. J. Eng. Technol. Sci., vol. 37, no. 1, pp. 220–229, 2017.

V. C. Chijindu, U. Chidiebele, M. Ahaneku, and E. C. Anoliefo, “DETECTION OF PROSTATE CANCER USING RADIAL / AXIAL SCANNING OF 2D Detection of Prostate Cancer Using Radial / Axial Scanning of 2D Trans-rectal Ultrasound Images,” Bull. Electr. Eng. Infomatics, vol. 7, no. 2, pp. 222–229, 2018.

A. Makandar, “Pre-processing of Mammography Image for Early Detection of Breast Cancer,” Int. J. Comput. Appl., vol. 144, no. 3, pp. 11–15, 2016.


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